cord-001071-bjx5td52	2013	The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. In particular, wearable sensors based on active Radio-Frequency IDentification (RFID) technology have been used to measure face-to-face proximity relations between individuals with a high spatio-temporal resolution in various contexts [17] that include social gatherings [18, 19] , schools [20, 21] and hospitals [22, 23] . In this paper we report on the use of wearable proximity sensors [17] to measure the numbers and durations of contacts between individuals in an acute care geriatric unit of a university hospital.
cord-004615-xfi3p601	2004	
cord-009797-8mdie73v	2018	title: Extending the Latent Dirichlet Allocation model to presence/absence data: A case study on North American breeding birds and biogeographical shifts expected from climate change The Latent Dirichlet Allocation (LDA) model is a mixed‐membership method that can represent gradual changes in community structure by delineating overlapping groups of species, but its use has been limited because it requires abundance data and requires users to a priori set the number of groups. Furthermore, by comparing the estimated proportion of each group for two time periods (1997–2002 and 2010–2015), our results indicate that nine (of 18) breeding bird groups exhibited an expansion northward and contraction southward of their ranges, revealing subtle but important community‐level biodiversity changes at a continental scale that are consistent with those expected under climate change. It is important to note that even in the absence of MM sampling units, LDA can still estimate well the true number of groups and has similar fit to the data as the other clustering approaches (results not shown).
cord-012511-fl5llkoj	2015	We were tasked to evaluate the 6 following interventions: invasive mechanical ventilators, influenza antiviral drugs for treatment (but not large-scale prophylaxis), influenza vaccines, respiratory protective devices for healthcare workers and surgical face masks for patients, school closings to reduce transmission, and airport-based screening to identify those ill with novel influenza virus entering the United States. To allow easy comparison between results (a specification), we standardized a risk space defined by using ranges of transmission and clinical severity from a previously published influenza severity assessment framework ( Figure 1 ) [5] . Standardized epidemiological curves-contact matrix: To model the 4 epidemic curves (Figure 2 ), we built a simple, nonprobabilistic (ie, deterministic) model in which we divided the population into 4 age groups (0-10, 11-20, 21-60, ≥61 years).
cord-021013-xvc791wx	2008	In animals, we can observe the analogous situation in that many insects and other invertebrates (especially those which are sessile and unprotected by armor), but also some vertebrates, store secondary metabolites for their defense which are often similar in structure to plant allelochemicals (1,4,12,16,17,28-30, [494] [495] [496] 503) . During the next three decades this concept was improved experimentally, and we can summarize the present situation as follows Although the biological function of many plant-derived secondary metabolites has not been studied experimentally, it is now generally assumed that these compounds are important for the survival and fitness of a plant and that they are not useless waste products, as was suggested earlier in the twentieth century (34, 35) . These "generalists," as we can also call this subgroup of herbivores, are usually deterred from feeding on plants which store especially noxious metabolites and select those with less active ones (such as our crop species, where man has bred away many of the secondary metabolites that were originally present; see Table XI ).
cord-048364-yfn8sy1m	2007	
cord-048446-gaemgm0t	2008	
cord-102749-tgka0pl0	2020	In this study, we first apply and compare different bioinformatics methods based on 16S ribosomal RNA gene and whole genome shotgun sequencing for taxonomic classification to three small mock communities of bacteria, of which the compositions are known. In particular, we propose an updated version of Kaiju, which combines the power of shotgun metagenomics data with a more focused marker gene classification method, similar to 16S rRNA, but based on core protein domain families (40, 41, 42, 43) from the PFAM database (44) . As shown in (27) , where different amplicon sequencing methods are tested on both simulated and real data and the results are compared to those obtained with metagenomic pipelines, the whole genome approach resulted to outperform the previous ones in terms of both number of identified strains, taxonomic and functional resolution and reliability on estimates of microbial relative abundance distribution in samples.
cord-103180-5hkoeca7	2020	Sample pooling methods improve the efficiency of large-scale pathogen screening campaigns by reducing the number of tests and reagents required to accurately categorize positive and negative individuals. Here we use computational simulations to determine how several theoretical approaches compare in terms of (a) the number of tests, to minimize costs and save reagents, (b) the number of sequential steps, to reduce the time it takes to complete the assay, (c) the number of samples per pool, to avoid the limits of detection, (d) simplicity, to reduce the risk of human error, and (e) robustness, to poor estimates of the number of positive samples. 25 Due to practical concerns, Dorfman''s group testing approach was never applied to 26 syphilis screening because the large number of negative samples had a tendency to 27 dilute the antigen in positive samples below the level of detection [6] .
cord-103342-stqj3ue5	2020	We propose a strategy for estimating the number of infections and the number of deaths, that does away with time-series modeling, and instead makes use of a ''phase portrait approach''. Using our model, we predict the number of infections and deaths in Italy and New York State, based on an adaptive algorithm which uses early available data, and show that our predictions closely match the actual outcomes. Our approach can be summarized as follows: The COVID-19 data from most countries suggests that, especially in the growing phase of the pandemic, the number of active cases and the number of hospitalizations are both proportional to the total number of infections: approximately around 70-90 % and 20-30%, respectively. Thus, using the data from South Korea as a reference standard, the deaths versus infections curve has been readjusted as seen in Figure:3A CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
cord-132307-bkkzg6h1	2020	
cord-151183-o06mwd4d	2020	While the Susceptible-Infected-Recovered (SIR) model may well describe the dynamics of the spreading 1,2 , accurate predictions rely on knowing the number of confirmed cases, which is severely hampered by the limitations of testing. Combining this information with the mortality rate can be a better strategy to predict the number of cases than relying on the con-firmed infection count alone. The exponential growth of the number of fatalities at the beginning of the epidemic should represent the spreading of COVID-19 reasonably well since the mechanisms for slowing the dynamics, such as improved detection and social distancing, are delayed in time By fitting the available fatalities data (see Appendix) between March 14 and 31 to Eq. 7, the parameters of the model can be determined. 4: The number of people who are infected and carrying the virus without being identified, I(t), as a function of time, with March 14 as day 0.
cord-151198-4fjya9wn	2020	Social distancing and lockdown are the two main non-pharmaceutical interventions being used by the UK government to contain and control the COVID-19 epidemic; these are being applied uniformly across the entire country, even though the results of the Imperial College report by Ferguson et al show that the impact of the infection increases sharply with age. We will denote by N j (t) the total number of j-individuals in the population at time t, and allow this to change gradually with the influx of new births, visitors from other countries; this is to model the possibility that new infecteds come in from outside and reignite the epidemic. where ι j and σ j are known functions of time representing the arrival of new asymptomatic infec-1 https://colab.research.google.com/drive/1tbB47uSGIA0WehY-hvIYgdO0mpnZU5A8 tives and susceptibles respectively 2 ; and the final term on the right-hand side of (3) allows for the possibility that removed infectives may not in fact be immune, and some may return to the population ready for reinfection.
cord-158219-hk55bzqm	2020	
cord-181220-gr29zq1o	2020	In order to estimate the counterfactual metric (say number of deaths), we use a geographic location as a treatment unit (say Italy) and a set of other geographic locations as donor group (say Brazil and United States). In this section we present three examples of the application of m-RSC to derive the counterfactual estimates of possible number of deaths under the changed conditions like delaying or starting the stringency measures at earlier date. In this work we use Multi-dimensional Robust Synthetic Control to understand the effects of stringency measure on COVID-19 pandemic. We construct synthetic version of a location using convex combination of other geographic locations in the donor pool that most closely resembled the treatment unit in terms of pre-intervention period using stringency index and adherence score (using mobility information).
cord-216208-kn0njkqg	2020	We develop a simple 3-dimensional iterative map model to forecast the global spread of the coronavirus disease. Our model contains at most two fitting parameters, which we determine from the data supplied by the world health organisation for the total number of cases and new cases each day. The fact that the available data for the pandemic can be fitted well by a simple model such as ours suggests that past and current interventions to curb the spread of the disease, globally, may not be very effective. In developing countries such as South Africa there is also a relatively large percentage of people with compromised immunity, due to the high prevalence of human immunodeficiency virus (HIV), and this also could result in the coronavirus having a much larger impact than our model of the current global data shows.
cord-221131-44n5pojb	2020	Since the start of the epidemic in China, a certain number of studies appeared in the mathematical community about this subject: the description of the spatial or temporal diffusion of the infected in given regions [4] , [8] [10] , the transmission dynamics of the infection [6] , the economic and financial consequences of the epidemic [1] , the effect of atmospheric indicators on the spread of the virus [5] , are only a fraction of the topics under investigation in these days. The reasonable assumption that the same fraction (with respect to the total) of infected, susceptible and recovered individuals are known, gives the possibility, in this case, to compare the measured data with the properties that are scale-independent. The second hypothesis is fundamental since we are going to look at scale-independent quantities: even in the case the measured number of infected and recovered individuals are different from the actual values, it is possible to estimate these quantities.
cord-223212-5j5r6dd5	2020	
cord-238241-ncz1b8dl	2020	
cord-248301-hddxaatp	2020	A number of alternatives for this computation are presented and results of numerical experiments involving over 230 people of various ages and background health levels in over 1700 visits that take place over three consecutive days. A novel partial infection model is introduced to discuss these proof of concept solutions which are compared to round robin uninformed time scheduling for visits to places. A method of optimization, in this proof of concept this is a Genetic Programming [7] method, takes these requests and simulates the outings by means of an infection model, to discover a nearly optimal allocation of precise time slots for visits that reduce the likely hospitalization and death numbers.
cord-252556-o4fyjqss	2020	The model has successfully predicted the rise and saturation of the spreading in terms of probabilities, i.e. the number of infected (or deceased) persons divided by the total number of tests performed. Among the EU countries, Germany shows the lowest number of deceased cases, which could be due to different ways of counting (for instance performing autopsies to check for the virus like in Italy). Thus in order to better stress the efficacy of the quarantine, we have plotted in figure 2 the number of cases DIVIDED by the population density, assuming that it is much easier to perform social distancing if the population density is low. Equation (1) has the same form observed in the figures (1) and (2) , but in reality it should be applied not the number of positives (or deceased) but to their probabilities, i.e. the number of cases divided by the total number of tests.
cord-257274-fzyamd7v	2020	CONCLUSION: According to our results, the pandemic has significantly affected our daily practice by decreasing elective surgeries and onsite clinics, but other activities have increased. Census data from 14 March 2018 to 14 April 2020, including our paediatric orthopaedics outpatient clinic, paediatric trauma emergency department (ED) and paediatric orthopaedic and trauma surgical cases were reviewed to compare the effects of the COVID-19 outbreak. In Figure 2 , *Univariate statistical analysis consisted of a student two-tailed t-test to compare the outcomes of mean number of consultations (including onsite and telemedicine), mean number of surgical procedures (including elective and urgent) and emergencies between 2018, 2019 and 2020 (including triage level). As the COVID-19 pandemic has interfered in our daily practice, we have found a decrease in the number of paediatric trauma patients admitted to our ED, the number of patients visiting onsite to our paediatric orthopaedic clinic and the number of elective cases compared with other years.
cord-258102-7q854ppl	2020	We use publicly available timeline data on the Covid-19 outbreak for nine indian states to calculate the important quantifier of the outbreak, the sought after Rt or the time varying reproduction number of the outbreak. This number can faithfully tell us the success of lockdown measures inside indian states, as containment policy for the spread of Covid-19 viral disease. The instantaneous version of basic reproduction number [14] of the infection is plotted against time to gauge the success [15] (or lack thereof) [16] of this policy intervention in nine dierent states of India. We set S (0) equals the population of the region, R(0) = 0, I (0) is 10 to 14 times the average number of conrmed cases from Day 0 to Day 7, and γ the inverse of mean infectious period, obtained from the parametrization of serial interval distribution collected directly from data described in section (3) .
cord-261530-vmsq5hhz	2020	Key findings in our results indicate that (i) universal social isolation measures appear effective in reducing total fatalities only if they are strict and the number of daily social interactions is reduced to very low numbers; (ii) selective isolation of only the elderly (at higher fatality risk) appears almost as effective in reducing total fatalities but at a much lower economic damage; (iii) an increase in the number of critical care beds could save up to eight lives per extra bed in a million population with the current parameters used; (iv) the use of protective equipment (PPE) appears effective to dramatically reduce total fatalities when implemented extensively and in a high degree; (v) infection recognition through random testing of the population, accompanied by subsequent (self) isolation of infected aware individuals, can dramatically reduce the total fatalities but only if conducted extensively to almost the entire population and sustained over time; (vi) ending isolation measures while R0 values remain above 1.0 (with a safety factor) renders the isolation measures useless and total fatality numbers return to values as if nothing was ever done; (vii) ending the isolation measures for only the population under 60 y/o at R0 values still above 1.0 increases total fatalities but only around half as much as if isolation ends for everyone; (viii) a threshold value, equivalent to that for R0, appears to exist for the daily fatality rate at which to end isolation measures, this is significant as the fatality rate is (unlike R0) very accurately known.
cord-270953-z2zwdxrk	2020	Analyzing the epidemic data reported in all 50 states of the USA, 61 during March of 2020 (the month when testing started), we investigated whether testing-related 62 variables -including massive and early testing− predict mortality. However, for predicting 86 deaths per million citizens, the apparent prevalence rate was a 3.5 times stronger predictor than 87 was the number of confirmed cases (Supplemental Table 2B) . Whether cases or fatalities are considered, findings indicate that reporting COVID-19 93 data as counts is not as informative as reporting metrics that consider two or more interacting 94 quantities, such as the apparent prevalence rate and the number of deaths/million citizens. For example, a recombination of those variables (the number of tests 105 performed in week I/million citizens/population density) empirically demonstrate that massive 106 and early testing may save lives (Figs.
cord-272085-4mqc8mqd	2020	Here, we develop a new mechanistic-statistical approach, based on a SIRD model (D being the dead cases compartment), in the aim of • estimating the effect of the lockdown in France on the contact rate and the effective reproduction number R e ; The computation of the solution of (1) with the posterior distribution of the parameters leads to a number of infectious I(t f ) = 7.0 · 10 5 and a total number of infected cases (including recovered) (I + R)(t f ) = 2.0 · 10 6 at the end of the observation period (April 14). We obtained an effective reproduction number that was divided by a factor 7, compared to the estimate of the R 0 carried out in France at the early stage of the epidemic, before the country went into lockdown [a value R 0 = 3.2 was obtained in (15) ].
cord-272838-wjapj65w	2019	This study employs an extended gravity model to analyse the complementarity or competitiveness relationship of the number of inbound tourists and corresponding tourism revenue between China and 19 other nations under the implementation of China''s Open-door Tourism Policy to Taiwan in 2008. Other studies have indicated that factors such as the security of the travelling spot, gourmet food, and scenic views are crucial for tourism decisions (Cîrstea, 2014; Enright & Table 1 Total number of tourists from the major nations to Taiwan, 2001 Taiwan, -2017 Year The other four inbound nations are India, Thailand, the Philippines, and Vietnam. The purpose of this study is to employ an extended gravity model (EGM) to explore the relationship between the change in the number of inbound tourists and the corresponding tourism revenue from China and from visitors from 19 other major nations to Taiwan in 2001-2017 under China''s Open-door Policy to Taiwan.
cord-273199-xmq502gm	2020	We propose an algebraic-type formula that describes with high accuracy the spread of Covid-19 pandemic under aggressive management for the periods of the intensive growth of the total number of infections. Anyway a sociological approach to the spread, which "explains" under some assumptions the power growth of the number of total cases, is quite natural in our work, because the active managements of epidemics is clearly of sociological nature, applicable only to humans. . https://doi.org/10.1101/2020.04.29.20084483 doi: medRxiv preprint An important outcome of our modeling is that the measures of "hard type", like detecting and isolating infected people and closing the places where the spread is almost inevitable, are the key for ending an epidemic. The predictions are of course based on the assumption that the intensity of hard measures continues to be proportional to the total number of detected infections to date, as it was clearly the case for the red dots.
cord-276870-gxtvlji7	2020	
cord-279245-z8pafxok	2020	
cord-282849-ve8krq78	2019	
cord-284195-qarz4o2z	2020	As such, a few weeks after these strict measures, and noting the reported success of China, governments of various provinces and countries are waiting for the new daily infections to cross over the peak. To date, other than China which continues to report nearly zero new infected cases every day for the past few weeks, all other countries are either in an exponential phase or a linear growth phase. In this work, we note by studying the COVID-19 infection data from several countries which implemented quarantine that the exponential growth phase ends, but it is followed by a linear growth phase. As much as the linear regime suggests the end of the exponential growth phase, a correlation of the daily cases with the average number of infections at the time of transition seems to suggest that the growth is only maintained in a "pause", frozen at the state where the quarantines are implemented.
cord-286076-60iwzsp6	2009	
cord-299846-yx18oyv6	2020	The stochastic discrete version of Pandæsim showed very good correlations between the simulation results and the statistics gathered from hospitals, both on day by day and on global numbers, including the effects of the lockdown. The number of people of each age slice leaving their home sub-regions is a stochastic sample (or averaged value for the deterministic continuous solver) of a percentage of the population of this sub-region. Starting from an initial state (number of contagious people in each sub-region), the simulation algorithm iterates the following process at each timestep until either the epidemic ends or the maximum duration of the simulation is reached (defaults to 720 days). When the initial number of contagious people was relatively high, for example, in the Val-de-Marne sub-region (180), the results for both solvers were nearly identical: 5207 deaths for the average of 1000 stochastic runs and 5204 deaths for a deterministic run (Figures 2 and 3) .
cord-300930-47a4pu27	2020	Mathematically, the problems of identifying infected individuals ( identification ) and estimating the total number of infected individuals in a given population ( infection rate ) are related but in fact can be addressed by subtly different algorithms to reduce the number of tests needed and thereby the total cost of doing testing. However, as we will demonstrate in this brief communication, estimating the number of infected individuals can be solved by novel adaptation of methods developed in theoretical computer science aimed at approximate counting. In addition to rate estimation we provide a review and analysis of several identification algorithms that can be deployed in communities with low infection rates that achieve reasonable improvement over the standard algorithms for group testing that have been previously explored. • Estimate the rate of the infection in the population or approximately count how many people test positive in a population of a given size with as few partially pooled tests as possible. We now describe approximate counting algorithms that use pools of samples to estimate accurate infection rates.
cord-303657-o66rchhw	2020	
cord-304820-q3de7r1p	2020	Cumulative number of reported (tested infectious) cases at time t Daily number of reported (tested infectious) cases at time t Phenomenological models for the reported cases: At the early stage of the epidemic, we assume that all the infected components of the system grow exponentially while the number of susceptible remains unchanged during a relatively short period of time t ∈ [t 1 , t 2 ]. In figure (d) we plot the cumulative number of cases coming from the model as a function of the cumulative number of tests from the data. In Figure 8 , we replace the daily number of tests n data (t) (coming from the data for New-York''s state) in the model by either 2 × n data (t), 5 × n data (t), 10 × n data (t) or 100 × n data (t). Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data
cord-306932-6vt60348	2020	In the absence of wide-spread testing, we provide one approach to infer prevalence based on the assumption that the fraction of true infections needing hospitalization is fixed and that all hospitalized cases of COVID-19 in Santa Clara are identified. However, even if this were true, we expect to continue to see an increase in hospitalized cases of COVID-19 in the short term due to the fact that infection of SARS-CoV-2 on March 17th can lead to hospitalizations up to 14 days later. As input parameters to our model, we need an estimate of the lag time , and the rate of growth of infections , and hospitalization rate for COVID-19 among those infected. For the rate of growth of infections , we compared two values: the first estimated from the change in hospitalizations from March 3 to March 12 in the Santa Clara data, and the second calculated from the reported 6-9 day doubling time 3 , 4 .
cord-307471-zukjh1hr	2020	The many variations on a graphic illustrating the impact of non-pharmaceutical measures to mitigate pandemic influenza that have appeared in recent news reports about COVID-19 suggest a need to better explain the mechanism by which social distancing reduces the spread of infectious diseases. In view of the extraordinary efforts underway to identify existing medications that are active against SARS-CoV-2 and to develop new antiviral drugs, vaccines and antibody therapies, any of which may have community-level effects, we also describe how pharmaceutical interventions affect transmission.  Social distancing refers to non-pharmaceutical measures to reduce the frequency or proximity of interpersonal encounters  The impact of these measures on epidemic curves is commonly misrepresented, suggesting a lack of understanding of the underlying mechanisms  As this may affect compliance with recommendations, we describe determinants of the magnitude and timing of peak incidence and the total number of infections  We also describe possible population-level effects of pharmaceutical interventions
cord-308505-nhcrbnfu	2020	
cord-309378-sfr1x0ob	2020	COVID-19 epidemic has been suppressed in Hungary due to timely non-pharmaceutical interventions, prompting a considerable reduction in the number of contacts and transmission of the virus. We incorporate various factors, such as age-specific measures, seasonal effects, and spatial heterogeneity to project the possible peak size and disease burden of a COVID-19 epidemic wave after the current measures are relaxed. Moreover, closing schools postpones the peak of the epidemic (by about one month in case of the above setting), suggesting that children may play a significant role in transmission due to their large number of contacts, even though they give negligible contribution to the overall mortality, cf. As control measures are being successively relaxed since May 4, we established an age-structured compartmental model to investigate several post-lockdown scenarios, and projected the epidemic curves and the demand for critical care beds assuming various levels of sustained reduction in transmission.
cord-310983-kwytbhe7	2020	
cord-314211-tv1nhojk	2020	The number of COVID-19 cases can be accurately predicted by considering historical data of reported cases alongside some external factors that affect the spread of the virus. [37] have proposed an AI-based algorithm for predicting COVID-19 cases using a hybrid Recurrent Neural Network (RNN) with a Long Short-Term Memory (LSTM) model. These important factors include population, median age index, public and private healthcare expenditure, air quality as a CO 2 trend, seasonality as month of data collection, number of arrivals in the country/territory, and education index. First, there is no previous study that simultaneously considers the historical data of the number of COVID-19 cases and most of the external factors that affect the spread of the virus. These external factors include population, median age index, public and private healthcare expenditure, air quality as a CO 2 trend, seasonality as month of data collection, number of arrivals in the country/territory, and education index.
cord-317093-c70c1op4	2015	With the aid of an SD model, we selected Kaohsiung as a case study to explore the effects of variations in demographics, fuel prices, and economic growth rate, among other factors, on the number of vehicles, fuel consumption, and energy-related CO 2 emissions. An SD model was used to evaluate the influence of the traditional supply chain and the vendor-managed inventory system on the performance of a firm''s supply chain [36] ; to examine the effects of policy scenarios on traffic volume, modal share, energy conservation, and CO 2 mitigation [37] ; and to investigate how incorporated systems, such as population, economy, transportation demand, transportation supply, and the vehicular emission of nitrous oxides, affect the dynamic development of urban transportation systems under five policy interventions on vehicle ownership [38] .
cord-319323-1qt7vf59	2020	
cord-326740-1fjr9qr4	2020	We propose a novel approach by which to calculate the risk of a customer being infected while queueing outside the store, while shopping, and while checking out with a cashier. We derive equilibrium strategies for a Stackelberg game in which the authority acts as a leader who first chooses the maximum number of customers allowed inside the store to minimize the risk of infection. In the second model, we analyze reducing waiting time in the payment queue (and ensuring the safety of cashiers and customers) by allowing store management to set aside a separate waiting space with limited capacity adjacent to the cashiers. In the game, the authority chooses a maximum number of customers allowed inside the store at a time to minimize the risk of transmission. Thus, in this setting, the store is divided into two separate areas: (i) the payment area with c ≥ 1 parallel cashiers and waiting space of size N customers and (ii) the shopping area, in which the maximum number of customers allowed, K.
cord-326785-le2t1l8g	2005	The lesions (usually multlpleand each 5 mm orless m diameter) were identified in lung parenchymaat a distance from the tumour and consisted of thickened alveolar walls lined by prominent, distinctly atypical cells morphologically Slmllar to type I 1 pneumacytes and cytologically different to the associated turnour Reactive changes 8" lung involved by obstrmtive pneumonitis were not included !n thts Sews All of the associated tumwra were peripheral adenocarcinamas and all showed a pattern of alveolar wall spread at the tumour periphery Clinically 7 of the patients were female and all were smokers or ex-smokers The slgnlflcance of this lesion in the histogenesis of primary pulmonary ademcarcinoma IS.
cord-328859-qx7kvn0u	2020	
cord-329357-ujh2nmh5	2020	Our aims are first to evaluate Tunisian control policies for COVID-19 and secondly to understand the effect of different screening, quarantine and containment strategies and the rule of the asymptomatic patients on the spread of the virus in the Tunisian population. With this work, we show that Tunisian control policies are efficient in screening infected and asymptomatic individuals and that if containment and curfew are maintained the epidemic will be quickly contained. In this work, a mathematical epidemiological model for COVID-19 is developed to study and predict the effect of different screening, quarantine, and containment strategies on the spread of the virus in the Tunisian population. Let''s assume that CR(t) = χ1 exp(χ2t) − χ3 with χ, χ2 and χ3 three positive parameters that we estimate using log-linear regression on cases data (see figure 2 and table 2).
cord-330956-692irru4	2020	The recent worldwide epidemic of Covid-19 disease, for which there is no vaccine or medications to prevent or cure it, led to the adoption of public health measures by governments and populations in most of the affected countries to avoid the contagion and its spread. α and β are the probability of disease transmission in a single contact with exposed (infected) people times the average daily number of contacts per person and have units of 1/day. We propose the use of control theory to determine public nonpharmaceuticals interventions (NPIs) in order to control the evolution of the epidemic, avoiding the collapse of health care systems while minimizing harmful effects on the population and on the economy. Therefore, the control action needs to be calculated as a function of the number of infected people I (the number of exposed people E is quite unknown) in order to avoid future hospitalization requirements in the next 10.6 days at most.
cord-331375-tbuijeje	2020	This paper provides an estimation of the accumulated detection rates and the accumulated number of infected individuals by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This paper provides an estimation of the accumulated detection rates and the accumulated number of infected individuals by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). By weighting the age-stratified IFRs by the country population agegroups shares in each country, it is possible to obtain countryspecific IFRs. The relevance of this study is 3-fold: Firstly, the estimation of the true number of infections includes not only confirmed cases but COVID-19 undetected cases, as well as SARS-CoV-2infected individuals without the disease, or in a pre-symptomatic stage. In order to provide reliable estimates of the number of SARS-CoV-2 infections and of the cumulative detection rates, it is necessary that governments provide real-time information about the number of COVID-19 deaths.
cord-334274-4jee19hx	2020	Private and public decision making should not be based on time series of CoV-2-infections as the latter do not provide information about the true epidemic dynamics in a country. 3 We show that time series on the number of tests and time series on reported infections do not allow one to obtain information about the true state of an epidemic. It also studies the (lack of) informational content of time series on reported infections and time series on the number of tests, and the properties of the positive rate. Testing increases the positive rate if the number of tests undertaken due to symptoms 17 This paper is about conceptional issues related to the …nding an unbiased estimator for an unobserved time series. If we knew the number of Covid-19 cases, i.e. CoV-2 infections with severe acute respiratory symptoms (SARS), then we would know at least one part of epidemic dynamics (Ĩ symp (t) in our model).
cord-337992-g4bsul8u	2018	We propose a simple continuous time stochastic Susceptible-Infected-Recovered model with a recurrent infection of an incidental host from a reservoir (e.g. humans by a zoonotic species), considering two modes of transmission, (1) animal-to-human and (2) human-to-human. The epidemiological processes are stochastic, which is particularly relevant in the case of transmission from the reservoir and more realistic because only a small number of individuals are expected to be infected at the beginning of an outbreak. In the case of emerging infectious diseases, no incidence is normally expected in the population so from a small number of infected individuals, the outbreak can be considered to spread. When the direct transmission increases the infection spreads more efficiently consuming a large number of susceptible individuals allowing few or no other excursion to reach the epidemiological threshold and producing only one outbreak when R 0 > 2.5.
cord-340131-refvewcm	2020	
cord-341088-bqdvx458	2020	
cord-344817-8xz7xbh1	2020	
cord-344911-pw0ghz3m	2020	title: Impact of the coronavirus disease pandemic on the number of strokes and mechanical thrombectomies: A systematic review and meta-analysis: COVID-19 and Stroke Care BACKGROUND: This systematic review and meta-analysis aimed to evaluate the impact of the coronavirus disease (COVID-19) pandemic on stroke care, including the number of stroke alerts/codes, number of reperfusions, and number of thrombectomies during the pandemic compared to those during the pre-pandemic period. This systematic review and meta-analysis aimed to evaluate the impact of this pandemic on stroke care, including the number of stroke alerts/codes, number of reperfusions, and number of thrombectomies during the COVID-19 pandemic compared to the pre-pandemic period. Meta-analysis of proportion was used to determine the number of stroke alerts/codes, reperfusions, and mechanical thrombectomies during the pandemic compared to that during the historical pre-pandemic control period. A meta-analysis of 9 studies showed that the number of stroke alerts/codes, reperfusions, and mechanical thrombectomies was less during the pandemic period than during the prepandemic period.
cord-347317-qcghtkk0	2020	For the estimation of the day-zero of the outbreak in Lombardy, as well as of the "effective" per-day transmission rate for which no clinical data are available, we have used the proposed SEIIRD simulator to fit the numbers of new daily cases from February 21 to the 8th of March. Among the perplexing problems that mathematical models face when they are used to estimate epidemiological parameters and to forecast the evolution of the outbreak, two stand out: (a) the uncertainty regarding the day-zero of the outbreak, the knowledge of which is crucial to assess the stage and dynamics of the epidemic, especially during the first growth period, and (b) the uncertainty that characterizes the actual number of the asymptomatic infected cases in the total population (see e.g.
cord-348584-j3r2veou	2020	Image processing and object detection software was used to count the number of passengers that were left behind on station platforms from surveillance video feeds. By comparing this data against manual observations of the times that train doors open and close in the station, a linear regression model is estimated to predict dwell time from the train tracking records, as described in Section 5.1. To test the implementation of object detection with video in transit stations, a first step is to identify locations and times to collect video feeds as well as direct manual observations of left-behind passengers. Transportation Research Part C 118 (2020) 102727 shows a clear relationship between the video counts and passengers being left behind on station platforms, so there is potential to use the video feed as an explanatory variable in a model to estimate the likelihood of passengers being unable to board a train.
cord-350510-o4libq5d	2020	
cord-351430-bpv7p7zo	2020	Further, we considered the following predictors: (1) time in days, to account for the exponential growth in case numbers during this period (Fig. 2) ; (2) number of arriving flights in the city''s metropolitan area in 2020, as airline connections can facilitate the spread of the virus (Ribeiro et al., 2020) ; (3) city population density, to account for facilitation of transmission under higher densities (Poole, 2020) ; (4) proportion of elderly people (≥60 years old) in the population, assuming that the elderly may be more likely to show severe symptoms of SARS-CoV-2 and, thus, to be diagnosed with COVID-19; (5) citizen mean income, which may affect the likelihood of people being infected by the virus, for example, due to limited access to basic sanitation or limited social isolation capabilities; (6) and the following meteorological variables: mean daily temperature ( C), mean daily solar radiation (kJ/m 2 ), mean daily relative humidity (%) and mean daily precipitation (mm).
cord-351830-x4sv6ieu	2020	In the absence of uncertainty, the optimal confinement policy is to impose a constant rate of lockdown until the suppression of the virus in the population. I show that introducing uncertainty about the reproduction number of deconfined people reduces the optimal initial rate of confinement. To illustrate, here is a short list of the sources of covid-19 uncertainties: The mortality rate, the rate of asymptomatic sick people, the rate of prevalence, the duration of immunity, the impact of various policies (lockdown, social distancing, compulsory masks, …) on the reproduction numbers, the proportion of people who could telework efficiently, and the possibility of cross-immunization from similar viruses. The uncertainty surrounding the reproduction number affects this expected cost because of the intricate non-linearities in the duration of the pandemic and in the sensitivity of the optimal future lockdown to new information.
cord-353318-12o3xniz	2020	
cord-354835-o0nscint	2020	
cord-355017-934v85q1	2020	Based on the WHO interim guidance developed for the 2014 Ebola outbreak [3] , convalescent plasma has advantages over other proposed treatment: it requires low technology (and therefore it can be produced where required independent of pharmaceutical companies), it is low cost and its production is easily scalable as long as there are sufficient donors. Furthermore, the real number of convalescent patients may be much greater than the number based on the recovery of previously identified patients because of the existence of asymptomatic and mild infections. Targeting populations at high risk of exposure such as contacts or health workers and self-identification of potentially convalescent patients using questionnaires could easily lead to as many plasma donors as required before the number of contagions peaks. Use of convalescent whole blood or plasma collected from patients recovered from Ebola virus disease for transfusion, as an empirical treatment during outbreaks.
cord-355201-pjoqahhk	2020	By adjusting or fitting necessary epidemic parameters, the effects of the following indicators on the development of the epidemic and the occupation of medical resources were explained: (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease cure time, and (4) population mobility. Through simulation, we show that the incubation period, response speed and detection capacity of the hospital, disease cure time, degree of population mobility, and infectivity of cured patients have different effects on the infectivity, scale, and duration of the epidemic. Among them, (1) incubation period, (2) response speed and detection capacity of the hospital, (3) disease cure time, and (4) population mobility have a significant impact on the demand and number of isolation beds (P <0.05), which agrees with the following regression equation: N = P * (-0.273 + 0.009I +0.234M + 0.012T1 + 0.015T2) * (1+V).