# pos

id: cord-295116-eo887olu
pos: ADD
token:
  http://www.icmje.org/recommendations/browse/roles-andresponsibilities/defining-the-role-of-authors-and-contributors.html#two

lemma:
  http://www.icmje.org/recommendations/browse/roles-andresponsibilities/defining-the-role-of-authors-and-contributors.html#two


# ent

id: cord-283291-lj3k53px
type: CHEMICAL
entity:
  des avantages de l inoculation pour la prevenir Daniel Bernoulli s epidemiological model revisited A


id: cord-254195-k7e8g0ni
type: ORG
entity:
  A New Fractional Modelling on Susceptible Infected Recovered Equations with Constant Vaccination Rate Fractional Differential Equations


id: cord-254195-k7e8g0ni
type: ORG
entity:
  Differential Equations An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations Numerical Approximations of Stochastic Differential Equations


id: cord-268630-vu8yyisx
type: PERSON
entity:
  Mutaz Mohammad Conceptualization Methodology Visualization Investigation Supervision Validation Writing


id: cord-268630-vu8yyisx
type: CHEMICAL
entity:
  L 2 R Fig 4 shows the graphs of the smoothed refinable functions and its corresponding Riesz wavelets In this paper


id: cord-259846-oxbmtend
type: ORG
entity:
  Fractional Calculus Theory and Applications Differentiation and Integration to Arbitrary Order Fractional Integrals Derivatives Theory


id: cord-322862-dcb237an
type: PROTEIN
entity:
  N fX t 2 Dg 1 P r N fX t 2 Dg Therefore P r S fX t 2 Dg 1 2 1 n exp 2n 1 2n 2 p t a 2n 4n 3 2 2n 1 n 2 n 1 1 n exp 2n 1 2n 2 p t a 2n 2n 3 2 2n 1 n 2 n 1


id: cord-322862-dcb237an
type: GGP
entity:
  P r S fX t 2 Dg 1 2 1 n exp 2n 1 2n 2 p t a 2n 4n 3 2 2n 1 n 2 n 1 1 n exp 2n 1 2n 2 p t a 2n 2n 3 2 2n 1 n 2 n 1


id: cord-322862-dcb237an
type: ORG
entity:
  Computational Biology Di erential Geometry of Curves and Surfaces Maximum Principle for Parabolic Inequalities


id: cord-308069-iydjrmhh
type: DNA
entity:
  coronavirus disease covid 19 rrt pcr or ct Estimating epidemic exponential growth rate and basic reproduction number Country wise forecast model


id: cord-330703-fbmy6osu
type: GGP
entity:
  S 1 t n 1 S n 1 I n 1 R n 1 D n 1 P n 1 S 1 t n 2 S n 2 I n 2 R n 2 D n 2 P n 2 S 1 t n S n I n R n D n P n 2 S 1 t n 1 S n 1 I n 1 R n 1 D n 1 P n 1 7 14 I n 1 I 0 t Θ Γ Θ 1 n j 2 I 1 t n 2 S n 2 I n 2 R n 2 D n 2 P n 2 n j 1 Θ n j Θ t Θ Γ Θ 2 n j 2 I 1 t n 1 S n 1 I n 1 R n 1 D n 1 P n 1 I 1 t n 2 S n 2 I n 2 R n 2 D n 2 P n 2 I 1 t n S n I n R n D n P n 2 E 1 t n 1 S n 1 I n 1 R n 1 D n 1 P n 1 t


id: cord-330703-fbmy6osu
type: GGP
entity:
  S 1 t n S t n I t n R t n D t n P t n S 1 t n 1 S t n 1 I t n 1 R t n 1 D t n 1 P t n 1 S 1 t n 1 S t n 1 I t n 1 R t n 1 D t n 1 P t n 1 t 5 12 S 1 t n 2 S t n 2 I t n 2 R t n 2 D t n 2 P t n 2 t 23 12 S 1 t n S t n I t n R t n D t n P t n t 7 33 I 1 t n 1 S t n 1 I t n 1 R t n 1 D t n 1 P t n 1 D t n 1 P t n 1 t 5 12 I 1 t n 2 S t n 2 I t n 2 R t n 2 D t n 2 P t n 2 t R 1 t n 1 S t n 1 I t n 1 R t n 1 D t n 1 P t n 1 R 1 t n 1 S t n 1 I t n 1 R t n 1 D t n 1 P t n 1 t 5 12 R 1 t n 2 S t n 2 I t n 2 R t n 2 D t n 2 P t n 2 t


id: cord-330703-fbmy6osu
type: GGP
entity:
  S 1 t n 1 S n 1 I n 1 R n 1 D n 1 P n 1 S 1 t n 2 S n 2 I n 2 R n 2 D n 2 P n 2 S 1 t n S n I n R n D n P n 2 S 1 t n 1 S n 1 I n 1 R n 1 D n 1 P n 1 7


id: cord-330703-fbmy6osu
type: GGP
entity:
  E 1 t n S n I n R n D n P n Θ t Θ AB Θ Γ Θ 1 n j 2 I 1 t n 2 S n 2 I n 2 R n 2 D n 2 P n 2 n j 1 Θ n j Θ Θ t Θ AB Θ Γ Θ 2 n j 2 I 1 t n 1 S n 1 I n 1 R n 1 D n 1 P n 1 I 1 t n 2 S n 2 I n 2 R n 2 D n 2 P n 2 I 1 t n S n I n R n D n P n 2 E 1 t n 1 S n 1 I n 1 R n 1 D n 1 P n 1 7


id: cord-355689-mo4mvwch
type: GGP
entity:
  Refs 7 8 9 10 explore the impact of various topological structures within the population on the infectious diseases spread and it is convincingly found that heterogeneous networks may quicken the disease spreading within the population even lead to the absence of epidemic threshold 11 Meanwhile the individual reactions to infectious diseases may also substantially influence the diffusion processes of epidemics One of the most striking cases regarding the outbreaks


id: cord-349841-eigcqb1b
type: GGP
entity:
  S d b2 α ν µ β 1 ρ1 1 ρ2 E d b 1 c µ Aβ 1 ρ1 1 ρ2 µ b2 α ν µ β 1 ρ1 1 ρ2 b2 c µ α ν µ b1 c µ Q d b 2 Aβ 1 ρ1 1 ρ2 µ b2 α ν µ β 1 ρ1 1 ρ2 b2 c µ α ν µ b1 c µ 40 I d α b1 c µ b2c Aβ 1 ρ1 1 ρ2 µ b2 α ν µ β 1 ρ1 1 ρ2 b2 c µ α ν µ b1 c µ η µ δ R d ηI d


id: cord-320980-srpgcy4b
type: GGP
entity:
  b β 1 p ξ i µ γ 0 η µ ν γ 0 p µ φ γ 0 γ 1 ν ξ a η µ γ 0 η ν ξ i k b1 µ δ 1 p µ γ 0 η µ ν γ 0 η ν k b2


id: cord-320980-srpgcy4b
type: GGP
entity:
  k b1 η µ ν γ 0 1 p α δ φ γ 0 α δ µ γ 1 p µ φ γ 0 γ 1 µ ν γ 0 η µ 2 µ 2 µ γ 0 α η µ γ 0 φ γ 0 γ 1 ν γ 0 α δ µ 2 µ ν µ φ µ γ 0 ν φ η µ µ φ γ 0 µ ν γ 0 µ ν φ 2 γ 0 γ 1 η µ 2 µ η φ γ 0 γ 1 ν γ 0 α 2 µ 5 γ 0 2 γ 1 α µ 3 ν α µ 3 φ 3 α µ 3 γ 0 α µ 3 γ 1 α µ γ 0 3 α µ 2 ν φ 2 α µ 2 ν γ 0 α µ 2 ν γ 1 2 α µ 2 φ γ 0 3 α µ 2 γ 0 2 2 α µ 2 γ 0 γ 1 α µ ν φ γ 0 α µ ν γ 0 2 α µ ν γ 0 γ 1 α µ φ γ 0 2 k b2 2 1


id: cord-312120-xt5v3bjh
type: DNA
entity:
  Brazilian Bitcoin A DFA Approach Long range behaviour and correlation in DFA and DCCA analysis of cryptocurrencies Market Efficiency Liquidity and Multifractality


id: cord-308296-43gmzqa6
type: FAC
entity:
  Italy A Mathematical Model of Treatment and Vaccination Interventions of Pneumococcal Pneumonia Infection Dynamics


# bib

id: cord-019114-934xczf3
title: Epidemic dynamics on information-driven adaptive networks
author: Zhan, Xiu-Xiu
date: 2018-02-16
txt: ./txt/cord-019114-934xczf3.txt
cache: ./cache/cord-019114-934xczf3.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7126912/
doi: 10.1016/j.chaos.2018.02.010
summary:
  Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. By depicting preventive measures as the reduction of transmitting probability [20, 21] or particular states of individuals (immune or vaccination) [22] , previous models showed that the disease information diffusion indeed inhibits the epidemic spreading significantly (reduce the epidemic prevalence as well as enhance the epidemic threshold) [15, 23] . In this work, we consider a more complicated case that two dynamical processes (i.e., epidemic spreading and disease information diffusion) are spreading on adaptive networks. Both numerical analyses based on the pairwise approach and simulation results indicate that the information diffusion and the adaptive behavior of the nodes can inhibit the epidemic outbreak significantly.

abstract:
  Research on the interplay between the dynamics on the network and the dynamics of the network has attracted much attention in recent years. In this work, we propose an information-driven adaptive model, where disease and disease information can evolve simultaneously. For the information-driven adaptive process, susceptible (infected) individuals who have abilities to recognize the disease would break the links of their infected (susceptible) neighbors to prevent the epidemic from further spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice as well as on a real-world network give visual representations about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, a continuous dynamic behavior, bistable and endemic, to understand the evolution of the observed dynamical behaviors. This work may shed some lights on understanding how information affects human activities on responding to epidemic spreading.


id: cord-254195-k7e8g0ni
title: Solutions of a disease model with fractional white noise
author: Akinlar, M.A.
date: 2020-04-30
txt: ./txt/cord-254195-k7e8g0ni.txt
cache: ./cache/cord-254195-k7e8g0ni.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.chaos.2020.109840
doi: 10.1016/j.chaos.2020.109840
summary:
  There is no SIRS-type model which considers fractional epidemic disease models with fractional white noise or Wick product settings which makes the paper totally a new contribution to the related science. Fractional-stochastic calculus consist of fractional-order derivatives, integral operators or fractional Brownian motion and a noise term representing uncertainty or randomness in modeling. In the modeling of epidemic diseases via compartmental type mathematical models, there exists not any study considering fractional white noise, Wick product and fractional-order operators all together. From this listed contributions, we can say that the present paper is totally a new contribution to mathematical biologists studying compartment models by fractional and stochastic differential equations. The mathematical models describing epidemic diseases are generated by deterministic, stochastic or fractional-order system of ordinary differential equations. To the best of our knowledge, there exists not any mathematical model for a epidemic disease which considers both fractional-order operators and white noise together.

abstract:
  We consider an epidemic disease system by an additive fractional white noise to show that epidemic diseases may be more competently modeled in the fractional-stochastic settings than the ones modeled by deterministic differential equations. We generate a new SIRS model and perturb it to the fractional-stochastic systems. We study chaotic behavior at disease-free and endemic steady-state points on these systems. We also numerically solve the fractional-stochastic systems by an trapezoidal rule and an Euler type numerical method. We also associate the SIRS model with fractional Brownian motion by Wick product and determine numerical and explicit solutions of the resulting system. There is no SIRS-type model which considers fractional epidemic disease models with fractional white noise or Wick product settings which makes the paper totally a new contribution to the related science.


id: cord-258018-29vtxz89
title: A SIR model assumption for the spread of COVID-19 in different communities
author: Cooper, Ian
date: 2020-06-28
txt: ./txt/cord-258018-29vtxz89.txt
cache: ./cache/cord-258018-29vtxz89.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920304549?v=s5
doi: 10.1016/j.chaos.2020.110057
summary:
  The data in [29] for China, South Korea, India, Australia, USA, Italy and the state of Texas (communities) are organised in the form of time-series where the rows are recordings in time (from January to June, 2020), and the three columns are, the total cases I d tot (first column), number of infected individuals I d (second column) and deaths D d (third column). Assuming the published data are reliable, the SIR model (1) can be applied to assess the spread of the COVID-19 disease and predict the number of infected, removed and recovered populations and deaths in the communities, accommodating at the same time possible surges in the number of susceptible individuals. In this work, we have augmented the classic SIR model with the ability to accommodate surges in the number of susceptible individuals, supplemented by recorded data from China, South Korea, India, Australia, USA and the state of Texas to provide insights into the spread of COVID-19 in communities.

abstract:
  In this paper, we study the effectiveness of the modelling approach on the pandemic due to the spreading of the novel COVID-19 disease and develop a susceptible-infected-removed (SIR) model that provides a theoretical framework to investigate its spread within a community. Here, the model is based upon the well-known susceptible-infected-removed (SIR) model with the difference that a total population is not defined or kept constant per se and the number of susceptible individuals does not decline monotonically. To the contrary, as we show herein, it can be increased in surge periods! In particular, we investigate the time evolution of different populations and monitor diverse significant parameters for the spread of the disease in various communities, represented by countries and the state of Texas in the USA. The SIR model can provide us with insights and predictions of the spread of the virus in communities that the recorded data alone cannot. Our work shows the importance of modelling the spread of COVID-19 by the SIR model that we propose here, as it can help to assess the impact of the disease by offering valuable predictions. Our analysis takes into account data from January to June, 2020, the period that contains the data before and during the implementation of strict and control measures. We propose predictions on various parameters related to the spread of COVID-19 and on the number of susceptible, infected and removed populations until September 2020. By comparing the recorded data with the data from our modelling approaches, we deduce that the spread of COVID-19 can be under control in all communities considered, if proper restrictions and strong policies are implemented to control the infection rates early from the spread of the disease.


id: cord-258235-khdyxiwe
author: Chakraborty, Tanujit
date: 2020-04-30
txt: ./txt/cord-258235-khdyxiwe.txt
cache: ./cache/cord-258235-khdyxiwe.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920302502
doi: 10.1016/j.chaos.2020.109850
summary:
  To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. In this section, we first briefly discuss these datasets, followed by the development of the proposed hybrid model, and finally, the application of the proposed model to generate short-term forecasts of the future COVID-19 cases for five different countries. Algorithm 1 Proposed Hybrid ARIMA-WBF Model 1 Given a time series of length n, input the in-sample (training) COVID-19 daily cases data. Thus, these real-time short-term forecasts based on the proposed hybrid ARIMA-WBF model for Canada, France, India, South Korea, and the UK will be helpful for government officials and policymakers to allocate adequate health care resources for the coming days.

title:
  Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

abstract:
  The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting 201 countries and territories around the globe. As of April 4, 2020, it has caused a pandemic outbreak with more than 11,16,643 confirmed infections and more than 59,170 reported deaths worldwide. The main focus of this paper is two-fold: (a) generating short term (real-time) forecasts of the future COVID-19 cases for multiple countries; (b) risk assessment (in terms of case fatality rate) of the novel COVID-19 for some profoundly affected countries by finding various important demographic characteristics of the countries along with some disease characteristics. To solve the first problem, we presented a hybrid approach based on autoregressive integrated moving average model and Wavelet-based forecasting model that can generate short-term (ten days ahead) forecasts of the number of daily confirmed cases for Canada, France, India, South Korea, and the UK. The predictions of the future outbreak for different countries will be useful for the effective allocation of health care resources and will act as an early-warning system for government policymakers. In the second problem, we applied an optimal regression tree algorithm to find essential causal variables that significantly affect the case fatality rates for different countries. This data-driven analysis will necessarily provide deep insights into the study of early risk assessments for 50 immensely affected countries.


id: cord-259846-oxbmtend
author: Naik, Parvaiz Ahmad
date: 2020-06-18
txt: ./txt/cord-259846-oxbmtend.txt
cache: ./cache/cord-259846-oxbmtend.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920302265
doi: 10.1016/j.chaos.2020.109826
summary:
  Furthermore, for the fractional optimal control problem associated with the control strategies such as condom use for exposed class, treatment for aware infectives, awareness about disease among unaware infectives and behavioral change for susceptibles, we formulated a fractional optimality condition for the proposed model. We incorporate into the model time dependent controls such as condom use for exposed individuals, treatment for infected female sex workers, awareness about the disease among unaware infectives and behavioral change for susceptibles in order to reduce the risk of the spread of HIV/AIDS disease. In order to justify our theoretical findings, we introduced in this section some numerical experiments obtained for different instances of fractional power κ for the HIV epidemic model without control (9) and with control (24) along with adjoint variable systems and the control strategies. We present the numerical results for the model (9) when all control measures are absent and also to examine the role of fractional order κ on the HIV disease spread.

title:
  Global dynamics of a fractional order model for the transmission of HIV epidemic with optimal control

abstract:
  In this paper, a nonlinear fractional order epidemic model for HIV transmission is proposed and analyzed by including extra compartment namely exposed class to the basic SIR epidemic model. Also, the infected class of female sex workers is divided into unaware infectives and the aware infectives. The focus is on the spread of HIV by female sex workers through prostitution, because in the present world sexual transmission is the major cause of the HIV transmission. The exposed class contains those susceptible males in the population who have sexual contact with the female sex workers and are exposed to the infection directly or indirectly. The Caputo type fractional derivative is involved and generalized Adams-Bashforth-Moulton method is employed to numerically solve the proposed model. Model equilibria are determined and their stability analysis is considered by using fractional Routh-Hurwitz stability criterion and fractional La-Salle invariant principle. Analysis of the model demonstrates that the population is free from the disease if [Formula: see text] and disease spreads in the population if [Formula: see text]. Meanwhile, by using Lyapunov functional approach, the global dynamics of the endemic equilibrium point is discussed. Furthermore, for the fractional optimal control problem associated with the control strategies such as condom use for exposed class, treatment for aware infectives, awareness about disease among unaware infectives and behavioral change for susceptibles, we formulated a fractional optimality condition for the proposed model. The existence of fractional optimal control is analyzed and the Euler-Lagrange necessary conditions for the optimality of fractional optimal control are obtained. The effectiveness of control strategies is shown through numerical simulations and it can be seen through simulation, that the control measures effectively increase the quality of life and age limit of the HIV patients. It significantly reduces the number of HIV/AIDS patients during the whole epidemic.


id: cord-261599-ddgoxape
title: Forecasting of COVID-19 pandemic: From integer derivatives to fractional derivatives
author: Nabi, Khondoker Nazmoon
date: 2020-09-21
txt: ./txt/cord-261599-ddgoxape.txt
cache: ./cache/cord-261599-ddgoxape.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920306792
doi: 10.1016/j.chaos.2020.110283
summary:
  In a recent study, Nabi [26] has proposed a new Susceptible-Exposed-Symptomatic Infectious-Asymptomatic Infectious-Quarantined-Hospitalized-Recovered-Dead (SEI D I U QHRD) compartmental mathematical model and calibrated model parameters to project the future dynamics of COVID-19 for various COVID-19 hotspots. The advantage of applying Caputo fractional derivatives to solve the proposed COVID-19 model is the dynamics of the model can be observed more deeply using the real-time Cameroon data. The aim of this work is to forecast the probable time and size of the epidemic peaks of the novel coronavirus outbreak in Cameroon by studying a realistic compartmental model using the robust concept of Caputo fractional derivative. Section 3 is devoted to model calibration using real data of reported cases of COVID-19 in Cameroon, global sensitivity analysis of the proposed model, and forecasting of the disease future dynamics.

abstract:
  In this work, a new compartmental mathematical model of COVID-19 pandemic has been proposed incorporating imperfect quarantine and disrespectful behavior of the citizens towards lockdown policies, which are evident in most of the developing countries. An integer derivative model has been proposed initially and then the formula for calculating basic reproductive number [Formula: see text] of the model has been presented. Cameroon has been considered as a representative for the developing countries and the epidemic threshold [Formula: see text] has been estimated to be ∼ 3.41 [Formula: see text] as of July 9, 2020. Using real data compiled by the Cameroonian government, model calibration has been performed through an optimization algorithm based on renowned trust-region-reflective (TRR) algorithm. Based on our projection results, the probable peak date is estimated to be on August 1, 2020 with approximately 1073 [Formula: see text] daily confirmed cases. The tally of cumulative infected cases could reach ∼ 20, 100 [Formula: see text] cases by the end of August 2020. Later, global sensitivity analysis has been applied to quantify the most dominating model mechanisms that significantly affect the progression dynamics of COVID-19. Importantly, Caputo derivative concept has been performed to formulate a fractional model to gain a deeper insight into the probable peak dates and sizes in Cameroon. By showing the existence and uniqueness of solutions, a numerical scheme has been constructed using the Adams-Bashforth-Moulton method. Numerical simulations enlightened the fact that if the fractional order α is close to unity, then the solutions will converge to the integer model solutions, and the decrease of the fractional-order parameter (0 < α < 1) leads to the delaying of the epidemic peaks.


id: cord-268630-vu8yyisx
author: Mohammad, Mutaz
date: 2020-06-17
txt: ./txt/cord-268630-vu8yyisx.txt
cache: ./cache/cord-268630-vu8yyisx.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920303908
doi: 10.1016/j.chaos.2020.109991
summary:
  title: Implicit Riesz wavelets based-method for solving singular fractional integro-differential equations with applications to hematopoietic stem cell modeling In this paper, an effective and accurate technique based on Riesz wavelets is presented for solving weakly singular type of fractional order integro-differential equations with applications to solve system of fractional order model that describe the dynamics of uninfected, infected and free virus carried out by cytotoxic T lymphocytes (CTL). Motivated by the above contributions and properties, that are essential to develop efficient algorithms for the numerical solutions of a given fractional integro-differential equations (FIDEs), the main goal of the proposed work is to develop an efficient algorithm based on Riesz wavelets using the collocation method to solve fractional order of integro-differential equations with weakly singular kernels. In this framework, the collocation method based on Riesz wavelets has been applied to numerically solve fractional order type of integro-differential equations with singular kernel type.

title:
  Implicit Riesz wavelets based-method for solving singular fractional integro-differential equations with applications to hematopoietic stem cell modeling

abstract:
  Riesz wavelets in [Formula: see text] have been proven as a useful tool in the context of both pure and numerical analysis in many applications, due to their well prevailing and recognized theory and its natural properties such as sparsity and stability which lead to a well-conditioned scheme. In this paper, an effective and accurate technique based on Riesz wavelets is presented for solving weakly singular type of fractional order integro-differential equations with applications to solve system of fractional order model that describe the dynamics of uninfected, infected and free virus carried out by cytotoxic T lymphocytes (CTL). The Riesz wavelet in this work is constructed via the smoothed pseudo-splines refinable functions. The advantage of using such wavelets, in the context of fractional and integro-differential equations, lies on the simple structure of the reduced systems and in the powerfulness of obtaining approximated solutions for such equations that have weakly singular kernels. The proposed method shows a good performance and high accuracy orders.


id: cord-269363-drjj705k
title: Optimal quarantine control of an infectious outbreak
author: Nenchev, Vladislav
date: 2020-07-28
txt: ./txt/cord-269363-drjj705k.txt
cache: ./cache/cord-269363-drjj705k.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834584/
doi: 10.1016/j.chaos.2020.110139
summary:
  An issue of practical concern for many disease outbreaks without an available vaccine, such as for SARS-CoV-2 as of June 2020, is minimizing the overall quarantine effort or the final outbreak size, while respecting control and capacity constraints on the current number of infections. Upon an outbreak of a previously unknown disease, better model parameter estimates can be obtained as more data becomes available, and the induced optimization problem can be recomputed in a data-driven receding horizon manner to improve actions. In this work, the goal is to obtain an optimal quarantine control policy u ( t ), t ∈ [0, t f ] for a fixed final time t f , that minimizes a weighted combination of the total number of infections and the overall number of quarantined individuals at time t f .

abstract:
  This paper studies the optimal control of an infectious spread based on common epidemic models with permanent immunity and no vaccine availability. Assuming limited isolation control and capacity constraints on the number of infections, an optimal quarantine control strategy that balances between the total number of infections and the overall isolation effort is derived from necessary optimality conditions. The specific optimal policy is then obtained by optimizing the switching times of this generalized strategy. In the case of a newly emerged disease, these results can be used in a data-driven receding horizon manner to improve actions as more data becomes available. The proposed approach is applied to publicly available data from the outbreak of SARS-CoV-2 in Germany. In particular, for minimizing the total number of infections or the number of isolated individuals, the simulations indicate that a sufficiently delayed and controlled release of the lock-down are optimal for overcoming the outbreak. The approach can support public health authorities to plan quarantine control policies.


id: cord-280975-9hgtvm6d
title: Modeling and forecasting the COVID-19 pandemic in India
author: Sarkar, Kankan
date: 2020-06-28
txt: ./txt/cord-280975-9hgtvm6d.txt
cache: ./cache/cord-280975-9hgtvm6d.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2005.07071v1.pdf
doi: 10.1016/j.chaos.2020.110049
summary:
  A sensitivity analysis is conducted to determine the robustness of model predictions to parameter values and the sensitive parameters are estimated from the real data on the COVID-19 pandemic in India. [27] extended the SEIR (susceptible-exposed-infectious-removed) compartment model to study the dynamics of COVID-19 incorporating public perception of risk and the number of cumulative cases. Here, we developed 70 a new epidemiological mathematical model for novel coronavirus or SARS-CoV-2 epidemic in India that extends the standard SEIR compartment model, alike to that studied by Tang et al. We develop here a classical SEIR (susceptible-exposed-infectious-recovered)-type epidemiological model 75 by introducing contact tracing and other interventions such as quarantine, lockdown, social distancing and isolation that can represent the overall dynamics of novel coronavirus or COVID-19 (SARS-CoV-2). The square of sum of 185 the error computed as Σ n i=1 (C(i) − S(i)) 2 , where C(i) represents the observed daily new COVID-19 cases on i-th day, S(i) is the SARII q S q model simulation on i-th day and n is the sample size of the observed data.

abstract:
  In India, 1,00,340 confirmed cases and 3,155 confirmed deaths due to COVID-19 were reported as of May 18, 2020. Due to absence of specific vaccine or therapy, non-pharmacological interventions including social distancing, contact tracing are essential to end the worldwide COVID-19. We propose a mathematical model that predicts the dynamics of COVID-19 in 17 provinces of India and the overall India. A complete scenario is given to demonstrate the estimated pandemic life cycle along with the real data or history to date, which in turn divulges the predicted inflection point and ending phase of SARS-CoV-2. The proposed model monitors the dynamics of six compartments, namely susceptible (S), asymptomatic (A), recovered (R), infected (I), isolated infected (I(q)) and quarantined susceptible (S(q)), collectively expressed SARII(q)S(q). A sensitivity analysis is conducted to determine the robustness of model predictions to parameter values and the sensitive parameters are estimated from the real data on the COVID-19 pandemic in India. Our results reveal that achieving a reduction in the contact rate between uninfected and infected individuals by quarantined the susceptible individuals, can effectively reduce the basic reproduction number. Our model simulations demonstrate that the elimination of ongoing SARS-CoV-2 pandemic is possible by combining the restrictive social distancing and contact tracing. Our predictions are based on real data with reasonable assumptions, whereas the accurate course of epidemic heavily depends on how and when quarantine, isolation and precautionary measures are enforced.


id: cord-283291-lj3k53px
title: How relevant is the decision of containment measures against COVID-19 applied ahead of time?
author: Brugnago, Eduardo L.
date: 2020-08-12
txt: ./txt/cord-283291-lj3k53px.txt
cache: ./cache/cord-283291-lj3k53px.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834648/
doi: 10.1016/j.chaos.2020.110164
summary:
  The cumulative number of confirmed infected individuals by the new coronavirus outbreak until April 30(th), 2020, is presented for the countries: Belgium, Brazil, United Kingdom (UK), and the United States of America (USA). For Belgium, UK, and USA, countries with a large number of infected people, after the power-law growth, a distinct behavior is obtained when approaching saturation. We study how changing the social distance and the number of daily tests to identify infected asymptomatic individuals can interfere in the number of confirmed cases of COVID-19 when applied in three distinct days, namely April 16(th) (early), April 30(th) (current), and May 14(th) (late). One leading observation was that after an initial time with a low incidence of newly infected people, the growth of the cumulative number of confirmed cases for all studied countries followed a power-law.

abstract:
  The cumulative number of confirmed infected individuals by the new coronavirus outbreak until April 30(th), 2020, is presented for the countries: Belgium, Brazil, United Kingdom (UK), and the United States of America (USA). After an initial period with a low incidence of newly infected people, a power-law growth of the number of confirmed cases is observed. For each country, a distinct growth exponent is obtained. For Belgium, UK, and USA, countries with a large number of infected people, after the power-law growth, a distinct behavior is obtained when approaching saturation. Brazil is still in the power-law regime. Such updates of the data and projections corroborate recent results regarding the power-law growth of the virus and their strong Distance Correlation between some countries around the world. Furthermore, we show that act in time is one of the most relevant non-pharmacological weapons that the health organizations have in the battle against the COVID-19, infectious disease caused by the most recently discovered coronavirus. We study how changing the social distance and the number of daily tests to identify infected asymptomatic individuals can interfere in the number of confirmed cases of COVID-19 when applied in three distinct days, namely April 16(th) (early), April 30(th) (current), and May 14(th) (late). Results show that containment actions are necessary to flatten the curves and should be applied as soon as possible.


id: cord-288080-rr9e61ay
title: Bibliometric method for mapping the state of the art of scientific production in Covid-19
author: Mohadab, Mohamed El
date: 2020-06-30
txt: ./txt/cord-288080-rr9e61ay.txt
cache: ./cache/cord-288080-rr9e61ay.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834606/
doi: 10.1016/j.chaos.2020.110052
summary:
  The latest statistics indicate that there has been an exponential increase in the number of publications since the discovery of the Covid-19 pandemic; the results provide a comprehensive view of interdisciplinary research in medicine, biology, finance and other fields. So the use of bibliometric analysis [2] to identify and analyze the scientific performance of authors, articles, journals, institutions, countries through the analysis of keywords and the number of citations constitutes an essential element which provides researchers with the means to identify avenues and new directions in relation to a theme of scientific research. In order to observe and evaluate the trends in publications in the thematic of Covid-19, the VOSviewer software was used to analyze the academic literature and examine the evolution of published articles, co-authorship, geographic area (country) of authors, co-citation, co-occurrence. Afterwards, a bibliometric analysis method was adopted in order to map the state of the art on the theme of Covid-19, so the three scientific databases (Scopus, Web of Science, Pubmed) were used.

abstract:
  Global scientific production around the Covid-19 pandemic, in the various disciplines on the various international scientific bibliographic databases, has grown exponentially. The latter builds a source of scientific enrichment and an important lever for most researchers around the world, each of its field and its position with an ultimate aim of overcoming this pandemic. In this direction, bibliometric data constitute a fundamental source in the process of evaluation of scientific production in the academic world; bibliometrics provides researchers and institutions with crucial strategic information for the enhancement of their research results with the local and international scientific community, especially in this international pandemic.


id: cord-288894-2iaq3ayv
title: A novel mathematical approach of COVID-19 with non-singular fractional derivative
author: Kumar, Sachin
date: 2020-07-01
txt: ./txt/cord-288894-2iaq3ayv.txt
cache: ./cache/cord-288894-2iaq3ayv.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920304458
doi: 10.1016/j.chaos.2020.110048
summary:
  A new operational matrix of fractional differentiation on domain [0, a], a ≥ 1, a ∈ N by using the extended Legendre polynomial on larger domain has been developed. Finally, we provide numerical evidence and theoretical arguments that our new model can estimate the output of the exposed, infected and asymptotic carrier with higher fidelity than the previous models, thereby motivating the use of the presented model as a standard tool for examining the effect of contact rate and transmissibility multiple on number of infected cases are depicted with graphs. We will present some numerical treatments based on the number of infected people increases with increment in contact rate. The derivation of operational matrix of fractional differentiation based on orthogonal Legendre polynomial on interval [0, a ] is derived in Section 3 . The use of this newly derived matrix with Legendre collocation method is applied to solve a system of fractional ordinary differential equation.

abstract:
  We analyze a proposition which considers new mathematical model of COVID-19 based on fractional ordinary differential equation. A non-singular fractional derivative with Mittag-Leffler kernel has been used and the numerical approximation formula of fractional derivative of function [Formula: see text] is obtained. A new operational matrix of fractional differentiation on domain [0, a], a ≥ 1, a ∈ N by using the extended Legendre polynomial on larger domain has been developed. It is shown that the new mathematical model of COVID-19 can be solved using Legendre collocation method. Also, the accuracy and validity of our developed operational matrix have been tested. Finally, we provide numerical evidence and theoretical arguments that our new model can estimate the output of the exposed, infected and asymptotic carrier with higher fidelity than the previous models, thereby motivating the use of the presented model as a standard tool for examining the effect of contact rate and transmissibility multiple on number of infected cases are depicted with graphs.


id: cord-290952-tbsccwgx
author: Ullah, Saif
date: 2020-07-03
txt: ./txt/cord-290952-tbsccwgx.txt
cache: ./cache/cord-290952-tbsccwgx.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920304720
doi: 10.1016/j.chaos.2020.110075
summary:
  In this paper, we develop a mathematical model to explore the transmission dynamics and possible control of the COVID-19 pandemic in Pakistan, one of the Asian countries with a high burden of disease with more than 100,000 confirmed infected cases so far. In this paper, we develop a mathematical model to explore the transmission dynamics and possible control of the COVID-19 pandemic in Pakistan, one of the Asian countries with a high burden of disease with more than 100,000 confirmed infected cases so far. The effect of low (or mild), moderate, and comparatively strict control interventions like social-distancing, quarantine rate, (or contact-tracing of suspected people) and hospitalization (or self-isolation) of testing positive COVID-19 cases are shown graphically. The effect of low (or mild), moderate, and comparatively strict control interventions like social-distancing, quarantine rate, (or contact-tracing of suspected people) and hospitalization (or self-isolation) of testing positive COVID-19 cases are shown graphically.

title:
  Modeling the impact of non-pharmaceutical interventions on the dynamics of novel coronavirus with optimal control analysis with a case study

abstract:
  Coronavirus disease (COVID-19) is the biggest public health challenge the world is facing in recent days. Since there is no effective vaccine and treatment for this virus, therefore, the only way to mitigate this infection is the implementation of non-pharmaceutical interventions such as social-distancing, community lockdown, quarantine, hospitalization or self-isolation and contact-tracing. In this paper, we develop a mathematical model to explore the transmission dynamics and possible control of the COVID-19 pandemic in Pakistan, one of the Asian countries with a high burden of disease with more than 100,000 confirmed infected cases so far. Initially, a mathematical model without optimal control is formulated and some of the basic necessary analysis of the model, including stability results of the disease-free equilibrium is presented. It is found that the model is stable around the disease-free equilibrium both locally and globally when the basic reproduction number is less than unity. Despite the basic analysis of the model, we further consider the confirmed infected COVID-19 cases documented in Pakistan from March 1 till May 28, 2020 and estimate the model parameters using the least square fitting tools from statistics and probability theory. The results show that the model output is in good agreement with the reported COVID-19 infected cases. The approximate value of the basic reproductive number based on the estimated parameters is [Formula: see text]. The effect of low (or mild), moderate, and comparatively strict control interventions like social-distancing, quarantine rate, (or contact-tracing of suspected people) and hospitalization (or self-isolation) of testing positive COVID-19 cases are shown graphically. It is observed that the most effective strategy to minimize the disease burden is the implementation of maintaining a strict social-distancing and contact-tracing to quarantine the exposed people. Furthermore, we carried out the global sensitivity analysis of the most crucial parameter known as the basic reproduction number using the Latin Hypercube Sampling (LHS) and the partial rank correlation coefficient (PRCC) techniques. The proposed model is then reformulated by adding the time-dependent control variables u(1)(t) for quarantine and u(2)(t) for the hospitalization interventions and present the necessary optimality conditions using the optimal control theory and Pontryagin’s maximum principle. Finally, the impact of constant and optimal control interventions on infected individuals is compared graphically.


id: cord-291227-dgjieg7t
title: A model based study on the dynamics of COVID-19: Prediction and control
author: Mandal, Manotosh
date: 2020-05-13
txt: ./txt/cord-291227-dgjieg7t.txt
cache: ./cache/cord-291227-dgjieg7t.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.chaos.2020.109889
doi: 10.1016/j.chaos.2020.109889
summary:
  authors: Mandal, Manotosh; Jana, Soovoojeet; Nandi, Swapan Kumar; Khatua, Anupam; Adak, Sayani; Kar, T.K. title: A model based study on the dynamics of COVID-19: Prediction and control Further, we perform the sensitivity analysis of the essential reproduction number and found that reducing the contact of exposed and susceptible humans is the most critical factor in achieving disease control. Finally, we forecast a short-term trend of COVID-19 for the three highly affected states, Maharashtra, Delhi, and Tamil Nadu, in India, and it suggests that the first two states need further monitoring of control measures to reduce the contact of exposed and susceptible humans. A theoretical study on mathematical modeling of an 578 infectious disease with application of optimal control Early dynamics of transmission and control of COVID-19: a 591 mathematical modelling study.

abstract:
  As there is no vaccination and proper medicine for treatment, the recent pandemic caused by COVID-19 has drawn attention to the strategies of quarantine and other governmental measures, like lockdown, media coverage on social isolation, and improvement of public hygiene, etc to control the disease. The mathematical model can help when these intervention measures are the best strategies for disease control as well as how they might affect the disease dynamics. Motivated by this, in this article, we have formulated a mathematical model introducing a quarantine class and governmental intervention measures to mitigate disease transmission. We study a thorough dynamical behavior of the model in terms of the basic reproduction number. Further, we perform the sensitivity analysis of the essential reproduction number and found that reducing the contact of exposed and susceptible humans is the most critical factor in achieving disease control. To lessen the infected individuals as well as to minimize the cost of implementing government control measures, we formulate an optimal control problem, and optimal control is determined. Finally, we forecast a short-term trend of COVID-19 for the three highly affected states, Maharashtra, Delhi, and Tamil Nadu, in India, and it suggests that the first two states need further monitoring of control measures to reduce the contact of exposed and susceptible humans.


id: cord-295116-eo887olu
title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks()
author: Chimmula, Vinay Kumar Reddy
date: 2020-05-08
txt: ./txt/cord-295116-eo887olu.txt
cache: ./cache/cord-295116-eo887olu.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920302642
doi: 10.1016/j.chaos.2020.109864
summary:
  title: Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks() Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Recurrent LSTM networks has capability to address the limitations of traditional time series forecasting techniques by adapting nonlinearities of given COVID-19 dataset and can result state of the art results on temporal data. Accord-COVID-19 forecasting using LSTM Networks ing to this second model within 10 days, Canada is expected to see exponential growth of confirmed cases.

abstract:
  On March 11(th) 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14(th) day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.


id: cord-298626-duvzwxv0
author: Džiugys, Algis
date: 2020-07-29
txt: ./txt/cord-298626-duvzwxv0.txt
cache: ./cache/cord-298626-duvzwxv0.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920305580
doi: 10.1016/j.chaos.2020.110162
summary:
  The model is developed on the basis of collected epidemiological data of Covid19 pandemic, which shows that the daily growth rate of new infections has tendency to decrease linearly when the quarantine is imposed in a country (or a region) until it reaches a constant value, which corresponds to the effectiveness of quarantine measures taken in the country. We propose to build epidemic analysis and model on the dynamics of rate of new infection cases as more reliable epidemiological data together with an assumption of effectiveness to isolate registered infectious during imposed quarantine. In order to predict Covid-19 disease spread in infected country or region with imposed quarantine, a model of the growth rate of new cases needs to be developed.

title:
  Simplified model of Covid-19 epidemic prognosis under quarantine and estimation of quarantine effectiveness

abstract:
  A simplified model of Covid-19 epidemic dynamics under quarantine conditions and method to estimate quarantine effectiveness are developed. The model is based on the daily growth rate of new infections when total number of infections is significantly smaller than population size of infected country or region. The model is developed on the basis of collected epidemiological data of Covid19 pandemic, which shows that the daily growth rate of new infections has tendency to decrease linearly when the quarantine is imposed in a country (or a region) until it reaches a constant value, which corresponds to the effectiveness of quarantine measures taken in the country. The daily growth rate of new infections can be used as criteria to estimate quarantine effectiveness.


id: cord-299312-asc120pn
title: A Quantitative and Qualitative Analysis of the COVID–19 Pandemic Model
author: Khoshnaw, Sarbaz H.A.
date: 2020-05-25
txt: ./txt/cord-299312-asc120pn.txt
cache: ./cache/cord-299312-asc120pn.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920303313
doi: 10.1016/j.chaos.2020.109932
summary:
  Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. Interestingly, we identify that 27 transition rates between asymptomatic infected with both reported and unreported 28 symptomatic infected individuals are very sensitive parameters concerning model variables 29 This helps international efforts to reduce the number of infected 30 individuals from the disease and to prevent the propagation of new coronavirus more 31 widely on the community. This helps international efforts to reduce the number of infected 30 individuals from the disease and to prevent the propagation of new coronavirus more 31 widely on the community. One of the identified key parameters is the transmission rate 515 between asymptomatic infected and reported symptomatic individuals.

abstract:
  Global efforts around the world are focused on to discuss several health care strategies for minimizing the impact of the new coronavirus (COVID-19) on the community. As it is clear that this virus becomes a public health threat and spreading easily among individuals. Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates. This work reviews and develops some suggested models for the COVID-19 that can address important questions about global health care and suggest important notes. Then, we suggest an updated model that includes a system of differential equations with transmission parameters. Some key computational simulations and sensitivity analysis are investigated. Also, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half normalizations, and full normalizations. Results based on the computational simulations show that the model dynamics are significantly changed for different key model parameters. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. This helps international efforts to reduce the number of infected individuals from the disease and to prevent the propagation of new coronavirus more widely on the community. Another novelty of this paper is the identification of the critical model parameters, which makes it easy to be used by biologists with less knowledge of mathematical modeling and also facilitates the improvement of the model for future development theoretically and practically.


id: cord-299810-e57pwgnx
author: Martelloni, Gabriele
date: 2020-07-01
txt: ./txt/cord-299810-e57pwgnx.txt
cache: ./cache/cord-299810-e57pwgnx.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.chaos.2020.110064
doi: 10.1016/j.chaos.2020.110064
summary:
  Finally we study the behavior of the ratio infected over swabs for Italy, Germany and USA, and we show as studying this parameter we recover the generalized Logistic model used in [1] for these three countries. The parameters r 0 represents the rates of growth of epidemic, K is the carrying capacity for the classical logistic model, α is a constant in order to have a power low initial growth before LD, β is the exponent of the second term of equation 1 that represents the influence of asymptomatic; δ,a correction of the quadratic term of logistic, and γ are the constant parameters considering the influence of the government measures 1 , K f is a proportionality constant between deaths and total number of infected, while t d and t r are the delays of deaths and recoveries respect to infected respectively; the constant A represents the contribution of asymptomatic people as introduced in [1] and finally t 0 is the time of LD start.

title:
  Modelling the downhill of the Sars-Cov-2 in Italy and a universal forecast of the epidemic in the world

abstract:
  In a previous article [1] we have described the temporal evolution of the Sars-Cov-2 in Italy in the time window February 24-April 1. As we can see in [1] a generalized logistic equation captures both the peaks of the total infected and the deaths. In this article our goal is to study the missing peak, i.e. the currently infected one (or total currently positive). After the April 7 the large increase in the number of swabs meant that the logistical behavior of the infected curve no longer worked. So we decided to generalize the model, introducing new parameters. Moreover, we adopt a similar approach used in [1] (for the estimation of deaths) in order to evaluate the recoveries. In this way, introducing a simple conservation law, we define a model with 4 populations: total infected, currently positives, recoveries and deaths. Therefore, we propose an alternative method to a classical SIRD model for the evaluation of the Sars-Cov-2 epidemic. However, the method is general and thus applicable to other diseases. Finally we study the behavior of the ratio infected over swabs for Italy, Germany and USA, and we show as studying this parameter we recover the generalized Logistic model used in [1] for these three countries. We think that this trend could be useful for a future epidemic of this coronavirus.


id: cord-301035-dz8642qx
author: Rasheed, Jawad
date: 2020-10-10
txt: ./txt/cord-301035-dz8642qx.txt
cache: ./cache/cord-301035-dz8642qx.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33071481/
doi: 10.1016/j.chaos.2020.110337
summary:
  As the pandemic has caused great disruption to normal day-to-day operations and created a sense of unknown amongst the public, many motivated scientists and citizens have tried to assist in the COVID-19 response by developing their own unique AI-based tools to solve a large number of problems, in a variety of applied domains, such as: COIVD-19 disease detection and classification, mortality rate prediction and severity assessment, outbreak forecasting and tracking, biological insight of SARS-Cov-2 strain, and drug discovery. The investigation of this paper reveals several AI-based approaches that have been proposed as potential ways to help, with the COVID-19 pandemic, covering everything from initial diagnoses via image diagnostics up to the presentation of models that help to understand the spread of COVID-19 and identify potential new outbreak areas. Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset

title:
  A Survey on Artificial Intelligence Approaches in Supporting Frontline Workers and Decision Makers for COVID-19 Pandemic

abstract:
  While the world has experience with many different types of infectious diseases, the current crisis related to the spread of COVID-19 has challenged epidemiologists and public health experts alike, leading to a rapid search for, and development of, new and innovative solutions to combat its spread. The transmission of this virus has infected more than 18.92 million people as of August 6, 2020, with over half a million deaths across the globe; the World Health Organization (WHO) has declared this a global pandemic. A multidisciplinary approach needs to be followed for diagnosis, treatment and tracking, especially between medical and computer sciences, so, a common ground is available to facilitate the research work at a faster pace. With this in mind, this survey paper aimed to explore and understand how and which different technological tools and techniques have been used within the context of COVID-19. The primary contribution of this paper is in its collation of the current state-of-the-art technological approaches applied to the context of COVID-19, and doing this in a holistic way, covering multiple disciplines and different perspectives. The analysis is widened by investigating Artificial Intelligence (AI) approaches for the diagnosis, anticipate infection and mortality rate by tracing contacts and targeted drug designing. Moreover, the impact of different kinds of medical data used in diagnosis, prognosis and pandemic analysis is also provided. This review paper covers both medical and technological perspectives to facilitate the virologists, AI researchers and policymakers while in combating the COVID-19 outbreak.


id: cord-301150-41lfsedz
author: Sardar, Tridip
date: 2020-07-08
txt: ./txt/cord-301150-41lfsedz.txt
cache: ./cache/cord-301150-41lfsedz.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920304756
doi: 10.1016/j.chaos.2020.110078
summary:
  title: Assessment of Lockdown Effect in Some States and Overall India: A Predictive Mathematical Study on COVID-19 Outbreak By validating our model to the data on notified cases from five different states and overall India, we estimated several epidemiologically important parameters as well as the basic reproduction number (R(0)). Our result suggests that lockdown will be effective in those locations where a higher percentage of symptomatic infection exists in the population. Furthermore, the trend of the effective reproduction number (R(t)) during the projection period indicates if the lockdown measures are completely removed after May 17, 2020, a high spike in notified cases may be seen in those locations. • Using current estimate of the lockdown rate and different parameters of our mathe-230 matical model (see Table 1 and Therefore, lockdown will be effective in those region where higher 310 percentage of symptomatic infection is found in the population and also larger COVID-19 311 mass testing will be required to isolate the cases.

title:
  Assessment of Lockdown Effect in Some States and Overall India: A Predictive Mathematical Study on COVID-19 Outbreak

abstract:
  In the absence of neither an effective treatment or vaccine and with an incomplete understanding of the epidemiological cycle, Govt. has implemented a nationwide lockdown to reduce COVID-19 transmission in India. To study the effect of social distancing measure, we considered a new mathematical model on COVID-19 that incorporates lockdown effect. By validating our model to the data on notified cases from five different states and overall India, we estimated several epidemiologically important parameters as well as the basic reproduction number (R(0)). Combining the mechanistic mathematical model with different statistical forecast models, we projected notified cases in the six locations for the period May 17, 2020, till May 31, 2020. A global sensitivity analysis is carried out to determine the correlation of two epidemiologically measurable parameters on the lockdown effect and also on R(0). Our result suggests that lockdown will be effective in those locations where a higher percentage of symptomatic infection exists in the population. Furthermore, a large scale COVID-19 mass testing is required to reduce community infection. Ensemble model forecast suggested a high rise in the COVID-19 notified cases in most of the locations in the coming days. Furthermore, the trend of the effective reproduction number (R(t)) during the projection period indicates if the lockdown measures are completely removed after May 17, 2020, a high spike in notified cases may be seen in those locations. Finally, combining our results, we provided an effective lockdown policy to reduce future COVID-19 transmission in India.


id: cord-301829-6yrgkx96
author: Bhardwaj, Rashmi
date: 2020-07-25
txt: ./txt/cord-301829-6yrgkx96.txt
cache: ./cache/cord-301829-6yrgkx96.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920305488
doi: 10.1016/j.chaos.2020.110152
summary:
  Wavelet-based forecasting model predicts for shorter time span such as five to ten days advanced number of confirmed, death and recovered cases of China, India and USA. Study forecasted impending COVID-19 spread cases for China plus some other regions using mathematical & traditional time-series prediction models [22] . None of the authors have studied the wavelet based neuronal fuzzification hybrid model for the data of countrywise spread of COVID-19 genome. The forecast of 50-60 days ahead varying in every case helps to understand the clear picture of the pandemic spread and the manner in which the transmission rate may change in the following time periods in these three countries India, China and America. Data-based analysis, modelling and forecasting of the COVID-19 outbreak Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis

title:
  Data Driven Estimation of Novel COVID-19 Transmission Risks Through Hybrid Soft-Computing Techniques

abstract:
  Coronavirus genomic infection-2019 (COVID-19) has been announced as a serious health emergency arising international awareness due to its spread to 201 countries at present. In the month of April of the year 2020, it has certainly taken the pandemic outbreak of approximately 11,16,643 infections confirmed leading to around 59,170 deaths have been recorded world-over. This article studies multiple countries-based pandemic spread for the development of the COVID-19 originated in the China. This paper focuses on forecasting via real-time responses data to inherit an idea about the increase and maximum number of virus-infected cases for the various regions. In addition, it will help to understand the panic that surrounds this nCoV-19 for some intensely affecting states possessing different important demographic characteristics that would be affecting the disease characteristics. This study aims at developing soft-computing hybrid models for calculating the transmissibility of this genome viral. The analysis aids the study of the outbreak of this virus towards the other parts of the continent and the world. A hybrid of wavelet decomposed data into approximations and details then trained & tested through neuronal-fuzzification approach. Wavelet-based forecasting model predicts for shorter time span such as five to ten days advanced number of confirmed, death and recovered cases of China, India and USA. While data-based prediction through interpolation applied through moving average predicts for longer time spans such as 50-60 days ahead with lesser accuracy as compared to that of wavelet-based hybrids. Based on the simulations, the significance level (alpha) ranges from 0.10 to 0.67, MASE varying from 0.06 to 5.76, sMAPE ranges from 0.15 to 1.97, MAE varies from 22.59 to 6024.76, RMSE shows a variation from 3.18 to 8360.29 & R(2) varying through 0.0018 to 0.7149. MASE and sMAPE are relatively lesser applied and novel measures that aimed to achieve increase in accuracy. They eliminated skewness and made the model outlier-free. Estimates of the awaited outburst for regions in this study are India, China and the USA that will help in the improvement of apportionment of healthcare facilities as it can act as an early-warning system for government policy-makers. Thus, data-driven analysis will provide deep insights into the study of transmission of this viral genome estimation towards immensely affected countries. Also, the study with the help of transmission concern aims to eradicate the panic and stigma that has spread like wildfire and has become a significant part of this pandemic in these times.


id: cord-303030-8unrcb1f
title: Social distancing versus early detection and contacts tracing in epidemic management
author: Gaeta, Giuseppe
date: 2020-07-16
txt: ./txt/cord-303030-8unrcb1f.txt
cache: ./cache/cord-303030-8unrcb1f.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2003.14102v3.pdf
doi: 10.1016/j.chaos.2020.110074
summary:
  In this paper we discuss the different effects of these ingredients on the epidemic dynamics; the discussion is conducted with the help of two simple models, i.e. the classical SIR model and the recently introduced variant A-SIR (arXiv:2003.08720) which takes into account the presence of a large set of asymptomatic infectives. In the SIR model [1] [2] [3] [4] [5] , a population of constant size (this means the analysis is valid over a relatively short time-span, or we should consider new births and also deaths not due to the epidemic) is subdivided in three classes: Susceptibles, Infected (and by this also Infectives), and Removed. Acting on α or on β to get the same γ will produce different timescales for the dynamics; see Fig. 1 , in which we have used values of the parameters resulting from our fit of early data for the Northern Italy COVID-19 epidemic [7] .

abstract:
  Different countries – and sometimes different regions within the same countries – have adopted different strategies in trying to contain the ongoing COVID-19 epidemic; these mix in variable parts social confinement, early detection and contact tracing. In this paper we discuss the different effects of these ingredients on the epidemic dynamics; the discussion is conducted with the help of two simple models, i.e. the classical SIR model and the recently introduced variant A-SIR (arXiv:2003.08720) which takes into account the presence of a large set of asymptomatic infectives.


id: cord-308069-iydjrmhh
author: Contreras, Sebastián
date: 2020-07-03
txt: ./txt/cord-308069-iydjrmhh.txt
cache: ./cache/cord-308069-iydjrmhh.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.chaos.2020.110087
doi: 10.1016/j.chaos.2020.110087
summary:
  We address the existence of different delays in the report of new cases, induced by the incubation time of the virus and testing-diagnosis time gaps, and other error sources related to the sensitivity/specificity of the tests used to diagnose COVID-19. In a previous work , we proposed a methodology to obtain real-time estimations of the Effective Reproduction Number R t directly from raw data, which was satisfactorily applied to evaluate the panorama of the COVID-19 spread in different countries and to forecast its evolution (Medina-Ortiz et al., 2020a) . We present an analogous methodology to estimate the number of discharged/recovered individuals, based on the reported evolution of the viral infection, the performance of the different tests for its diagnosis, and the case fatality, which can be easily adapted for a particular country.

title:
  Statistically-based methodology for revealing real contagion trends and correcting delay-induced errors in the assessment of COVID-19 pandemic

abstract:
  COVID-19 pandemic has reshaped our world in a timescale much shorter than what we can understand. Particularities of SARS-CoV-2, such as its persistence in surfaces and the lack of a curative treatment or vaccine against COVID-19, have pushed authorities to apply restrictive policies to control its spreading. As data drove most of the decisions made in this global contingency, their quality is a critical variable for decision-making actors, and therefore should be carefully curated. In this work, we analyze the sources of error in typically reported epidemiological variables and usual tests used for diagnosis, and their impact on our understanding of COVID-19 spreading dynamics. We address the existence of different delays in the report of new cases, induced by the incubation time of the virus and testing-diagnosis time gaps, and other error sources related to the sensitivity/specificity of the tests used to diagnose COVID-19. Using a statistically-based algorithm, we perform a temporal reclassification of cases to avoid delay-induced errors, building up new epidemiologic curves centered in the day where the contagion effectively occurred. We also statistically enhance the robustness behind the discharge/recovery clinical criteria in the absence of a direct test, which is typically the case of non-first world countries, where the limited testing capabilities are fully dedicated to the evaluation of new cases. Finally, we applied our methodology to assess the evolution of the pandemic in Chile through the Effective Reproduction Number R(t), identifying different moments in which data was misleading governmental actions. In doing so, we aim to raise public awareness of the need for proper data reporting and processing protocols for epidemiological modelling and predictions.


id: cord-308115-bjyr6ehq
title: Fractional Order Model for the Role of Mild Cases in the Transmission of COVID-19
author: Baba, Isa Abdullah
date: 2020-10-20
txt: ./txt/cord-308115-bjyr6ehq.txt
cache: ./cache/cord-308115-bjyr6ehq.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920307682?v=s5
doi: 10.1016/j.chaos.2020.110374
summary:
  To execute these measures effectively, there is need to have an in depth study about the number of persons that each infected individual can infect, meanwhile a mathematical model describing the transmission dynamics of the disease should be established. [6] developed a mathematical model (for MERS) inform of nonlinear system of differential equations, in which he considered a camel to be the source of infection that spread the virus to infective human population, then human to human transmission, then to clinic center then to care center. However, they constructed the Lyapunov candidate function to investigate the local and global stability analysis of the equilibriums solution and subsequently obtained the basic reproduction number or roughly, a key parameter describing transmission of the infection. A mathematical model for COVID-19 transmission by using the Caputo fractional derivative A fractional differential equation model for the COVID-19 transmission by using the Caputo-Fabrizio derivative

abstract:
  Most of the nations with deplorable health conditions lack rapid COVID-19diagnostic test due to limited testing kits and laboratories. The un-diagnosticmild cases (who show no critical sign and symptoms) play the role as a route that spread the infection unknowingly to healthy individuals. In this paper, we present a fractional order SIR model incorporating individual with mild cases as a compartment to become SMIR model. The existence of the solutions of the model is investigated by solving the fractional Gronwall's inequality using the Laplace transform approach. The equilibrium solutions (DFE & Endemic) are found to be locally asymptotically stable, and subsequently the basic reproduction number is obtained. Also the global stability analysis is carried out by constructing Lyapunov function. Lastly, numerical simulations that support analytic solution follow. It was also shown that when the rate of infection of the mild cases increases, there is equivalent increase in the overall population of infected individuals. Hence to curtail the spread of the disease there is need to take care of the Mild cases as well.


id: cord-308296-43gmzqa6
title: A novel mathematics model of covid-19 with fractional derivative. Stability and numerical analysis
author: Alkahtani, Badr Saad T.
date: 2020-06-17
txt: ./txt/cord-308296-43gmzqa6.txt
cache: ./cache/cord-308296-43gmzqa6.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920304045?v=s5
doi: 10.1016/j.chaos.2020.110006
summary:
  title: A novel mathematics model of covid-19 with fractional derivative. Uncertainties around the spread of Covid-19 have lead many researchers to understand investigation in many field of technology, science and engineering in the last five months since its appearance in Wuhan-China last December-2019 Many mathematical models were suggested in the last five months with the aim to understand the dynamics spread of the novel deathly disease [10] . with the function f differentiable then, the definition of the new fractional derivative (Atangana-Baleanu derivative in Caputo sense) is given as In this paper, we considered a set of 8 nonlinear ordinary differential equations to model the spread of covid-19 in a given population. We presented the positivity of each class as function of time, for classical and fractional case. New numerical approach for fractional differential equations

abstract:
  a mathematical model depicting the spread of covid-19 epidemic and implementation of population covid-19 intervention in Italy. The model has 8 components leading to system of 8 ordinary differential equations. In this paper, we investigate the model using the concept of fractional differential operator. A numerical method based on the Lagrange polynomial was used to solve the system equations depicting the spread of COVID-19. A detailed investigation of stability including reproductive number using the next generation matrix, and the Lyapunov were presented in detail. Numerical simulations are depicted for various fractional orders.


id: cord-309758-2rnhrbeq
title: SIRSi compartmental model for COVID-19 pandemic with immunity loss
author: Batistela, Cristiane M.
date: 2020-10-29
txt: ./txt/cord-309758-2rnhrbeq.txt
cache: ./cache/cord-309758-2rnhrbeq.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920307827
doi: 10.1016/j.chaos.2020.110388
summary:
  The proposed Susceptible -Infected -Removed -Sick (SIRSi) model also considers birth and death of individuals in the given population and introduces a feedback from those in the recovered group who did not gain immunity or lost their immunity after a period of time. In this section the parameters of the proposed SIRSi model (1) (see Fig. 1 ) are numerically adjusted to fit the curve of confirmed symptomatic cases of three major cities in the state of São Paulo -Brazil, using publicly available data from the State Data Analysis System -SEADE ( Sistema Estadual de Análise de Dados 2 ) [47] . The proposed model with re-susceptibility feedback adjusted to the confirmed infection data, suggests the possibility that recovered patients may have temporary immunity γ > 0 or even permanent γ = 0 .

abstract:
  The coronavirus disease 2019 (Covid-19) outbreak led the world to an unprecedented health and economic crisis. In an attempt to respond to this emergency, researchers worldwide are intensively studying the dynamics of the Covid-19 pandemic. In this study, a Susceptible - Infected - Removed - Sick (SIRSi) compartmental model is proposed, which is a modification of the classical Susceptible - Infected - Removed (SIR) model. The proposed model considers the possibility of unreported or asymptomatic cases, and differences in the immunity within a population, i.e., the possibility that the acquired immunity may be temporary, which occurs when adopting one of the parameters ([Formula: see text]) other than zero. Local asymptotic stability and endemic equilibrium conditions are proved for the proposed model. The model is adjusted to the data from three major cities of the state of São Paulo in Brazil, namely, São Paulo, Santos, and Campinas, providing estimations of duration and peaks related to the disease propagation. This study reveals that temporary immunity favors a second wave of infection and it depends on the time interval for a recovered person to be susceptible again. It also indicates the possibility that a greater number of patients would get infected with decreased time for reinfection.


id: cord-311054-dwns5l64
title: Evaluation and prediction of COVID-19 in India: a case study of worst hit states
author: Rafiq, Danish
date: 2020-06-19
txt: ./txt/cord-311054-dwns5l64.txt
cache: ./cache/cord-311054-dwns5l64.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920304124?v=s5
doi: 10.1016/j.chaos.2020.110014
summary:
  For example, in [12] , a data-driven estimation method like long short-term memory (LSTM) is used for the prediction of total number of COVID-19 cases in India for a 30-days ahead prediction window. The model is then used for the prediction of the total number of cases and deaths in most affected states of India for the next 30 days. To estimate the spread of COVID-19 in India, we used a Predictive Error Minimization (PEM) based system identification technique to identify a discrete-time, single-input, single-output (SISO) model [19] [20] [21] . The models were then verified on the testing data and upon validation, the models were used to predict the total number of cases and deaths for the next 30-days in the 10 worst hit states in India. As per our prediction based on data up to 17 th May 2020, Delhi along with other states would continue to see marginal surge in the number of COVID-19 cases owing to the relaxations in lock-down measures.

abstract:
  In this manuscript, system modeling and identification techniques are applied in developing a prognostic yet deterministic model to forecast the spread of COVID-19 in India. The model is verified with the historical data and a forecast of 30-days ahead is presented for the 10 most affected states of India. The major results suggest that our model can very well capture the disease variations with high accuracy. Results also show a steep rise in the total cumulative cases and deaths in the coming weeks.


id: cord-311544-7ihtyiox
title: Modeling COVID-19 Epidemic in Heilongjiang Province, China
author: Sun, Tingzhe
date: 2020-05-29
txt: ./txt/cord-311544-7ihtyiox.txt
cache: ./cache/cord-311544-7ihtyiox.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920303489
doi: 10.1016/j.chaos.2020.109949
summary:
  However, massive imported patients especially into Heilongjiang Province in China recently have been an alert for local COVID-19 outbreak. Stochastic simulations further showed that significantly increased local contacts among imported ''escaper'', its epidemiologically associated cases and susceptible populations greatly contributed to the local outbreak of COVID-19. Collectively, our model has characterized the epidemic of COVID-19 in Heilongjiang province and implied that strongly controlled measured should be taken for infected and asymptomatic patients to minimize total infections. Specifically, a recent ''super spreader'' or ''imported escaper'' in Heilongjiang province has led to tens of diagnosed or asymptomatic cases [3] . Using this model, we performed stochastic simulations and found that partial relief in strictly controlled interventions may contribute to the occurrence of diagnosed patients recently (from April 9 to April 19) provided that there is only one imported patient without surveillance [3] . Estimating the Effects of Asymptomatic and Imported Patients on COVID-19 Epidemic Using Mathematical Modeling

abstract:
  The Coronavirus Disease 2019 (COVID-19) surges worldwide. However, massive imported patients especially into Heilongjiang Province in China recently have been an alert for local COVID-19 outbreak. We collected data from January 23 to March 25 from Heilongjiang province and trained an ordinary differential equation model to fit the epidemic data. We extended the simulation using this trained model to characterize the effect of an imported ‘escaper’. We showed that an imported ‘escaper’ was responsible for the newly confirmed COVID-19 infections from Apr 9 to Apr 19 in Heilongjiang province. Stochastic simulations further showed that significantly increased local contacts among imported ‘escaper’, its epidemiologically associated cases and susceptible populations greatly contributed to the local outbreak of COVID-19. Meanwhile, we further found that the reported number of asymptomatic patients was markedly lower than model predictions implying a large asymptomatic pool which was not identified. We further forecasted the effect of implementing strong interventions immediately to impede COVID-19 outbreak for Heilongjiang province. Implementation of stronger interventions to lower mutual contacts could accelerate the complete recovery from coronavirus infections in Heilongjiang province. Collectively, our model has characterized the epidemic of COVID-19 in Heilongjiang province and implied that strongly controlled measured should be taken for infected and asymptomatic patients to minimize total infections.


id: cord-312120-xt5v3bjh
author: Lahmiri, Salim
date: 2020-05-28
txt: ./txt/cord-312120-xt5v3bjh.txt
cache: ./cache/cord-312120-xt5v3bjh.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.chaos.2020.109936
doi: 10.1016/j.chaos.2020.109936
summary:
  The measures of Largest Lyapunov Exponent (LLE) based on the Rosenstein''s method and Approximate Entropy (ApEn), which are robust to small samples, are applied to price time series in order to estimate degrees of stability and irregularity in cryptocurrency and international stock markets. During the COVID-19 pandemic period it was found that (a) the level of stability in cryptocurrency markets has significantly diminished while the irregularity level significantly augmented, (b) the level of stability in international equity markets has not changed but gained more irregularity, (c) cryptocurrencies became more volatile, (d) the variability in stability and irregularity in equities has not been affected, (e) cryptocurrency and stock markets exhibit a similar degree of stability in price dynamics, whilst finally (f) cryptocurrency exhibit a low level of regularity compared to international equity markets. Hence, measuring both LLE and approximate entropy in price time series allows to assess divergence/convergence and regularity/irregularity of cryptocurrency and stock time series before and during Covid-19 pandemic.

title:
  The Impact of COVID-19 pandemic upon Stability and Sequential Irregularity of Equity and Cryptocurrency Markets

abstract:
  We explore the evolution of the informational efficiency in 45 cryptocurrency markets and 16 international stock markets before and during COVID-19 pandemic. The measures of Largest Lyapunov Exponent (LLE) based on the Rosenstein's method and Approximate Entropy (ApEn), which are robust to small samples, are applied to price time series in order to estimate degrees of stability and irregularity in cryptocurrency and international stock markets. The amount of regularity infers on the unpredictability of fluctuations. The t-test and F-test are performed on estimated LLE and ApEn. In total, 36 statistical tests are performed to check for differences between time periods (pre- versus during COVID-19 pandemic samples) on the one hand, as well as check for differences between markets (cryptocurrencies versus stocks), on the other hand. During the COVID-19 pandemic period it was found that (a) the level of stability in cryptocurrency markets has significantly diminished while the irregularity level significantly augmented, (b) the level of stability in international equity markets has not changed but gained more irregularity, (c) cryptocurrencies became more volatile, (d) the variability in stability and irregularity in equities has not been affected, (e) cryptocurrency and stock markets exhibit a similar degree of stability in price dynamics, whilst finally (f) cryptocurrency exhibit a low level of regularity compared to international equity markets. We find that cryptos showed more instability and more irregularity during the COVID-19 pandemic compared to international stock markets. Thus, from an informational efficiency perspective, investing in digital assets during big crises as the COVID-19 pandemic, could be considered riskier as opposed to equities.


id: cord-315676-y0qbkszx
title: Predictions for COVID-19 with Deep Learning Models of LSTM, GRU and Bi-LSTM
author: Shahid, Farah
date: 2020-08-19
txt: ./txt/cord-315676-y0qbkszx.txt
cache: ./cache/cord-315676-y0qbkszx.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920306081?v=s5
doi: 10.1016/j.chaos.2020.110212
summary:
  In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19.  Statistical models as ARIMA, ML technique of SVR with polynomial and RBF kernels, and DL mechanisms of LSTM, GRU and Bi-LSTM are proposed to predict the COVID-19 three categories, confirmed cases, deaths and recovered cases for ten countries. Parameters with their values of SVR, ARIMA and LSTM is shown in Table 1 , while results of actual and predicted cases in three categories in terms of performance measures are presented in Table 2 .  COVID-19 dataset has been modelled using various regressors including ARIMA, SVR with polynomial and RBF kernels, LSTM, GRU and Bi-LSTM for future predictions on confirmed cases, deaths and recovered case for ten countries across the globe.

abstract:
  COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.


id: cord-316705-3wzurnfp
author: Lalmuanawma, Samuel
date: 2020-06-25
txt: ./txt/cord-316705-3wzurnfp.txt
cache: ./cache/cord-316705-3wzurnfp.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920304562?v=s5
doi: 10.1016/j.chaos.2020.110059
summary:
  A new novel model, that forecast and predicting 1-3 to 6 days ahead of total Covid-19 patient of 10 Brazilian states, using stacking-ensemble with support vector regression algorithm on the cumulative positive Covid-19 cases of Brazilian data was proposed, thus augmenting the short-term forecasting process to alert the healthcare expert and the government to tackle the pandemic [38] . A Canadian based forecasting model using time-series was developed employing Deep learning algorithm for the long-short-term-memory network, the studies found out a key factor intended for predicting the course with an ending point estimation of the current SARS-CoV-2 epidemic in Canada and all over the globe [40] . Since the outbreak of the novel SARS-CoV-2, scientists and medical industries around the globe ubiquitously urged to fight against the pandemic, searching alternative method of rapid screening and prediction process, contact tracing, forecasting, and development of vaccine or drugs with the more accurate and reliable operation.

title:
  Applications of Machine Learning and Artificial Intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

abstract:
  BACKGROUND AND OBJECTIVE: : During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic. METHOD: A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic. RESULT: This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead. CONCLUSION: The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic.


id: cord-317371-v7hmc9sj
title: Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries
author: Zhang, Xiaolei
date: 2020-04-20
txt: ./txt/cord-317371-v7hmc9sj.txt
cache: ./cache/cord-317371-v7hmc9sj.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920302290
doi: 10.1016/j.chaos.2020.109829
summary:
  In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries. To identify the turning point and predict the further spread of COVID-19 outbreaks while accounting for governments enforcement of stay-at-home advises/orders, social distancing, lockdowns, and quarantines against COVID-19, we combine the power law with the exponential law for daily new cases based on a segmented Poisson model.

abstract:
  In this paper, we employed a segmented Poisson model to analyze the available daily new cases data of the COVID-19 outbreaks in the six Western countries of the Group of Seven, namely, Canada, France, Germany, Italy, UK and USA. We incorporated the governments’ interventions (stay-at-home advises/orders, lockdowns, quarantines and social distancing) against COVID-19 into consideration. Our analysis allowed us to make a statistical prediction on the turning point (the time that the daily new cases peak), the duration (the period that the outbreak lasts) and the attack rate (the percentage of the total population that will be infected over the course of the outbreak) for these countries.


id: cord-319804-i5oprni9
author: Mahajan, Ashutosh
date: 2020-07-28
txt: ./txt/cord-319804-i5oprni9.txt
cache: ./cache/cord-319804-i5oprni9.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834644/
doi: 10.1016/j.chaos.2020.110156
summary:
  In this paper, we employ a compartmental epidemic model SIPHERD for COVID-19 and predict the total number of confirmed, active and death cases, and daily new cases. A different compartmental model SEIR [9] predicts the dynamics of the transmission of the COVID-19 for certain countries, and the impact of quarantine of the infected persons are also studied in it. We employ an improved mathematical model SIPHERD [19] for the COVID-19 pandemic embedding the purely asymptomatic infected cases and the transmission of the disease from them. The model simulations bring out the efficacy of different ways for the containment, by predicting the total number of active and confirmed cases, total deaths, and daily new positive cases considering various social distancing/lockdown conditions and the number of tests done per day. An epidemic model sipherd and its application for prediction of the spread of covid-19 infection for india and usa

title:
  An Epidemic Model SIPHERD and its application for prediction of the spread of COVID-19 infection in India

abstract:
  Originating from Wuhan, China, in late 2019, and with a gradual spread in the last few months, COVID-19 has become a pandemic crossing 9 million confirmed positive cases and 450 thousand deaths. India is not only an overpopulated country but has a high population density as well, and at present, a high-risk nation where COVID-19 infection can go out of control. In this paper, we employ a compartmental epidemic model SIPHERD for COVID-19 and predict the total number of confirmed, active and death cases, and daily new cases. We analyze the impact of lockdown and the number of tests conducted per day on the prediction and bring out the scenarios in which the infection can be controlled faster. Our findings indicate that increasing the tests per day at a rapid pace (10k per day increase), stringent measures on social-distancing for the coming months and strict lockdown in the month of July all have a significant impact on the disease spread.


id: cord-320262-9zxgaprl
author: Asamoah, Joshua Kiddy K.
date: 2020-07-10
txt: ./txt/cord-320262-9zxgaprl.txt
cache: ./cache/cord-320262-9zxgaprl.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834629/
doi: 10.1016/j.chaos.2020.110103
summary:
  title: Global stability and cost-effectiveness analysis of COVID-19 considering the impact of the environment:using data from Ghana that other optimal control model on COVID-19 have been studied (see for example [27, 28, 29 , 30, 31, The model further assumes that, no exposed individual transmits the disease. It is further inferred from this 310 study that; applying optimal control strategy on the rate at which the virus is released into the system, m 1 311 and m 2 , and also on the relative transmission rate due to human behaviour will considerably strike down 312 COVID-19 pandemic. Early dynamics of transmission and control 376 of COVID-19: a mathematical modelling study A model based study on the dynamics 431 of COVID-19: Prediction and control A model based study on the dynamics 431 of COVID-19: Prediction and control Modeling the impact of non-pharmaceutical interventions on the dynamics of 435 novel coronavirus with optimal control analysis with a case study Modelling of rabies transmission dynamics 477 using optimal control analysis

title:
  Global stability and cost-effectiveness analysis of COVID-19 considering the impact of the environment:using data from Ghana

abstract:
  COVID-19 potentially threatens the lives and livelihood of people all over the world. The disease is presently a major health concern in Ghana and the rest of the world. Although, human to human transmission dynamics has been established, not much research is done on the dynamics of the virus in the environment and the role human play by releasing the virus into the environment. Therefore, investigating the human-environment-human by use of mathematical analysis and optimal control theory is relatively necessary. The dynamics of COVID-19 for this study is segregated into compartments as: Susceptible (S), Exposed (E), Asymptomatic (A), symptomatic (I), Recovered (R) and the Virus in the environment/surfaces (V). The basic reproduction number [Formula: see text] without controls is computed. The application of Lyapunov’s function is used to analyse the global stability of the proposed model. We fit the model to real data from Ghana in the time window 12th March 2020 to 7th May 2020, with the aid of python programming language using the least-squares method. The average basic reproduction number without controls, [Formula: see text] is approximately 2.68. An optimal control is formulated based on the sensitivity analysis. Numerical simulation of the model is also done to verify the analytic results. The admissible control set such as: effective testing and quarantine when boarders are opened, the usage of masks and face shields through media education, cleaning of surfaces with home-based detergents, practising proper cough etiquette and fumigating commercial areas; health centers is simulated in MATLAB. We used forward-backward sweep Runge-Kutta scheme which gave interesting results in the main text, for example, the cost-effectiveness analysis shows that, Strategy 4 (cleaning of surfaces with home-based detergents) is the most cost-effective strategy among all the six control intervention strategies under consideration.


id: cord-320980-srpgcy4b
author: Aldila, Dipo
date: 2020-06-28
txt: ./txt/cord-320980-srpgcy4b.txt
cache: ./cache/cord-320980-srpgcy4b.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834600/
doi: 10.1016/j.chaos.2020.110042
summary:
  title: A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment : The case of Jakarta, Indonesia The aim of this study is to investigate the effects of rapid testing and social distancing in controlling the spread of COVID-19, particularly in the city of Jakarta, Indonesia. The objective of our study is to analyze the effect of rapid testing and self-monitored isolation, and to predict the long-term dynamics of the incidence data of Jakarta, Indonesia. 265 Figure 10 : long-time simulation for prediction of incidence of COVID-19 in Jakarta with easing the social distancing policy combined with more massive rapid test and hospitalization. The model considers many important factors, such as hidden cases, rapid testing to trace hidden cases, limitation of medical resources, social distancing, quarantine/isolation, and parameter estimation for the incidence date from the city of Jakarta, Indonesia.

title:
  A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment : The case of Jakarta, Indonesia

abstract:
  The aim of this study is to investigate the effects of rapid testing and social distancing in controlling the spread of COVID-19, particularly in the city of Jakarta, Indonesia. We formulate a modified susceptible exposed infectious recovered compartmental model considering asymptomatic individuals. Rapid testing is intended to trace the existence of asymptomatic infected individuals among the population. This asymptomatic class is categorized into two subclasses: detected and undetected asymptomatic individuals. Furthermore, the model considers the limitations of medical resources to treat an infected individual in a hospital. The model shows two types of equilibrium point: COVID-19 free and COVID-19 endemic. The COVID-19-free equilibrium point is locally and asymptotically stable if the basic reproduction number [Formula: see text] is less than unity. In contrast, COVID-19-endemic equilibrium always exists when [Formula: see text]. The model can also show a backward bifurcation at [Formula: see text] whenever the treatment saturation parameter, which describes the hospital capacity, is larger than a specific threshold. To justify the model parameters, we use the incidence data from the city of Jakarta, Indonesia. The data pertain to infected individuals who self-isolate in their homes and visit the hospital for further treatment. Our numerical experiments indicate that strict social distancing has the potential to succeed in reducing and delaying the time of an outbreak. However, if the strict social distancing policy is relaxed, a massive rapid-test intervention should be conducted to avoid a large-scale outbreak in the future.


id: cord-322862-dcb237an
title: SBDiEM: A new Mathematical model of Infectious Disease Dynamics
author: Bekiros, Stelios
date: 2020-04-23
txt: ./txt/cord-322862-dcb237an.txt
cache: ./cache/cord-322862-dcb237an.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920302289?v=s5
doi: 10.1016/j.chaos.2020.109828
summary:
  A worldwide multi-scale interplay among a plethora of factors, ranging from micro-pathogens and individual or population interactions to macro-scale environmental, socio-economic and demographic conditions, entails the development of highly sophisticated mathematical models for robust representation of the contagious disease dynamics that would lead to the improvement of current outbreak control strategies and vaccination and prevention policies. Our study presents for the …rst time a new stochastic mathematical model for describing infectious dynamics and tracking virus temporal transmissibility on 3-dimensional space (earth). As a matter of fact, it introduces a novel approach to mathematical modelling of infectious dynamics of any disease, and sets a starting point for conducting simulations, forecasting and nowcasting investigations based on real-world stereographic and spherical tracking on earth. In what follows, we compute the transition and transmission density for the X t ; t 0, and we derive the stochastic di¤erential equations that govern the infectious disease dynamics for X t ; t 0 in those local coordinates.

abstract:
  A worldwide multi-scale interplay among a plethora of factors, ranging from micro-pathogens and individual or population interactions to macro-scale environmental, socio-economic and demographic conditions, entails the development of highly sophisticated mathematical models for robust representation of the contagious disease dynamics that would lead to the improvement of current outbreak control strategies and vaccination and prevention policies. Due to the complexity of the underlying interactions, both deterministic and stochastic epidemiological models are built upon incomplete information regarding the infectious network. Hence, rigorous mathematical epidemiology models can be utilized to combat epidemic outbreaks. We introduce a new spatiotemporal approach (SBDiEM) for modeling, forecasting and nowcasting infectious dynamics, particularly in light of recent efforts to establish a global surveillance network for combating pandemics with the use of artificial intelligence. This model can be adjusted to describe past outbreaks as well as COVID-19. Our novel methodology may have important implications for national health systems, international stakeholders and policy makers.


id: cord-325862-rohhvq4h
author: Zhang, Yong
date: 2020-06-04
txt: ./txt/cord-325862-rohhvq4h.txt
cache: ./cache/cord-325862-rohhvq4h.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920303581?v=s5
doi: 10.1016/j.chaos.2020.109959
summary:
  The model results revealed that 1) the transmission, infection and recovery dynamics follow the integral-order SEIR model with significant spatiotemporal variations in the recovery rate, likely due to the continuous improvement of screening techniques and public hospital systems, as well as full city lockdowns in China, and 2) the evolution of number of deaths follows the time FDE, likely due to the time memory in the death toll. The main contributions of this work, therefore, include 1) the first application of FDEs in modeling the evolution of the COVID-19 death toll, 2) an updated SEIR model with a transient recovery rate to better capture the dynamics of COVID-19 pandemic within China and for other countries, and 3) a particle-tracking approach based on stochastic bimolecular reaction theory to evaluate the mitigation of the spread of the COVID-19 outbreak.

title:
  Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19

abstract:
  Fractional calculus provides a promising tool for modeling fractional dynamics in computational biology, and this study tests the applicability of fractional-derivative equations (FDEs) for modeling the dynamics and mitigation scenarios of the novel coronavirus for the first time. The coronavirus disease 2019 (COVID-19) pandemic radically impacts our lives, while the evolution dynamics of COVID-19 remain obscure. A time-dependent Susceptible, Exposed, Infectious, and Recovered (SEIR) model was proposed and applied to fit and then predict the time series of COVID-19 evolution observed over the last three months (up to 3/22/2020) in China. The model results revealed that 1) the transmission, infection and recovery dynamics follow the integral-order SEIR model with significant spatiotemporal variations in the recovery rate, likely due to the continuous improvement of screening techniques and public hospital systems, as well as full city lockdowns in China, and 2) the evolution of number of deaths follows the time FDE, likely due to the time memory in the death toll. The validated SEIR model was then applied to predict COVID-19 evolution in the United States, Italy, Japan, and South Korea. In addition, a time FDE model based on the random walk particle tracking scheme, analogous to a mixing-limited bimolecular reaction model, was developed to evaluate non-pharmaceutical strategies to mitigate COVID-19 spread. Preliminary tests using the FDE model showed that self-quarantine may not be as efficient as strict social distancing in slowing COVID-19 spread. Therefore, caution is needed when applying FDEs to model the coronavirus outbreak, since specific COVID-19 kinetics may not exhibit nonlocal behavior. Particularly, the spread of COVID-19 may be affected by the rapid improvement of health care systems which may remove the memory impact in COVID-19 dynamics (resulting in a short-tailed recovery curve), while the death toll and mitigation of COVID-19 can be captured by the time FDEs due to the nonlocal, memory impact in fatality and human activities.


id: cord-327544-7ws2kleo
title: Impact assessment of containment measure against COVID-19 spread in Morocco
author: Hammoumi, Aayah
date: 2020-08-22
txt: ./txt/cord-327544-7ws2kleo.txt
cache: ./cache/cord-327544-7ws2kleo.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32863612/
doi: 10.1016/j.chaos.2020.110231
summary:
  Since the appearance of the first case of COVID-19 in Morocco on March, 02,2020, the cumulative number of reported infectious cases continues to increase and, up to date, the peak-time of infection is not reached yet. In this study, we propose a Susceptible-Asymptomatic-Infectious deterministic model to evaluate the impact of compulsory containment imposed in Morocco on March, 21 on the spread of COVID-19 epidemic across the country. Since the containment measure started 19 days since the first reported case then the model equations without containment is defined for 0 ≤ t < t 0 := 19 as follows Here, we assume that confined asymptomatic and confined unreported individuals can still spread the virus to their families. We used reported infectious case data, from March 2nd to April 9th, 2020, provided by the Health Ministry of Morocco to parameterize the model.

abstract:
  Since the appearance of the first case of COVID-19 in Morocco on March, 02,2020, the cumulative number of reported infectious cases continues to increase and, up to date, the peak-time of infection is not reached yet. In this study, we propose a Susceptible-Asymptomatic-Infectious deterministic model to evaluate the impact of compulsory containment imposed in Morocco on March, 21 on the spread of COVID-19 epidemic across the country. The model takes account of the unconfined individuals that continue to work or to leave their home for urgent needs and the existence of infectious asymptomatic and unreported individuals within susceptible population. Furthermore, the model is able to predict the peak-size, peak-time, final size and epidemic duration according to different rates of containment. Advanced knowledge of these details will be of great interest to establish an optimal plan-of-action to control or eradicate the epidemic. Indeed, mitigating and delaying the epidemic peak allow the official health authorities to anticipate and control the spread of COVID-19. Moreover, prediction of the epidemic duration can help the government to predict the end time of containment to avoid consequent social-economic damages as well. Using our model, the basic reproduction number R(0) is estimated to be 2.9949, with [Formula: see text] reflecting a high speed of spread of the epidemic. The model shows that the compulsory containment can be efficient if more than 73% of population are confined. In the absence of other efficient measure of control, even with 90% of containment, the end-time is estimated to happen on July, 4,2020 with 7558 final cumulative cases. Furthermore, a threshold value of containment rate, below which the epidemic duration is postponed, has been determined. Finally, the sensitivity analysis is performed and showed that the COVID-19 dynamics strongly depends on the asymptomatic duration as well as the contact and containment rates. Our previsions can help the government to adjust its plan-of-action to fight the disease and to face the social-economic shock induced by the containment.


id: cord-328069-a9fi9ssg
author: Pathan, Refat Khan
date: 2020-06-13
txt: ./txt/cord-328069-a9fi9ssg.txt
cache: ./cache/cord-328069-a9fi9ssg.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920304161
doi: 10.1016/j.chaos.2020.110018
summary:
  title: Time Series Prediction of COVID-19 by Mutation Rate Analysis using Recurrent Neural Network-based LSTM Model This study explores the mutation rate of the whole genomic sequence gathered from the patient''s dataset of different countries. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, The United States, and the rest of the World. Using this train and testing process, the nucleotide mutation rate of 400(th) patient in future time has been predicted. The complete genomic sequence (Wuhan-HU1) of this large RNA virus (SARS-CoV-2) was first discovered in the laboratory of China on 10th January [10] and placed in the NCBI GenBank. al have performed Phylogenetic analysis of SARS-CoV-2 virus based on the spike gene of the genomic sequence [17] . An adequate amount of gene dataset is currently available in the NCBI GenBank which has the complete genome sequence of SARS-CoV-2.

title:
  Time Series Prediction of COVID-19 by Mutation Rate Analysis using Recurrent Neural Network-based LSTM Model

abstract:
  SARS-CoV-2, a novel coronavirus mostly known as COVID-19 has created a global pandemic. The world is now immobilized by this infectious RNA virus. As of May 18, already more than 4.8 million people have been infected and 316k people died. This RNA virus has the ability to do the mutation in the human body. Accurate determination of mutation rates is essential to comprehend the evolution of this virus and to determine the risk of emergent infectious disease. This study explores the mutation rate of the whole genomic sequence gathered from the patient's dataset of different countries. The collected dataset is processed to determine the nucleotide mutation and codon mutation separately. Furthermore, based on the size of the dataset, the determined mutation rate is categorized for four different regions: China, Australia, The United States, and the rest of the World. It has been found that a huge amount of Thymine (T) and Adenine (A) are mutated to other nucleotides for all regions, but codons are not frequently mutating like nucleotides. A recurrent neural network-based Long Short Term Memory (LSTM) model has been applied to predict the future mutation rate of this virus. The LSTM model gives Root Mean Square Error (RMSE) of 0.06 in testing and 0.04 in training, which is an optimized value. Using this train and testing process, the nucleotide mutation rate of 400(th) patient in future time has been predicted. About 0.1% increment in mutation rate is found for mutating of nucleotides from T to C and G, C to G and G to T. While a decrement of 0.1% is seen for mutating of T to A, and A to C. It is found that this model can be used to predict day basis mutation rates if more patient data is available in updated time.


id: cord-330703-fbmy6osu
author: Zhang, Zizhen
date: 2020-07-28
txt: ./txt/cord-330703-fbmy6osu.txt
cache: ./cache/cord-330703-fbmy6osu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834655/
doi: 10.1016/j.chaos.2020.110175
summary:
  title: Mathematical model of Ebola and covid-19 with fractional differential operators: Non-Markovian process and class for virus pathogen in the environment Differential operators based on convolution definitions have been recognized as powerful mathematics tools to help model real world problems due to the properties associated to their different kernels. In this paper, we used new trend of fractional differential and integral operators to model the spread of Ebola and Covid-19. The left Caputo fractional derivative of order of the function f is given by the following equality; Thus, we can present the following scheme for numerical solution of our above equation as S 1 (t n−1 , S n−1 , I n−1 , R n−1 , D n−1 , P n−1 ) − S 1 (t n−2 , S n−2 , I n−2 , R n−2 , D n−2 , P n−2 )

title:
  Mathematical model of Ebola and covid-19 with fractional differential operators: Non-Markovian process and class for virus pathogen in the environment

abstract:
  Differential operators based on convolution definitions have been recognized as powerful mathematics tools to help model real world problems due to the properties associated to their different kernels. In particular the power law kernel helps include into mathematical formulation the effect of long range, while the exponential decay helps with fading memory, also with Poisson distribution properties that lead to a transitive behavior from Gaussian to non-Gaussian phases respectively, however, with steady state in time and finally the generalized Mittag-Leffler helps with many features including the queen properties, transitive behaviors, random walk for earlier time and power law for later time. Very recently both Ebola and Covid-19 have been a great worry around the globe, thus scholars have focused their energies in modeling the behavior of such fatal diseases. In this paper, we used new trend of fractional differential and integral operators to model the spread of Ebola and Covid-19.


id: cord-332618-8al98ya2
author: Barraza, Néstor Ruben
date: 2020-09-18
txt: ./txt/cord-332618-8al98ya2.txt
cache: ./cache/cord-332618-8al98ya2.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920306937?v=s5
doi: 10.1016/j.chaos.2020.110297
summary:
  We propose a functional form of birth rate that depends on the number of individuals in the population and on the elapsed time, allowing us to model a contagion effect. Hence, 35 we propose a different model based on a Pure Birth process with an event rate that, like Polya''s, depends on both the elapsed time and the number of previous events, but with a different functional form. Our main motivation is to obtain a model that describes an epidemic outbreak at its first stage, before it reaches the inflection point in the case incidence curve, which is useful to monitor how contagion is spreading out. Since the mean value function of the Polya-Lundberg process is a linear function of time (see Appendix B), we introduce a modification in the event rate in order to get a mean value function that grows 85 subexponentially with either positive or negative concavity as we observe in the early epidemic growth curves usually reported.

title:
  A non-homogeneous Markov early epidemic growth dynamics model. Application to the SARS-CoV-2 pandemic

abstract:
  This work introduces a new markovian stochastic model that can be described as a non-homogeneous Pure Birth process. We propose a functional form of birth rate that depends on the number of individuals in the population and on the elapsed time, allowing us to model a contagion effect. Thus, we model the early stages of an epidemic. The number of individuals then becomes the infectious cases and the birth rate becomes the incidence rate. We obtain this way a process that depends on two competitive phenomena, infection and immunization. Variations in those rates allow us to monitor how effective the actions taken by government and health organizations are. From our model, three useful indicators for the epidemic evolution over time are obtained: the immunization rate, the infection/immunization ratio and the mean time between infections (MTBI). The proposed model allows either positive or negative concavities for the mean value curve, provided the infection/immunization ratio is either greater or less than one. We apply this model to the present SARS-CoV-2 pandemic still in its early growth stage in Latin American countries. As it is shown, the model accomplishes a good fit for the real number of both positive cases and deaths. We analyze the evolution of the three indicators for several countries and perform a comparative study between them. Important conclusions are obtained from this analysis.


id: cord-333162-gwmvsoru
author: Malki, Zohair
date: 2020-07-17
txt: ./txt/cord-333162-gwmvsoru.txt
cache: ./cache/cord-333162-gwmvsoru.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920305336?v=s5
doi: 10.1016/j.chaos.2020.110137
summary:
  title: Association between Weather Data and COVID-19 Pandemic Predicting Mortality Rate: Machine Learning Approaches In this work, various regressor machine learning models are proposed to extract the relationship between different factors and the spreading rate of COVID-19. The machine learning algorithms employed in this work estimate the impact of weather variables such as temperature and humidity on the transmission of COVID-19 by extracting the relationship between the number of confirmed cases and the weather variables on certain regions. Thus, from this result, we can conclude that temperature and humidity are important features for predicting COVID-19 mortality rate. For Italy, regions 33 with a temperature higher than 15 degrees Celsius and 34 75% humidity have less spread of COVID-19 cases. Temperature and latitude 554 analysis to predict potential spread and seasonality for COVID-555 19 Temperature, population and longitu-571 dinal analysis to predict potential spread for COVID-19

title:
  Association between Weather Data and COVID-19 Pandemic Predicting Mortality Rate: Machine Learning Approaches

abstract:
  Nowadays, a significant number of infectious diseases such as human coronavirus disease (COVID-19) are threatening the world by spreading at an alarming rate. Some of the literatures pointed out that the pandemic is exhibiting seasonal patterns in its spread, incidence and nature of the distribution. In connection to the spread and distribution of the infection, scientific analysis that answers the questions whether the next summer can save people from COVID-19 is required. Many researchers have been exclusively asked whether high temperature during summer can slow down the spread of the COVID-19 as it has with other seasonal flues. Since there are a lot of questions that are unanswered right now, and many mysteries aspects about the COVID-19 that is still unknown to us, in-depth study and analysis of associated weather features are required. Moreover, understanding the nature of COVID-19 and forecasting the spread of COVID-19 request more investigation of the real effect of weather variables on the transmission of the COVID-19 among people. In this work, various regressor machine learning models are proposed to extract the relationship between different factors and the spreading rate of COVID-19. The machine learning algorithms employed in this work estimate the impact of weather variables such as temperature and humidity on the transmission of COVID-19 by extracting the relationship between the number of confirmed cases and the weather variables on certain regions. To validate the proposed method, we have collected the required datasets related to weather and census features and necessary prepossessing is carried out. From the experimental results, it is shown that the weather variables are more relevant in predicting the mortality rate when compared to the other census variables such as population, age, and urbanization. Thus, from this result, we can conclude that temperature and humidity are important features for predicting COVID-19 mortality rate. Moreover, it is indicated that the higher the value of temperature the lower number of infection cases.


id: cord-334413-h6n36jei
title: A Novel CFD Analysis to Minimize the Spread of COVID-19 Virus in Hospital Isolation Room
author: Bhattacharyya, Suvanjan
date: 2020-09-17
txt: ./txt/cord-334413-h6n36jei.txt
cache: ./cache/cord-334413-h6n36jei.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920306901?v=s5
doi: 10.1016/j.chaos.2020.110294
summary:
  Present study investigates the effectiveness of conditioned air released from air-conditioning machines to mix with aerosol sanitizer to reach every point of the space of the isolation room so as to kill the COVID-19 virus which will help to protect the lives of doctors, nurses and health care workers. It is found from the analysis that high turbulent fields generated inside the isolation room may be an effective way of distributing sanitizer in entire volume of isolation room to kill the COVID-19 virus. As the medical treatments are often inaccurate, besides precautionary measures and supports, it is therefore reasonable to investigate the possibilities to sanitize the confined volume of air to mitigate the spread of COVID-19 virus inside the airborne infection isolation rooms, and ICUs of a hospital. The study has been carried out to investigate the effectiveness of conditioned air released from air-conditioning machines to mix with aerosol sanitizer so as to reach every corner of the isolation room and kill the COVID-19 virus.

abstract:
  The COVID-19 is a severe respiratory disease caused by a devastating coronavirus family (2019-nCoV) has become a pandemic across the globe. It is an infectious virus and transmits by inhalation or contact with droplet nuclei produced during sneezing, coughing, and speaking by infected people. Airborne transmission of COVID-19 is also possible in a confined place in the immediate environment of the infected person. Present study investigates the effectiveness of conditioned air released from air-conditioning machines to mix with aerosol sanitizer to reach every point of the space of the isolation room so as to kill the COVID-19 virus which will help to protect the lives of doctors, nurses and health care workers. In order to numerically model the laminar-transitional flows, transition SST k-ε model, which involves four transport equations are employed in the current study. It is found from the analysis that high turbulent fields generated inside the isolation room may be an effective way of distributing sanitizer in entire volume of isolation room to kill the COVID-19 virus.


id: cord-337256-b3j3kg73
title: Prediction of Epidemic Trends in COVID-19 with Logistic Model and Machine Learning Technics
author: Wang, Peipei
date: 2020-07-01
txt: ./txt/cord-337256-b3j3kg73.txt
cache: ./cache/cord-337256-b3j3kg73.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077920304550
doi: 10.1016/j.chaos.2020.110058
summary:
  title: Prediction of Epidemic Trends in COVID-19 with Logistic Model and Machine Learning Technics We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into Fbprophet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Many scholars have developed a number of predicting methods for the trend forecasting of COVID-19, in some severe countries and global [8, 9] , debating 30 about mathematical model, infectious disease model, and artificial intelligence model. The models based on mathematical statistics, machine learning and deep learning have been applied to the prediction of time series of epidemic development [10, 11] . Generalized logistic growth modeling of the covid-19 outbreak in 29 provinces in china and in the rest of the world

abstract:
  COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, itâs not known when the epidemic will end in global and various countries. Predicting the trend of COVID-19 is an extremely important challenge. We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into Fbprophet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Three significant points are summarized from our modeling results for global, Brazil, Russia, India, Peru and Indonesia. Under mathematical estimation, the global outbreak will peak in late October, with an estimated 14.12 million people infected cumulatively.


id: cord-337275-phgfpzbt
title: Is Spread of COVID-19 a Chaotic Epidemic?
author: Andrew, Jones
date: 2020-10-20
txt: ./txt/cord-337275-phgfpzbt.txt
cache: ./cache/cord-337275-phgfpzbt.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.chaos.2020.110376
doi: 10.1016/j.chaos.2020.110376
summary:
  Traditional compartmental epidemiological models demonstrated limited ability to predict the scale and dynamics of COVID-19 epidemic in different countries. Our mathematical examination of COVID-19 epidemic data in different countries reveals similarity of this dynamic to the chaotic behavior of many dynamics systems, such as logistic maps. In a previous study, [4] demonstrated that the coronavirus raw data in China''s first two months of the disease suggest chaotic growth, similar to other epidemics like H1N1 and measles. These systems are now termed "chaotic." Unpredictability due to highly-sensitive reliance on initial conditions inspired the term "deterministic chaos." After Poincaré''s studies, the deterministic chaotic behavior was discovered in numerous dynamical systems and confirmed experimentally [15, 6, 2, 20] . Through use of an interactive data map, it was shown that the spread of COVID-19 exhibits the major characteristics of chaotic systems, namely, determinism, high sensitivity, large number of equilibria, and unpredictability.

abstract:
  The COVID-19 epidemic challenges humanity in 2020. It has already taken an enormous number of human lives and had a substantial negative economic impact. Traditional compartmental epidemiological models demonstrated limited ability to predict the scale and dynamics of COVID-19 epidemic in different countries. In order to gain a deeper understanding of its behavior, we turn to chaotic dynamics, which proved fruitful in analyzing previous diseases such as measles. We hypothesize that the unpredictability of the pandemic could be a fundamental property if the disease spread is a chaotic dynamical system. Our mathematical examination of COVID-19 epidemic data in different countries reveals similarity of this dynamic to the chaotic behavior of many dynamics systems, such as logistic maps. We conclude that the data does suggest that the COVID-19 epidemic demonstrates chaotic behavior, which should be taken into account by public policy makers. Furthermore, the scale and behavior of the epidemic may be essentially unpredictable due to the properties of chaotic systems, rather than due to the limited data available for model parameterization.


id: cord-337760-joti9nwg
author: Buldú, Javier M.
date: 2020-06-04
txt: ./txt/cord-337760-joti9nwg.txt
cache: ./cache/cord-337760-joti9nwg.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920303635?v=s5
doi: 10.1016/j.chaos.2020.109964
summary:
  Our results highlight the influence of the days between matches, the frequency of virus tests and their sensitivity on the number of players infected at the end of the season. The model, whose main parameters were based on the scientific literature concerning the infection and recovery periods of COVID-19, could be easily adapted to describe other kinds of sports competitions just by modifying the number of players and teams participating in the tournament. Table 1: Summary of the main parameters used in the model: Probability of being infected during the training period β train , during a match β match and from the player''s social circle β ext ; latent period σ −1 , infectious period γ −1 and quarantine period γ −1 Q ; probability of being detected as exposed (by virus test) µ E and as infectious (by virus test or by symptoms) µ I ; number of days between virus tests N test and matches N match .

title:
  The resumption of sports competitions after COVID-19 lockdown: The case of the Spanish football league

abstract:
  In this work, we present a stochastic discrete-time SEIR Susceptible-Exposed-Infectious-Recoveredmodel adapted to describe the propagation of COVID-19 during a football tournament. Specifically, we are concerned about the re-start of the Spanish national football league, La Liga, which is currently –May 2020– stopped with 11 fixtures remaining. Our model includes two additional states of an individual, confined and quarantined, which are reached when an individual presents COVID-19 symptoms or has undergone a virus test with a positive result. The model also accounts for the interaction dynamics of players, considering three different sources of infection: the player social circle, the contact with his/her team colleagues during training sessions, and the interaction with rivals during a match. Our results highlight the influence of the days between matches, the frequency of virus tests and their sensitivity on the number of players infected at the end of the season. Following our findings, we finally propose a variety of strategies to minimize the probability that COVID-19 propagates in case the season of La Liga was re-started after the current lockdown.


id: cord-342591-6joc2ld1
title: Novel Fractional Order SIDARTHE Mathematical Model of The COVID-19 Pandemic
author: Higazy, M.
date: 2020-06-13
txt: ./txt/cord-342591-6joc2ld1.txt
cache: ./cache/cord-342591-6joc2ld1.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32565624/
doi: 10.1016/j.chaos.2020.110007
summary:
  The existence of a stable solution of the fractional order COVID-19 SIDARTHE model is proved and the fractional order necessary conditions of four proposed control strategies are produced. In addition, we study an optimal control plans for the fractional order SIDARTHE model via four control strategies that include the availability of vaccination and existence of treatments for the infected detected three population fraction phases. Applying the fractional order differential equations numerical solver using MATLAB software, we show the dynamics of the state variables of the model and display the effect of changing the fractional derivative order on the system response. We also implement the optimal control strategies numerically for the fractional order SIDARTHE model. Figure 9 displays the phase plane of state variables: total infected ( ) and susceptible cases (S(t)) with different fractional derivative order .

abstract:
  Nowadays, COVID-19 has put a significant responsibility on all of us around the world from its detection to its remediation. The globe suffer from lockdown due to COVID-19 pandemic. The researchers are doing their best to discover the nature of this pandemic and try to produce the possible plans to control it. One of the most effective method to understand and control the evolution of this pandemic is to model it via an efficient mathematical model. In this paper, we propose to model the COVID-19 pandemic by fractional order SIDARTHE model which not appear in the literature before. The existence of a stable solution of the fractional order COVID-19 SIDARTHE model is proved and the fractional order necessary conditions of four proposed control strategies are produced. The sensitivity of the fractional order COVID-19 SIDARTHE model to the fractional order and the infection rate parameters are displayed. All studies are numerically simulated using MATLAB software via fractional order differential equation solver.


id: cord-342955-vf3c6ksm
author: Lu, Jingjing
date: 2020-06-21
txt: ./txt/cord-342955-vf3c6ksm.txt
cache: ./cache/cord-342955-vf3c6ksm.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834590/
doi: 10.1016/j.chaos.2020.110024
summary:
  The model is described by a mixed system of ordinary and partial differential equations which is constituted by the within-host virus infectious fast time ordinary system and the between-host disease transmission slow time age-structured system. For the isolated slow system, the basic reproduction number R(b0), the positivity and ultimate boundedness of solutions are obtained, the existence of equilibria, the local stability of equilibria, and the global stability of disease-free equilibrium are established. Lemma 7 shows that due to the feedback effect of virus in the environment on the virus infection in the host, the coupled between-host disease transmission slow time system produces two positive equilibrium when the basic reproduction number R b < 1. Theorem 6 shows that when the basic reproduction number R b < 1, because the coupled slow system may have two positive equilibrium at this time, the infectious disease will be extinct only when the number of infected individuals is relatively small.

title:
  An age-structured model for coupling within-host and between-host dynamics in environmentally-driven infectious diseases

abstract:
  In this paper, an age-structured epidemic model for coupling within-host and between-host dynamics in environmentally-driven infectious diseases is investigated. The model is described by a mixed system of ordinary and partial differential equations which is constituted by the within-host virus infectious fast time ordinary system and the between-host disease transmission slow time age-structured system. The isolated fast system has been investigated in previous literatures, and the main results are introduced. For the isolated slow system, the basic reproduction number R(b0), the positivity and ultimate boundedness of solutions are obtained, the existence of equilibria, the local stability of equilibria, and the global stability of disease-free equilibrium are established. We see that when R(b0) ≤ 1 the system only has the disease-free equilibrium which is globally asymptotically stable, and when R(b0) > 1 the system has a unique endemic equilibrium which is local asymptotically stable. With regard to the coupled slow system, the basic reproduction number R(b), the positivity and boundedness of solutions and the existence of equilibria are firstly obtained. Particularly, the coupled slow system can exist two positive equilibria when R(b) < 1 and a unique endemic equilibrium when R(b) > 1. When R(b) < 1 the disease-free equilibrium is local asymptotically stable, and when R(b) > 1 and an additional condition is satisfied the unique endemic equilibrium is local asymptotically stable. When there exist two positive equilibria, under an additional condition the local asymptotic stability of a positive equilibrium and the instability of other positive equilibrium also are established. The numerical examples show that the additional condition may be removed. The research shows that the coupled slow age-structured system has more complex dynamical behavior than the corresponding isolated slow system.


id: cord-344252-6g3zzj0o
title: A Novel Adaptive Deep Learning Model of Covid-19 with focus on mortality reduction strategies
author: Farooq, Junaid
date: 2020-07-21
txt: ./txt/cord-344252-6g3zzj0o.txt
cache: ./cache/cord-344252-6g3zzj0o.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.chaos.2020.110148
doi: 10.1016/j.chaos.2020.110148
summary:
  We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. In this work, we employ deep learning to propose an Artificial Neural Network (ANN) based real-time online incremental learning technique to estimate parameters of a data stream guided analytical model of Covid-19 to study the transmission dynamics and prevention mechanism for SARS-Cov-2 novel coronavirus in order to aid in optimal policy formulation, efficient decision making, forecasting and simulation. To the best of our knowledge, this paper develops for the first time a deep learning model of epidemic diseases with data science approach in which parameters are intelligently adapted to the new ground realities with fast evolving infection dynamics.

abstract:
  We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. Modeling and simulation of such problems poses an additional challenge of continuously evolving training data in which the model parameters change over time depending upon external factors. Our main contribution is that in a scenario of continuously evolving training data, unlike typical deep learning techniques, this non-intrusive model eliminates the need to retrain or rebuild the model from scratch every time a new training data set is received. After validating the model, we use it to study the impact of different strategies for epidemic control. Finally, we propose and simulate a strategy of controlled natural immunization through risk based population compartmentalization (PC) wherein the population is divided in Low Risk (LR) and High Risk (HR) compartments based on risk factors (like comorbidities and age) and subjected to different disease transmission dynamics by isolating the HR compartment while allowing the LR compartment to develop natural immunity. Upon release from the preventive isolation, the HR compartment finds itself surrounded by enough number of immunized individuals to prevent spread of infection and thus most of the deaths occurring in this group are avoided.


id: cord-346185-qmu1mrmx
title: Forecast and evaluation of COVID-19 spreading in USA with Reduced-space Gaussian process regression
author: Velásquez, Ricardo Manuel Arias
date: 2020-05-22
txt: ./txt/cord-346185-qmu1mrmx.txt
cache: ./cache/cord-346185-qmu1mrmx.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32501372/
doi: 10.1016/j.chaos.2020.109924
summary:
  title: Forecast and evaluation of COVID-19 spreading in USA with Reduced-space Gaussian process regression In this report, we analyze historical and forecast infections for COVID-19 death based on Reduced-Space Gaussian Process Regression associated to chaotic Dynamical Systems with information obtained in 82 days with continuous learning, day by day, from January 21(th), 2020 to April 12(th). According last results, COVID-19 could be predicted with Gaussian models mean-field models can be meaningfully used to gather a quantitative picture of the epidemic spreading, with infections, fatality and recovery rate. able on the Center for Systems Science and Engineering at Johns Hopkins University [6] , the available data analyzed is considered between January 21 th 2020 and April 39 12 th 2020, included, with a feedback process in a neural network applied; it allows 40 to examined the information in real time in each state, at Fig. 1 • .

abstract:
  In this report, we analyze historical and forecast infections for COVID-19 death based on Reduced-Space Gaussian Process Regression associated to chaotic Dynamical Systems with information obtained in 82 days with continuous learning, day by day, from January 21(th), 2020 to April 12(th). According last results, COVID-19 could be predicted with Gaussian models mean-field models can be meaning- fully used to gather a quantitative picture of the epidemic spreading, with infections, fatality and recovery rate. The forecast places the peak in USA around July 14(th) 2020, with a peak number of 132,074 death with infected individuals of about 1,157,796 and a number of deaths at the end of the epidemics of about 132,800. Late on January, USA confirmed the first patient with COVID-19, who had recently traveled to China, however, an evaluation of states in USA have demonstrated a fatality rate in China (4%) is lower than New York (4.56%), but lower than Michigan (5.69%). Mean estimates and uncertainty bounds for both USA and his cities and other provinces have increased in the last three months, with focus on New York, New Jersey, Michigan, California, Massachusetts,... (January e April 12(th)). Besides, we propose a Reduced-Space Gaussian Process Regression model predicts that the epidemic will reach saturation in USA on July 2020. Our findings suggest, new quarantine actions with more restrictions for containment strategies implemented in USA could be successfully, but in a late period, it could generate critical rate infections and death for the next 2 month.


id: cord-349841-eigcqb1b
title: Dynamics of a stochastic coronavirus (COVID-19) epidemic model with Markovian switching
author: Boukanjime, Brahim
date: 2020-10-16
txt: ./txt/cord-349841-eigcqb1b.txt
cache: ./cache/cord-349841-eigcqb1b.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33100608/
doi: 10.1016/j.chaos.2020.110361
summary:
  title: Dynamics of a stochastic coronavirus (COVID-19) epidemic model with Markovian switching In this paper, we analyze a stochastic coronavirus (COVID-19) epidemic model which is perturbed by both white noise and telegraph noise incorporating general incidence rate. In fact, the COVID-19 epidemic model is unavoidably subjected to the environmental noise, which made the parameters involved in the system often fluctuate randomly around some average values as the surrounding environment fluctuation. In this paper, we propose a stochastic COVID-19 model adopting a generalized incidence function [25, 26] as follows: Note that the COVID-19 epidemic models may be perturbed by telegraph noise which can causes the system to switch from one environmental regime to another [22] . To study the dynamical behaviour of an epidemic model, we firstly need to consider whether the solution is global and positive. This paper investigates a stochastic epidemic model describing COVID-19 dynamics affected 125 by mixture of environmental perturbations modeled by white and telegraph noises.

abstract:
  In this paper, we analyze a stochastic coronavirus (COVID-19) epidemic model which is perturbed by both white noise and telegraph noise incorporating general incidence rate. Firstly, we investigate the existence and uniqueness of a global positive solution. Then, we establish the stochastic threshold for the extinction and the persistence of the disease. The data from Indian states, are used to confirm the results established along this paper.


id: cord-351343-zdh8ms1z
title: STATIONARY DISTRIBUTION AND EXTINCTION OF STOCHASTIC CORONAVIRUS (COVID-19) EPIDEMIC MODEL
author: Din, Anwarud
date: 2020-06-24
txt: ./txt/cord-351343-zdh8ms1z.txt
cache: ./cache/cord-351343-zdh8ms1z.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920304343?v=s5
doi: 10.1016/j.chaos.2020.110036
summary:
  The most basic stochastic epidemic models are those involving global transmission, meaning that infection rates depend only on the type and state of the individuals involved, and not on their location in the population. In the recent study, we proposed a stochastic epidemic model for the transmission dynamics of the COVID-19 with a changing environment considering long term behavior. The present section is devoted to formulation of a model based on stochastic theory for studying the transmissions dynamic of the novel virus i.e., COVID-19 pandemic. This section is about studying the existence and uniqueness of solution of the proposed stochastic COVID-19 model (1). Proof: To prove the theorem, we shall apply direct integration to the proposed stochastic COVID-19 model (1) . With the help of stochastic theory, we developed a model for the novel COVID-19 keeping in view the characteristic of the disease to investigate the transmission dynamics with changing population environment.

abstract:
  Similar to other epidemics, the novel coronavirus (COVID-19) spread very fast and infected almost two hundreds countries around the globe since December 2019. The unique characteristics of the COVID-19 include its ability of faster expansion through freely existed viruses or air molecules in the atmosphere. Assuming that the spread of virus follows a random process instead of deterministic. The continuous time Markov Chain (CTMC) through stochastic model approach has been utilized for predicting the impending states with the use of random variables. The proposed study is devoted to investigate a model consist of three exclusive compartments. The first class includes white nose based transmission rate (termed as susceptible individuals), the second one pertains to the infected population having the same perturbation occurrence and the last one isolated (quarantined) individuals. We discuss the model’s extinction as well as the stationary distribution in order to derive the the sufficient criterion for the persistence and disease’ extinction. Lastly, the numerical simulation is executed for supporting the theoretical findings.


id: cord-352990-0uglwvid
author: Nadim, Sk Shahid
date: 2020-08-17
txt: ./txt/cord-352990-0uglwvid.txt
cache: ./cache/cord-352990-0uglwvid.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.chaos.2020.110163
doi: 10.1016/j.chaos.2020.110163
summary:
  title: Occurrence of backward bifurcation and prediction of disease transmission with imperfect lockdown: A case study on COVID-19 In this case, for imperfect lockdown, the basic reproduction number does not rep-90 resent the required elimination effort; rather, the effort at the turning point is described The paper is organized as follows: Our proposed mathematical model which incorporates 108 the lockdown of susceptible individuals and imperfect lockdown efficacy is described in 109 Section 2. backward bifurcation phenomenon, where two stable equilibria, namely the disease-free 387 equilibrium and an endemic equilibrium coexist when the corresponding basic number 388 of reproduction is less than unity. We have 394 seen that the disease-free equilibrium is globally asymptotically stable whenever the as-395 sociated basic reproduction number is less than unity for the perfect lockdown model.

title:
  Occurrence of backward bifurcation and prediction of disease transmission with imperfect lockdown: A case study on COVID-19

abstract:
  The outbreak of COVID-19 caused by SARS-CoV-2 is spreading rapidly around the world, which is causing a major public health concerns. The outbreaks started in India on March 2, 2020. As of April 30, 2020, 34,864 confirmed cases and 1,154 deaths are reported in India and more than 30,90,445 confirmed cases and 2,17,769 deaths are reported worldwide. Mathematical models may help to explore the transmission dynamics, prediction and control of COVID-19 in the absence of an appropriate medication or vaccine. In this study, we consider a mathematical model on COVID-19 transmission with the imperfect lockdown effect. The basic reproduction number, R(0), is calculated using the next generation matrix method. The system has a disease-free equilibrium (DFE) which is locally asymptotically stable whenever R(0) < 1. Moreover, the model exhibits the backward bifurcation phenomenon, where the stable DFE coexists with a stable endemic equilibrium when R(0) < 1. The epidemiological implications of this phenomenon is that the classical epidemiological requirement of making R(0) less than unity is only a necessary, but not sufficient for effectively controlling the spread of COVID-19 outbreak. It is observed that the system undergoes backward bifurcation which is a new observation for COVID-19 disease transmission model. We also noticed that under the perfect lockdown scenario, there is no possibility of having backward bifurcation. Using Lyapunov function theory and LaSalle Invariance Principle, the DFE is shown globally asymptotically stable for perfect lockdown model. We have calibrated our proposed model parameters to fit daily data from India, Mexico, South Africa and Argentina. We have provided a short-term prediction for India, Mexico, South Africa and Argentina of future cases of COVID-19. We calculate the basic reproduction number from the estimated parameters. We further assess the impact of lockdown during the outbreak. Furthermore, we find that effective lockdown is very necessary to reduce the burden of diseases.


id: cord-353306-hwwswvi3
author: Zhu, Bangren
date: 2020-07-17
txt: ./txt/cord-353306-hwwswvi3.txt
cache: ./cache/cord-353306-hwwswvi3.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32834635/
doi: 10.1016/j.chaos.2020.110123
summary:
  COVID-19 blocked Wuhan in China, which was sealed off on Chinese New Year''s Eve. During this period, the research on the relevant topics of COVID-19 and emotional expressions published on social media can provide decision support for the management and control of large-scale public health events. The research assisted the analysis of microblog text topics with the help of the LDA model, and obtained 8 topics ("origin", "host", "organization", "quarantine measures", "role models", "education", "economic", "rumor") and 28 interactive topics. At the same time, the discussion rate of epidemic topics gradually weakens; (3) The political and economic center is an area where social media is highly concerned. The spatial division of the number of Weibo social media texts has a high correlation with the economic zone division; (4) The existence of the topic of "rumor" will enable people to have more communication and discussion.

title:
  Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics

abstract:
  COVID-19 blocked Wuhan in China, which was sealed off on Chinese New Year's Eve. During this period, the research on the relevant topics of COVID-19 and emotional expressions published on social media can provide decision support for the management and control of large-scale public health events. The research assisted the analysis of microblog text topics with the help of the LDA model, and obtained 8 topics (“origin”, “host”, “organization”, “quarantine measures”, “role models”, “education”, “economic”, “rumor”) and 28 interactive topics. Obtain data through crawler tools, with the help of big data technology, social media topics and emotional change characteristics are analyzed from spatiotemporal perspectives. The results show that: (1) “Double peaks” feature appears in the epidemic topic search curve. Weibo on the topic of the epidemic gradually reduced after January 24. However, the proportion of epidemic topic searches has gradually increased, and a “double peaks” phenomenon appeared within a week; (2) The topic changes with time and the fluctuation of the topic discussion rate gradually weakens. The number of texts on different topics and interactive topics changes with time. At the same time, the discussion rate of epidemic topics gradually weakens; (3) The political and economic center is an area where social media is highly concerned. The areas formed by Beijing, Shanghai, Guangdong, Sichuan and Hubei have published more microblog texts. The spatial division of the number of Weibo social media texts has a high correlation with the economic zone division; (4) The existence of the topic of “rumor” will enable people to have more communication and discussion. The interactive topics of “rumors” always have higher topic popularity and low emotion text expressions. Through the analysis of media information, it helps relevant decision makers to grasp social media topics from spatiotemporal characteristics, so that relevant departments can accurately grasp the public's subjective ideas and emotional expressions, and provide decision support for macro-control response strategies and measures and risk communication.


id: cord-354792-6ckgxn9l
author: Ghosh, Mousam
date: 2020-10-19
txt: ./txt/cord-354792-6ckgxn9l.txt
cache: ./cache/cord-354792-6ckgxn9l.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920307712?v=s5
doi: 10.1016/j.chaos.2020.110377
summary:
  title: Dynamic Model of Infected Population Due to Spreading of Pandemic COVID-19 Considering Both Intra and Inter Zone Mobilization Factors with Rate of Detection In this paper a dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection has been proposed. In view of these, a dynamic model to predict the pattern and volume of infected population due to the spread of COVID-19 has been proposed in the present paper considering several real life factors such as intra and inter zone mobilization, lockdown on local and global activities before detection, rate of detection and the effects of quarantine after detection. In this paper a dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection, have been proposed with various operating procedures.

title:
  Dynamic Model of Infected Population Due to Spreading of Pandemic COVID-19 Considering Both Intra and Inter Zone Mobilization Factors with Rate of Detection

abstract:
  Most of the widely populated countries across the globe have been observing vicious spread and detrimental effects of pandemic COVID-19 since its inception on December 19. Therefore to restrict the spreading of pandemic COVID-19, various researches are going on in both medical and administrative sectors. The focus has been given in this research keeping an administrative point of view in mind. In this paper a dynamic model of infected population due to spreading of pandemic COVID-19 considering both intra and inter zone mobilization factors with rate of detection has been proposed. Few factors related to intra zone mobilization; inter zone mobilization and rate of detection are the key points in the proposed model. Various remedial steps are taken into consideration in the form of operating procedures. Further such operating procedures are applied over the model in standalone or hybridized mode and responses are reported in this paper in a case-studies manner. Further zone-wise increase in infected population due to the spreading of pandemic COVID-19 has been studied and reported in this paper. Also the proposed model has been applied over the real world data considering three states of India and the predicted responses are compared with real data and reported with bar chart representation in this paper.


id: cord-355267-ndzgxk0k
title: Analysis of the mitigation strategies for COVID-19: from mathematical modelling perspective
author: Kassa, Semu M.
date: 2020-06-05
txt: ./txt/cord-355267-ndzgxk0k.txt
cache: ./cache/cord-355267-ndzgxk0k.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32536760/
doi: 10.1016/j.chaos.2020.109968
summary:
  Whereas knowledge of the virus dynamics and host response are essential for formulating strategies for antiviral treatment, vaccination, and epidemiological control of COVID-19, estimation of changes in transmission over time can provide insights into the epidemiological situation and help to identify whether public health control measures are having a measurable effect [5, 39] . Applying the above described set of assumptions in the bounds for some of the parameters, we optimized the model output to fit the daily new cases data reported from the Hubei province, China. Analysis of the mitigation strategies for COVID-19 Figure 11 : Dynamics of the disease with at most 10% of the population in the class and at least 50% of the class are detected and quarantined just after Phase 1 period, with strict social distancing rule imposed for 11 weeks.

abstract:
  In this article, a mathematical model for the transmission of COVID-19 disease is formulated and analysed. It is shown that the model exhibits a backward bifurcation at [Formula: see text] when recovered individuals do not develop a permanent immunity for the disease. In the absence of reinfection, it is proved that the model is without backward bifurcation and the disease free equilibrium is globally asymptotically stable for [Formula: see text]. By using available data, the model is validated and parameter values are estimated. The sensitivity of the value of [Formula: see text] to changes in any of the parameter values involved in its formula is analysed. Moreover, various mitigation strategies are investigated using the proposed model and it is observed that the asymptomatic infectious group of individuals may play the major role in the re-emergence of the disease in the future. Therefore, it is recommended that in the absence of vaccination, countries need to develop capacities to detect and isolate at least 30% of the asymptomatic infectious group of individuals while treating in isolation at least 50% of symptomatic patients to control the disease.


id: cord-355419-8txtk0b3
title: Epidemic in networked population with recurrent mobility pattern
author: Feng, Liang
date: 2020-06-25
txt: ./txt/cord-355419-8txtk0b3.txt
cache: ./cache/cord-355419-8txtk0b3.txt
extension: .txt
mime: text/plain
url: https://www.sciencedirect.com/science/article/pii/S0960077920304148
doi: 10.1016/j.chaos.2020.110016
summary:
  In this paper, we utilize a discrete-time Markov chain approach and propose an epidemic model to describe virus propagation in the heterogeneous graph, which is used to represent individuals with intra social connections and mobility between individuals and common locations. Different from commonly used homogeneous mixing approaches [2, 3] , we give an analysis of epidemic spreading in population following a structured network with recurrent mobility pattern in this work. One widely used approach to analyse epidemic spreading in complex networks is metapopulation model, which divides the whole population into several geographical structured parts [13, 18] , and contacts among individuals in the same subpopulation are assumed to be well-mixed. In Section 2 , we give the formulation of epidemic model for virus spreading in networked population with recurrent mobility pattern, along with theoretical results of epidemic threshold. We formulate an epidemic model of virus propagating in networked population with recurrent mobility pattern between individuals and public areas.

abstract:
  The novel Coronavirus (COVID-19) has caused a global crisis and many governments have taken social measures, such as home quarantine and maintaining social distance. Many recent studies show that network structure and human mobility greatly influence the dynamics of epidemic spreading. In this paper, we utilize a discrete-time Markov chain approach and propose an epidemic model to describe virus propagation in the heterogeneous graph, which is used to represent individuals with intra social connections and mobility between individuals and common locations. There are two types of nodes, individuals and public places, and disease can spread by social contacts among individuals and people gathering in common areas. We give theoretical results about epidemic threshold and influence of isolation factor. Several numerical simulations are performed and experimental results further demonstrate the correctness of proposed model. Non-monotonic relationship between mobility possibility and epidemic threshold and differences between Erdös-Rényi and power-law social connections are revealed. In summary, our proposed approach and findings are helpful to analyse and prevent the epidemic spreading in networked population with recurrent mobility pattern.


id: cord-355689-mo4mvwch
title: Role of vaccine efficacy in the vaccination behavior under myopic update rule on complex networks
author: Huang, Jiechen
date: 2019-09-06
txt: ./txt/cord-355689-mo4mvwch.txt
cache: ./cache/cord-355689-mo4mvwch.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0960077919303662
doi: 10.1016/j.chaos.2019.109425
summary:
  The results indicate that healthy individuals are often willing to inoculate the vaccine under the myopic update rule, which can stop the infectious disease from being spread, in particular, it is found that the vaccine efficacy influences the fraction of vaccinated individuals much more than the relative cost of vaccination on the regular lattice, Meanwhile, vaccine efficacy is more sensitive on the heterogeneous scale-free network. On the one hand, they classify these models according to source and type of information that individuals base their neighbors on, in which source of information may be local or global and the type of information that individuals change their behaviors are prevalence-based or belief-based; On the other hand, they classify the previous works based on the impact of individual behavior changes on the disease dynamics, which include the following three aspects: (i) the disease state; (ii) model parameters (infection or recovering rate); and (iii) the network contact structure relevant for the spread of epidemics.

abstract:
  How to effectively prevent the diffusion of infectious disease has become an intriguing topic in the field of public hygienics. To be noted that, for the non-periodic infectious diseases, many people hope to obtain the vaccine of epidemics in time to be inoculated, rather than at the end of the epidemic. However, the vaccine may fail as a result of invalid storage, transportation and usage, and then vaccinated individuals may become re-susceptible and be infected again during the outbreak. To this end, we build a new framework that considers the imperfect vaccination during the one cycle of infectious disease within the spatially structured and heterogeneous population. Meanwhile, we propose a new vaccination update rule: myopic update rule, which is only based on one focal player’s own perception regarding the disease outbreak, and one susceptible individual makes a decision to adopt the vaccine just by comparing the perceived payoffs vaccination with the perceived ones of being infected. Extensive Monte-Carlo simulations are performed to demonstrate the imperfect vaccination behavior under the myopic update rule in the spatially structured and heterogeneous population. The results indicate that healthy individuals are often willing to inoculate the vaccine under the myopic update rule, which can stop the infectious disease from being spread, in particular, it is found that the vaccine efficacy influences the fraction of vaccinated individuals much more than the relative cost of vaccination on the regular lattice, Meanwhile, vaccine efficacy is more sensitive on the heterogeneous scale-free network. Current results are helpful to further analyze and model the choice of vaccination strategy during the disease outbreaks.


