quadgram

This is a table of type quadgram and their frequencies. Use it to search & browse the list to learn more about your study carrel.

quadgram	frequency
the basic reproduction number	87
the spread of covid	76
the spread of the	64
chaos solitons fractals doi	58
the total number of	56
on the other hand	52
the number of infected	43
the dynamics of the	37
the transmission dynamics of	37
the authors declare that	35
in the number of	34
of the novel coronavirus	34
that they have no	34
of the number of	34
basic reproduction number r	34
authors declare that they	33
is locally asymptotically stable	33
declare that they have	32
reported in this paper	31
number of confirmed cases	31
severe acute respiratory syndrome	31
competing financial interests or	29
interests or personal relationships	29
appeared to influence the	29
that could have appeared	29
the evolution of the	29
have appeared to influence	29
could have appeared to	29
financial interests or personal	29
or personal relationships that	29
relationships that could have	29
personal relationships that could	29
to influence the work	29
work reported in this	28
the work reported in	28
no known competing financial	28
have no known competing	28
they have no known	28
known competing financial interests	28
influence the work reported	28
it can be seen	26
number of infected individuals	25
in the case of	25
on the dynamics of	24
with the help of	24
growth rate of new	24
as well as the	24
is globally asymptotically stable	23
the dynamics of covid	23
the beginning of the	23
on the basis of	22
a mathematical model for	22
spread of the virus	22
a large number of	22
is given by the	21
the mathematical theory of	20
the number of cases	20
transmission dynamics of the	20
spread of the disease	20
we have the following	20
the number of confirmed	20
number of infected people	20
can be seen that	20
the number of deaths	19
the rest of the	19
on the spread of	19
as shown in fig	19
the number of infections	19
and control of covid	19
can be used to	18
the end of the	18
at the beginning of	18
in the absence of	17
of the spread of	17
dynamics of novel coronavirus	17
of the basic reproduction	17
sir epidemic model with	17
the world health organization	17
the cumulative number of	17
the next generation matrix	17
virus in the environment	16
and the number of	16
on the number of	16
to the mathematical theory	16
the rate of infection	16
the numerical solution of	16
international spread of the	16
mathematical theory of epidemics	16
for the spread of	15
in cryptocurrency markets before	15
the early phase of	15
nowcasting and forecasting the	15
we assume that the	15
the dynamics of novel	15
domestic and international spread	15
potential domestic and international	15
outbreak originating in wuhan	15
forecasting the potential domestic	15
the potential domestic and	15
ncov outbreak originating in	15
and forecasting the potential	15
the number of new	15
the number of susceptible	15
the virus in the	15
and international spread of	15
dynamics of transmission and	15
for different values of	14
the impact of the	14
the daily growth rate	14
of transmission and control	14
is one of the	14
transmission and control of	14
with respect to the	14
rate of new cases	14
as a function of	14
a mathematical modelling study	14
this completes the proof	14
acute respiratory syndrome coronavirus	14
early dynamics of transmission	14
in the context of	14
it is assumed that	13
can be seen in	13
variance in cryptocurrency markets	13
of the mitigation strategies	13
asymptotically stable if r	13
the growth rate of	13
the peak of the	13
at the end of	13
is organized as follows	13
is based on the	13
the existence of the	13
modeling the dynamics of	13
locally asymptotically stable if	13
basic reproduction number of	13
mitigation strategies for covid	13
of this paper is	13
transmission dynamics of covid	13
the transmission of covid	12
in cryptocurrency markets during	12
in the early phase	12
the mitigation strategies for	12
cryptocurrency markets before pandemic	12
cryptocurrency markets during pandemic	12
stock markets before pandemic	12
the value of the	12
dynamics of the covid	12
mean in cryptocurrency markets	12
confirmed cases of covid	12
mean in stock markets	12
in stock markets during	12
it is possible to	12
which may be considered	12
stock markets during pandemic	12
one of the most	12
of the sir model	12
the disease free equilibrium	12
may be considered as	12
it should be noted	12
that the number of	12
be considered as potential	12
should be noted that	12
variance in stock markets	12
in stock markets before	12
to the best of	11
mathematical models have been	11
dynamics of the disease	11
can be written as	11
number of new cases	11
the ministry of health	11
the third equation of	11
of the disease in	11
the average number of	11
analysis of the model	11
of individuals in the	11
the best of our	11
in the form of	11
daily growth rate of	11
the lancet infectious diseases	11
analysis of the mitigation	11
number of novel coronavirus	11
of a novel coronavirus	11
that there is no	11
fractional order differential equations	11
the high risk group	11
the classic sir model	11
the second equation of	11
best of our knowledge	11
the proof of theorem	11
paper is organized as	11
the low risk group	11
of the virus and	11
the influence of the	11
early phase of the	11
a case study of	11
in this paper we	10
financial interests personal relationships	10
total number of infections	10
the time evolution of	10
a second wave of	10
no conflict of interest	10
the transmission of the	10
sarii q s q	10
model for simulating the	10
in this section we	10
the effect of the	10
the solution of the	10
of health of morocco	10
analysis and forecast of	10
with recurrent mobility pattern	10
total number of cases	10
number of susceptible individuals	10
increase in the number	10
of the risk of	10
represents the number of	10
number of infected players	10
relationships which may be	10
ministry of health of	10
the parameters of the	10
as a result of	10
fractional optimal control problem	10
following financial interests personal	10
interests personal relationships which	10
the proposed model is	10
personal relationships which may	10
of coronavirus disease in	10
on the one hand	10
of fractional differential equations	10
it follows from the	10
mathematical model for simulating	10
w is locally asymptotically	10
the following financial interests	10
declare the following financial	10
total number of confirmed	10
spreading and information diffusion	9
is shown in fig	9
of the virus in	9
time evolution of the	9
as potential competing interests	9
at the same time	9
is shown in figure	9
considered as potential competing	9
contributions to the mathematical	9
ncov and its implication	9
existence and uniqueness of	9
with a case study	9
a model based study	9
of the transmission risk	9
autoregressive integrated moving average	9
the impact of non	9
for the numerical solution	9
the number of days	9
of transmission of the	9
spread of the covid	9
implication for public health	9
all over the world	9
models of disease transmission	9
operational matrix of fractional	9
for public health interventions	9
in the susceptible population	9
sensitivity analysis of the	9
relative cost of vaccination	9
of the epidemic in	9
cryptocurrency and stock markets	9
a function of time	9
for the number of	9
of the proposed model	9
the case of the	9
are shown in fig	9
parameters of the model	9
and forecast of covid	9
and its implication for	9
infected with novel coronavirus	9
q s q model	9
the duration of the	9
spread of the epidemic	9
the asymptomatic infectious individuals	9
risk assessment of novel	9
transmission dynamics in wuhan	9
reproduction number r b	9
transmission risk of the	9
be seen that the	9
estimation of the transmission	9
its implication for public	9
assessment of novel coronavirus	9
the spread of infectious	9
different values of fractional	9
reduce the number of	9
the transmission risk of	9
by the end of	9
the number of daily	9
to the number of	9
control the spread of	9
countries around the world	9
authors declare the following	9
models have been proposed	9
estimation of the risk	9
is assumed that the	9
the mean time between	9
dynamics and control of	9
middle east respiratory syndrome	9
it is observed that	9
can be applied to	9
the mathematics of infectious	9
the amount of virus	9
p r a ft	9
the onset of symptoms	9
covidmaroc the ministry of	9
the local stability of	9
endemic equilibria for compartmental	8
it is easy to	8
are given in table	8
the number of individuals	8
evolution of the sars	8
diamond princess cruise ship	8
the analysis of the	8
early transmission dynamics in	8
the nature of the	8
equilibrium is globally asymptotically	8
peak number of infections	8
a contribution to the	8
networked population with recurrent	8
from the third equation	8
with respect to time	8
infectious diseases of humans	8
of the epidemic peak	8
mean time between infections	8
cumulative number of confirmed	8
spread of infectious diseases	8
infected population due to	8
of the coronavirus disease	8
the first equation of	8
population with recurrent mobility	8
total number of deaths	8
numerical solution of the	8
preliminary estimation of the	8
the existence of a	8
time forecasts and risk	8
of the lockdown rate	8
it is necessary to	8
that there is a	8
have been proposed to	8
mathematical model for the	8
the control measures v	8
as soon as possible	8
contribution to the mathematical	8
the number of active	8
model to predict the	8
the caputo fractional derivative	8
the basic reproductive number	8
is the number of	8
the incubation period of	8
number of days between	8
the diamond princess cruise	8
phase of the outbreak	8
transmission of the novel	8
spreading of pandemic covid	8
controlling the spread of	8
the daily new cases	8
based transmissibility of a	8
for compartmental models of	8
transmissibility of a novel	8
infection in the host	8
compartmental models of disease	8
as shown in figure	8
novel coronavirus in wuhan	8
the new fractional derivative	8
the state of the	8
is the rate of	8
isolation of cases and	8
the peak number of	8
equilibria for compartmental models	8
fractional order sidarthe model	8
the number of recovered	8
the existence and uniqueness	8
basic reproduction number is	8
system of differential equations	8
mathematics of infectious diseases	8
model to study the	8
is less than unity	8
it is clear that	8
the fact that the	8
cost of vaccination c	8
we obtain the following	8
reproduction number of novel	7
for a long time	7
describe the dynamics of	7
is estimated to be	7
model for the transmission	7
the reproductive number of	7
the basis of the	7
clinical features of patients	7
of ordinary differential equations	7
can be described as	7
to control the spread	7
the outbreak of covid	7
is observed that the	7
apen variance in cryptocurrency	7
of the mild cases	7
for the optimal control	7
that the spread of	7
can be found in	7
it has been observed	7
mathematical model of covid	7
there is no conflict	7
estimated value of the	7
taking into account the	7
the vaccine failure rate	7
study on the dynamics	7
for the seir model	7
analysis in the early	7
of confirmed cases of	7
the effective reproduction number	7
total number of infected	7
outbreaks by isolation of	7
and risk assessment of	7
of the impact of	7
we consider the following	7
model based on the	7
features of patients infected	7
to study the dynamics	7
definition of fractional derivative	7
intra and inter zone	7
and inter zone mobilization	7
of the epidemic and	7
forecasts and risk assessment	7
the characteristic equation of	7
an increase in the	7
model based study on	7
estimation of the number	7
derivative without singular kernel	7
based study on the	7
in the sir model	7
in supplementary material fig	7
driven analysis in the	7
at each time step	7
updated estimation of the	7
equation in the system	7
patients infected with novel	7
individuals in the population	7
the optimal control problem	7
can be defined as	7
the risk of transmission	7
the coupled slow system	7
rest of the world	7
value of the lockdown	7
in the field of	7
of the isolation room	7
the appearance of symptoms	7
the proof of the	7
to the fact that	7
of the pandemic in	7
and implementation of population	7
has been carried out	7
optimal control of a	7
of the next generation	7
rate of new infection	7
on the topic of	7
of new infection cases	7
of asymptomatic infected individuals	7
that is to say	7
by isolation of cases	7
the estimated value of	7
of cases and contacts	7
in the same way	7
a novel coronavirus from	7
we observe that the	7
has a unique positive	7
in the presence of	7
the following system of	7
the behavior of the	7
it is obvious that	7
accumulated number of infected	7
estimation of the basic	7
the number of tests	7
the fractional order sidarthe	7
the fractional derivative order	7
we see that the	7
described by the following	7
the presence of a	7
an updated estimation of	7
likely due to the	7
for simulating the phase	7
fractional derivative without singular	7
second wave of infection	7
with novel coronavirus in	7
virus infection in the	7
the maximum number of	7
in each of the	7
it is important to	7
of virus in the	7
natural science foundation of	7
i r epidemic model	7
the novel coronavirus outbreak	7
is no conflict of	7
the rate at which	7
matrix of fractional differentiation	7
to reduce the number	7
global stability of the	7
for the transmission dynamics	7
global asymptotic stability of	7
sei i r epidemic	7
risk of transmission of	7
the state of texas	7
is related to the	7
the city of jakarta	7
of an epidemic model	7
the effectiveness of the	7
study the dynamics of	7
epidemic and implementation of	7
show that the model	7
free equilibrium is globally	7
of patients infected with	7
pandemic lle variance in	6
amount of virus in	6
from the first equation	6
the sarii q s	6
in the city of	6
reaction and governmental action	6
to spreading of pandemic	6
using reduction in the	6
continuously evolving training data	6
and the existence of	6
due to spreading of	6
be noted that the	6
wide interventions in italy	6
and the effectiveness of	6
the sir model is	6
and move to the	6
spread of the novel	6
the relative cost of	6
markets before pandemic lle	6
when the basic reproduction	6
proof of the theorem	6
a stochastic epidemic model	6
fractional order sei i	6
has been shown in	6
is assumed to be	6
we can see that	6
pandemic apen mean in	6
and asymptotically infected people	6
model to analyze the	6
the myopic update rule	6
model of the covid	6
model with saturated incidence	6
lle variance in stock	6
of the epidemics trend	6
the spreading of the	6
the spread of coronavirus	6
of the asymptomatic infectious	6
of the model is	6
of an infectious disease	6
population due to spreading	6
in the population and	6
the model from scratch	6
epidemic model for the	6
apen mean in cryptocurrency	6
the epidemics trend of	6
a period of time	6
east respiratory syndrome coronavirus	6
of infected population due	6
during the pandemic period	6
the battle against the	6
feasibility of controlling covid	6
is the same as	6
values of fractional order	6
the strict social distancing	6
was supported by the	6
number of coronavirus disease	6
the accumulated number of	6
it is known that	6
has been observed that	6
deep convolutional neural network	6
of the infected individuals	6
at any time t	6
based on the data	6
lle variance in cryptocurrency	6
the number of swabs	6
and the state of	6
of continuously evolving training	6
in the estimated value	6
with a large number	6
we can observe that	6
transmission dynamics with a	6
lle mean in cryptocurrency	6
the normalized forward sensitivity	6
asymptotically stable when r	6
lle mean in stock	6
the disease in the	6
the development of the	6
it is found that	6
results show that the	6
reduction in the estimated	6
epidemics trend of covid	6
epidemiology of infectious diseases	6
of the optimal control	6
an epidemic model with	6
integrating the second equation	6
presence of a large	6
r a ft t	6
of the model and	6
are found to be	6
this study is to	6
the case of covid	6
in the time window	6
signifies the rate of	6
the number of coronavirus	6
order sei i r	6
and optimal control of	6
of severe acute respiratory	6
development of the epidemic	6
have been applied to	6
of the fractional derivative	6
adjusted estimation of the	6
the results of the	6
the onset of the	6
the existence of equilibria	6
case projection using reduction	6
are detected and quarantined	6
stable if r b	6
dynamics with a case	6
pandemic apen variance in	6
pandemic lle mean in	6
apen mean in stock	6
been shown in figure	6
markets during pandemic apen	6
the first day of	6
characteristics of the covid	6
number of infected cases	6
the start of the	6
the severe acute respiratory	6
as we can see	6
the spread of hiv	6
this is not the	6
solutions of the system	6
the fractional optimal control	6
in the battle against	6
coronavirus disease in china	6
in the state of	6
that the mean of	6
in networked population with	6
and the effects of	6
that there are no	6
clinical characteristics of coronavirus	6
can be described by	6
individual reaction and governmental	6
case study of wuhan	6
number of infections and	6
of this study is	6
markets during pandemic lle	6
of the fractional order	6
at the point t	6
characteristics of coronavirus disease	6
as one of the	6
it is shown that	6
to the spread of	6
the total population n	6
a mathematical model to	6
projection using reduction in	6
the observed daily new	6
would like to thank	6
evolution of the covid	6
a total population of	6
markets before pandemic apen	6
the course of the	6
due to the fact	6
epidemic spreading and information	6
the overall number of	6
data set is received	6
end of the season	6
apen variance in stock	6
of the total number	6
the paper is organized	6
the total population of	6
are assumed to be	5
network in the network	5
fractional order model for	5
partial rank correlation coefficient	5
be taken into account	5
move to the asymptomatic	5
can be shown that	5
national natural science foundation	5
are given in the	5
in the initial phase	5
the disease transmission rate	5
total cumulative number of	5
to study the transmission	5
effect of control strategies	5
in terms of the	5
model for coupling within	5
model is able to	5
as compared to the	5
the case of a	5
number of confirmed covid	5
texas in the usa	5
the growth of the	5
reproductive number of covid	5
modified seir and ai	5
the global stability of	5
where the number of	5
mathematical modeling of covid	5
from publicly reported confirmed	5
at a given time	5
this is shown in	5
basic reproduction number and	5
fractional order epidemic model	5
infected people increases with	5
the network visualization display	5
which is assumed to	5
a backward bifurcation at	5
the proposed fractional order	5
and international stock markets	5
two types of spreading	5
in the previous section	5
in addition to the	5
the force of infection	5
the pathogen from the	5
beginning of the quarantine	5
compared to sars coronavirus	5
dynamics of epidemic spreading	5
from patients with pneumonia	5
the expected number of	5
infection facilitates the rapid	5
unique solution of the	5
local and global stability	5
is given in appendix	5
as the number of	5
number of daily new	5
to predict the covid	5
when the value of	5
global sensitivity analysis of	5
probability of disease transmission	5
clusters distributed as follows	5
the context of covid	5
which is shown in	5
number of confirmed infected	5
in the network visualization	5
china under public health	5
at the time of	5
the model parameters are	5
analysis of control strategy	5
the proposed sirsi model	5
network visualization display mode	5
in the dynamics of	5
model can be used	5
undocumented infection facilitates the	5
have the greatest potential	5
model of infected population	5
no more than one	5
is the total number	5
of the infected density	5
a fractional optimal control	5
a new training data	5
the study of the	5
system of ordinary differential	5
have the following results	5
completes the proof of	5
dynamic model of infected	5
root mean square error	5
with pneumonia in china	5
stability of dynamical systems	5
an sir epidemic model	5
is considered to be	5
individuals which is shown	5
more than one sentence	5
novel coronavirus from patients	5
and global stability of	5
analysis of an epidemic	5
used in this study	5
is depicted in figure	5
this model can be	5
the level of stability	5
model from scratch every	5
at least one root	5
to the basic reproduction	5
in controlling the spread	5
the spectral radius of	5
when the number of	5
is higher compared to	5
the risk of being	5
increase the value of	5
total number of covid	5
is found that the	5
let us consider the	5
the fourth equation of	5
in view of the	5
we have clusters distributed	5
of control strategies to	5
we confirm that the	5
of the slow system	5
infected country or region	5
new training data set	5
w is unstable when	5
the total cumulative number	5
we obtain this way	5
estimate the model parameters	5
is considered as the	5
with rate of detection	5
are presented in section	5
rapid dissemination of novel	5
the authors would like	5
numerical solution of fractional	5
different values of the	5
dissemination of novel coronavirus	5
with a total population	5
mathematical epidemiology of infectious	5
specify contribution in more	5
the natural death rate	5
authors would like to	5
for the coronavirus disease	5
infected individuals and the	5
equilibrium w is locally	5
in the united states	5
which means that the	5
the mean of apen	5
coronavirus from patients with	5
the nucleotide mutation rate	5
for the development of	5
contribution in more detail	5
south africa and argentina	5
based on the assumption	5
number of active cases	5
the susceptible population s	5
under public health interventions	5
the presence of the	5
patients with pneumonia in	5
a reduction in the	5
control strategies to reduce	5
next generation matrix method	5
science foundation of china	5
population and aids people	5
dynamics of infectious diseases	5
the only way to	5
the number of the	5
with individual reaction and	5
necessary conditions for the	5
we can say that	5
the unique solution of	5
model to explore the	5
is the average time	5
social distancing rule is	5
to control the disease	5
of the infected population	5
state of the art	5
in the spread of	5
we confirm that we	5
to reduce social mixing	5
one or the other	5
local asymptotic stability of	5
is shown that the	5
dynamics of the model	5
the rapid dissemination of	5
has a unique solution	5
of the disease with	5
it can be shown	5
for the prediction of	5
the disease information diffusion	5
the severity of the	5
the endemic equilibrium point	5
between epidemic spreading and	5
infected population and aids	5
a unique endemic equilibrium	5
of the evolution of	5
local stability of the	5
it can be observed	5
cumulative number of infected	5
since there is no	5
of the proposed hybrid	5
the manuscript has been	5
risk of being infected	5
the outbreak of sars	5
pathogen from the environment	5
the epidemic peak and	5
substantial undocumented infection facilitates	5
the effect of control	5
higher compared to sars	5
the application of the	5
analysis is carried out	5
is governed by the	5
modelling the spread of	5
infected and recovered individuals	5
the infected population i	5
law growth of the	5
of vaccine failure rate	5
every time a new	5
of new cases is	5
study the impact of	5
developed a mathematical model	5
the spread of disease	5
endemic equilibrium point is	5
from scratch every time	5
the stability of dynamical	5
the sensitivity of the	5
as the basic reproduction	5
good agreement with the	5
as shown in table	5
for more details about	5
spread of hiv aids	5
strategies to reduce social	5
brownian motion on s	5
on the nature of	5
for the sir model	5
the peak of infection	5
a large set of	5
in the next section	5
as can be seen	5
infection of cd t	5
be described by the	5
the structure of the	5
existence of backward bifurcation	5
be seen in fig	5
if and only if	5
decrease the number of	5
with different fractional derivative	5
locally asymptotically stable whenever	5
cases and recovered cases	5
under the myopic update	5
the point t n	5
observed daily new covid	5
by the following system	5
in the host will	5
of backward bifurcation in	5
the global asymptotic stability	5
solution of fractional differential	5
total population of n	5
facilitates the rapid dissemination	5
the effect of a	5
have clusters distributed as	5
when compared to the	5
in china under public	5
this leads to a	5
seen in supplementary material	5
take into account the	5
and forecasting of the	5
publicly reported confirmed cases	5
the outbreak of the	5
the endemic equilibrium is	5
the number of people	5
prediction of the epidemics	5
scratch every time a	5
is said to be	5
deaths and recovered cases	5
the necessary conditions for	5
prediction of the epidemic	5
of the cumulative number	5
for fractional differential equations	5
the isolated slow system	5
reduce social mixing on	5
the disease persists in	5
the performance of the	5
time a new training	5
and that there are	4
in the range of	4
in this study is	4
the epidemic spreading probability	4
for the first time	4
order model for the	4
follows from the third	4
an artificial neural network	4
of the pathogen from	4
control of the covid	4
lockdown save mankind before	4
until the end of	4
paper is structured as	4
estimating the asymptomatic proportion	4
of texas in the	4
always has a disease	4
at the final state	4
host dynamics in environmentally	4
the initial value problem	4
proportion of coronavirus disease	4
the reason is that	4
the impact of lockdown	4
one of the key	4
masks in public places	4
to find out the	4
mathematical modeling of the	4
the topic discussion rate	4
s of radius a	4
the final size of	4
with each other for	4
hubei province in china	4
days since the first	4
the model shows that	4
the most influential parameters	4
pharmaceutical interventions on curtailing	4
at any given time	4
by the sir model	4
considered to be the	4
and recovered cases of	4
control and capacity constraints	4
increase in the susceptible	4
impact of public health	4
of the epidemic is	4
we observe as the	4
a new study on	4
epidemic spreading and disease	4
an important role in	4
as in the case	4
set the control measures	4
time of the virus	4
the probability distribution function	4
control of the epidemic	4
globally asymptotically stable for	4
fractional optimal control problems	4
infection from an asymptomatic	4
as well as to	4
is consistent with the	4
to fight against the	4
the epidemic of covid	4
diagram of function h	4
compared with the case	4
the simulation results of	4
result of contacting among	4
is defined as the	4
the complexity of the	4
class of asymptomatic infectives	4
of confirmed cases in	4
the infected individual is	4
it is worth noting	4
a mathematical model that	4
the value of r	4
to predict the peak	4
and the impact of	4
the disease and to	4
time forecasts of the	4
dynamics of the etiological	4
numbers and subthreshold endemic	4
incubation period of coronavirus	4
is smaller than the	4
the dynamics and the	4
the same as in	4
is applied to the	4
in india and effectiveness	4
have more communication and	4
a unique positive root	4
a numerical solution of	4
disease persists in the	4
is due to the	4
can be reduced to	4
by severe acute respiratory	4
number of new infections	4
a conceptual model for	4
from the onset of	4
to show that the	4
for the other countries	4
decay of the pathogen	4
and d is the	4
the sirsi model is	4
new definition of fractional	4
of reported infectious cases	4
the mean value function	4
to predict the epidemic	4
of the proposed fractional	4
with the case without	4
of newly infected people	4
optimal control strategies for	4
secondary infection seeded by	4
parameters are given in	4
to accurately predict the	4
equilibrium is locally asymptotically	4
the sum of the	4
is assumed to have	4
there is need to	4
down the epidemic spreading	4
the height of the	4
take care of the	4
and ai prediction of	4
seir and ai prediction	4
the case without control	4
epidemic analysis of covid	4
a new mathematical model	4
stop the spread of	4
the daily number of	4
and active the control	4
the epidemic topic search	4
of hiv infection of	4
numerical solution of differential	4
for disease control and	4
with bar chart representation	4
see table and table	4
strict social distancing in	4
analysis of fractional differential	4
infected female sex workers	4
been carried out to	4
effects of changing the	4
the evolution of this	4
equations of fractional order	4
the i ic class	4
also given in fig	4
on the disease spread	4
is lower than that	4
t dg p r	4
in the current study	4
of the standard sir	4
the jacobian matrix of	4
a result of contacting	4
a new definition of	4
to better understand the	4
number of infections is	4
the effect of social	4
second wave of infections	4
parameters are estimated from	4
we may conclude that	4
optimization algorithm based on	4
be related to the	4
a continuous dynamic behavior	4
set of parameter values	4
two time periods is	4
approach for the numerical	4
consider a mathematical model	4
to inoculate the vaccine	4
factors with rate of	4
solution of the proposed	4
theorem is given in	4
rmse and r score	4
is structured as follows	4
for the disease to	4
after a period of	4
the model can be	4
the sir model and	4
is to analyze the	4
model to characterize the	4
mixing on outcomes of	4
the effects of changing	4
effectiveness of preventive measures	4
the city of wuhan	4
nonlinear ordinary differential equations	4
model of hiv infection	4
of novel corona virus	4
of the most important	4
number of cases and	4
assess the evolution of	4
per unit of time	4
number of cases in	4
reported with bar chart	4
for each of the	4
to account for the	4
the fractional differential equations	4
interventions on curtailing the	4
of the disease are	4
number of individuals in	4
a numerical scheme for	4
number of cases of	4
are two types of	4
as it can be	4
models have been developed	4
hiv infection of cd	4
save mankind before vaccination	4
purpose of this study	4
models for infectious diseases	4
the rate of change	4
in standalone or hybridized	4
publicly available data from	4
that the total number	4
a ft t g	4
china with individual reaction	4
is the length of	4
that number of infected	4
been assumed to be	4
after the start of	4
a basic reproduction number	4
the probability that the	4
a new approach for	4
is worth noting that	4
from to a we	4
to assess the evolution	4
to model the spread	4
the number of covid	4
results of the covid	4
locally asymptotically stable when	4
the magnitude of the	4
of the model parameters	4
the evolution of covid	4
estimation of epidemiological parameters	4
and effectiveness of preventive	4
number of isolated individuals	4
number of active infected	4
the state and co	4
short period of time	4
are used in the	4
used to predict the	4
this shows that the	4
has become a pandemic	4
in the world and	4
the parameters in systems	4
agent of sars in	4
deaths due to covid	4
to predict the future	4
forecasting the spread of	4
to take care of	4
able to predict the	4
the transition density function	4
transmission of the virus	4
is the unique solution	4
an increasing number of	4
dynamics and mitigation scenarios	4
conceptual model for the	4
for the proof of	4
on the transmission of	4
responses are reported in	4
modeling of epidemic diseases	4
is it difficult to	4
people in the country	4
is unstable when r	4
forecasting using lstm networks	4
the overall population of	4
number is less than	4
the expected values of	4
ai prediction of the	4
mathematical assessment of the	4
model to forecast the	4
the case of china	4
states and overall india	4
the rate of recovery	4
battle against the covid	4
from the environment by	4
chinese center for disease	4
etiological agent of sars	4
of the epidemic peaks	4
behavioral change for susceptibles	4
approved by all named	4
based on the pairwise	4
to take into account	4
model to fit the	4
is shown in table	4
of wuhan novel coronavirus	4
data of the covid	4
without loss of generality	4
for the parameters of	4
virus into the environment	4
solution of differential equations	4
of infection as a	4
leads to the following	4
the etiological agent of	4
beginning of the pandemic	4
obtain the following eigenvalue	4
the actual number of	4
have shown that the	4
sir model and the	4
contacting among a susceptible	4
stability results of the	4
the standard sir model	4
model for the coronavirus	4
the lockdown save mankind	4
compared with real data	4
the fraction of vaccinated	4
role as a route	4
has at least one	4
on the assumption that	4
reproduction numbers and subthreshold	4
on a boundary arc	4
the informed individuals would	4
beginning of the epidemic	4
of the fast system	4
training data set is	4
time delay to become	4
onset of the epidemic	4
is given by where	4
wearing face masks in	4
to deal with the	4
transmission of the disease	4
this paper is structured	4
the lives of doctors	4
the interaction radius r	4
the sensitivity analysis of	4
which leads to the	4
of the infected and	4
zone mobilization factors with	4
pharmaceutical interventions on the	4
of sars in hong	4
compared to that of	4
types of spreading dynamics	4
delayed sir epidemic model	4
with the increase of	4
the greatest potential of	4
an optimal regression tree	4
analysis of the covid	4
there will be a	4
we know that the	4
are presented in the	4
of the pandemic and	4
mathematical modelling of human	4
number of infections in	4
infection as a result	4
the global sensitivity analysis	4
by all named authors	4
can be explained by	4
in the infected population	4
the average level of	4
are shown in figure	4
epidemiological parameters and epidemic	4
the local outbreak of	4
fractional derivatives with nonlocal	4
to explore the transmission	4
it can be easily	4
or the other of	4
the epidemic dynamics and	4
play a major role	4
v and active the	4
different fractional derivative order	4
study the transmission dynamics	4
level of stability in	4
modelling of human liver	4
on curtailing the novel	4
of cases and deaths	4
of the disease and	4
both locally and globally	4
cumulative case projection using	4
is the class of	4
of days between matches	4
may not be as	4
in most of the	4
that the increase in	4
the initial conditions are	4
based on riesz wavelets	4
these two types of	4
and application to heat	4
in good agreement with	4
it is interesting to	4
of epidemiological parameters and	4
asymptotically stable whenever r	4
the end of this	4
daily notified case projection	4
epidemic and disease information	4
spread of this disease	4
can the lockdown save	4
a pure birth process	4
of fractional differentiation on	4
spreading and disease information	4
the asymptomatic proportion of	4
we have seen that	4
study on the mathematical	4
curtailing the novel coronavirus	4
delay to become cure	4
india and effectiveness of	4
the spreading of covid	4
that the implementation of	4
of contacting among a	4
in the caputo sense	4
the following eigenvalue problem	4
sir model can be	4
the topic with the	4
of the arima model	4
per people in the	4
of a fractional order	4
cases on board the	4
on the mathematical modelling	4
the number of publications	4
like to thank the	4
sars in hong kong	4
the diagram of function	4
partial derivatives of f	4
of the endemic equilibrium	4
in order to obtain	4
results are shown in	4
of the new fractional	4
the early stages of	4
effective reproduction number r	4
threshold endemic equilibria for	4
with saturated incidence rate	4
that the total population	4
it is observed from	4
a short period of	4
parameters and epidemic predictions	4
bar chart representation in	4
early estimation of epidemiological	4
this is because the	4
globally asymptotically stable when	4
the aim of this	4
increasing the rate of	4
the virus into the	4
using the proposed model	4
the largest lyapunov exponent	4
in this study we	4
it was found that	4
characteristic equation of system	4
model with general incidence	4
can see that the	4
a we can obtain	4
mortality and healthcare demand	4
are estimated from the	4
the national natural science	4
that can be used	4
proposed model can be	4
among a susceptible case	4
with optimal control analysis	4
can be observed from	4
the distribution of the	4
of novel coronavirus infections	4
r with respect to	4
with the novel coronavirus	4
for the fractional optimal	4
spreading in networked population	4
section is devoted to	4
in the following theorem	4
confirm that we have	4
the length of the	4
on board the diamond	4
the virus infection in	4
the ministry of interior	4
the mathematical modelling of	4
a ib satisfying a	4
average number of new	4
for the rest of	4
more than of population	4
the solutions of the	4
of the epidemic spreading	4
across the two time	4
beltrami operator on s	4
social mixing on outcomes	4
by the fact that	4
board the diamond princess	4
disease control and prevention	4
it is evident that	4
the individuals in the	4
on the diamond princess	4
the chinese center for	4
on outcomes of the	4
a recovered person to	4
we conclude that the	4
model the spread of	4
standalone or hybridized mode	4
modeling of the spread	4
assessment of the impact	4
during the rest of	4
inter zone mobilization factors	4
the induced optimization problem	4
face masks in public	4
the first days of	4
a huge amount of	4
in the proposed model	4
dashboard to track covid	4
positivity and boundedness of	4
with the real data	4
is the implementation of	4
the endemic equilibrium of	4
novel coronavirus outbreak in	4
after the beginning of	4
declare that there is	4
the definition of fractional	4
the trend of the	4
that the model is	4
in a similar way	4
rate of disease transmission	4
the theme of covid	4
average time delay to	4
interventions such as social	4
the probability of having	4
markets before the pandemic	4
can be interpreted as	4
reproduction number is less	4
period of the disease	4
operating procedures have been	4
solution of the model	4
growth of the cumulative	4
makes it possible to	4
ml and ai technology	4
of ml and ai	4
the transmission dynamics and	4
than that of the	4
number of new covid	4
the proposed hybrid model	4
outcomes of the covid	4
growth of the number	4
of human liver with	4
proposed fractional order sei	4
for a recovered person	4
a total of days	4
in the ncbi genbank	4
the two time periods	4
to stop the spread	4
long short term memory	4
of the paper is	4
mobilization factors with rate	4
a susceptible case and	4
are depicted in fig	4
is also given in	4
given by the following	4
of infected individuals from	4
of deaths at the	4
are listed in table	4
infection from the environment	4
the epidemic starts to	4
rate of change of	4
to a we can	4
a given time t	4
number of deaths at	4
and subthreshold endemic equilibria	4
to tackle the pandemic	4
more sensitive to the	4
difficult to accurately predict	4
increases with increment in	4
daily new cases data	4
for the solution of	4
obtain this way a	4
the model parameters using	4
the eigenvalues of this	4
of theorem is given	4
we can write the	4
is not the case	4
are given in section	4
from the preventive isolation	4
the epidemic spreading process	4
in more than countries	4
when the rate of	4
a new type of	4
the size of the	4
of the confirmed cases	4
asymptomatic proportion of coronavirus	4
epidemic in china from	4
that the peak of	4
virus in the host	4
the novel coronavirus disease	4
of infected players at	4
we would like to	4
both intra and inter	4
value of the basic	4
stability and irregularity in	4
we set the control	4
to describe the dynamics	4
w is globally asymptotically	4
come down to from	4
reproduction number r t	4
class are detected and	4
based dashboard to track	4
time from infection to	4
equations of the system	4
and of the asymptomatic	4
and mitigation scenarios of	4
of the exposed class	4
reduce the spread of	4
surges in the number	4
fraction of vaccinated individuals	4
of differential equations of	4
to heat transfer model	4
by the new coronavirus	4
given in the following	4
prediction for the spread	4
of the total population	4
during the first days	4
the turning point and	4
to model the covid	4
of the state variables	4
reproduction numbers and sub	4
differential equations of fractional	4
large set of asymptomatic	4
effect of social distancing	4
decreased due to natural	4
application to heat transfer	4
transmission dynamics of hiv	4
derivatives with nonlocal and	4
the local asymptotic stability	4
new fractional derivatives with	4
with respect to t	4
of india and the	4
the latin hypercube sampling	4
order epidemic model for	4
to study the impact	4
multiplicative fractional white noise	4
to publicly available data	4
epidemic model with saturated	4
after the end of	4
solutions of the model	4
of infected people increases	4
can also be seen	4
is able to predict	4
it difficult to accurately	4
as the spectral radius	4
is given in the	4
of the etiological agent	4
dynamics of the infection	4
is in good agreement	4
theory and application to	4
on the pairwise approach	4
algorithm for the numerical	4
proof of theorem is	4
accurately predict the covid	4
in the real world	4
rate of infection as	4
modeling the impact of	4
new study on the	4
of fractional order differential	4
corrector approach for the	4
between these two types	4
optimal control problem is	4
unstable when r b	4
subthreshold endemic equilibria for	4
why is it difficult	4
explore the impact of	4
reproduction number r is	4
asymptotic stability of the	4
for important intellectual content	4
the covid epidemic in	4
period of coronavirus disease	4
the findings of this	4
number of the infected	4
the impact of different	4
state of texas in	4
final size and epidemic	3
the infection rate of	3
in a variety of	3
for the same limited	3
it is well known	3
of the reproduction number	3
have the following result	3
can help the government	3
the system of eqs	3
of quarantine control in	3
investigate the effect of	3
effect in some states	3
of infectious diseases and	3
are known to be	3
that there has been	3
disease free equilibrium e	3
we recall some basic	3
control measures v and	3
the model dynamics are	3
using the concept of	3
a large class of	3
if this is not	3
in the assessment of	3
the use of a	3
stable if r and	3
optimal control of the	3
death cases and recovered	3
jacobian to zero gives	3
characteristics of and important	3
most of the population	3
real data for the	3
are based on the	3
a delayed sir epidemic	3
the same as that	3
span of the epidemic	3
mathematical study on covid	3
used in the model	3
the period from january	3
individual is introduced into	3
of infections and the	3
is described by the	3
the exposed individuals which	3
is presented in figure	3
four transport equations are	3
are employed in the	3
found to be the	3
since the beginning of	3
phases at day with	3
increasing the number of	3
in order to estimate	3
equilibrium u is globally	3
the cumulative case incidence	3
the report of new	3
equation for this jacobian	3
the impact of awareness	3
the time profile of	3
the epidemic can be	3
a route that spread	3
null that the mean	3
optimal control for a	3
of a large class	3
important lessons from the	3
dynamics of the epidemic	3
fractional calculus and fractional	3
china by dynamical modeling	3
predict the behavior of	3
that the immunity acquired	3
stabilize at a positive	3
of changing the fractional	3
india and the predicted	3
p r n fx	3
claim the following result	3
summary of a report	3
reported in the literature	3
of infected population of	3
lyapunov functions for fractional	3
rate of new infections	3
order on the system	3
strategy to minimize the	3
the asymptomatic duration as	3
it makes sense to	3
number of recovered cases	3
attack rate of the	3
it is given by	3
effect of quarantine control	3
will be used to	3
with external source for	3
the epidemic peak of	3
are presented in fig	3
quarantine measures and economic	3
using the estimated parameters	3
deaths at the time	3
jacobian at the dfe	3
during pandemic apen mean	3
it is important that	3
measures v and v	3
relax the social distance	3
seir infectious disease model	3
next generation matrix f	3
first case was reported	3
using these values in	3
in the report of	3
a larger fraction of	3
a sir epidemic model	3
is the maximum number	3
diagnosis of the coronavirus	3
given to the aware	3
is local asymptotically stable	3
into account the presence	3
lockdown effect in some	3
of strict social distancing	3
fractional order derivative have	3
we have shown that	3
of confirmed infected cases	3
have also been used	3
the codon mutation rate	3
it was also shown	3
is yet to be	3
longer period of time	3
model for the covid	3
parameters used in the	3
infected individuals by the	3
and it can be	3
can be found that	3
class and of the	3
the endemic equilibrium points	3
the optimal control is	3
we introduce the following	3
of relative cost of	3
the mutation rates for	3
the early stage of	3
the isolation room with	3
a consequence of the	3
the dynamics of epidemic	3
enough to contain the	3
modeling the epidemic dynamics	3
the need to retrain	3
a time delay dynamic	3
hence by theorem above	3
known as the basic	3
to become smir model	3
for the local outbreak	3
based on estimates from	3
an algorithm for the	3
of spread of the	3
is equivalent increase in	3
the possibility of a	3
fit the model to	3
in the first phase	3
is used for the	3
rate at which the	3
interventions on the dynamics	3
formulation of the model	3
the variability in stability	3
which implies that the	3
in the high risk	3
coronavirus disease in japan	3
the same limited resources	3
number r b and	3
to the zero eigenvalue	3
have been used to	3
the problem of endemicity	3
the global financial crisis	3
nonlinear fractional differential equation	3
the result of this	3
asymptomatic duration as well	3
cells in the host	3
dynamics and describe biological	3
analogous to a mixing	3
order sir model with	3
predict the scale and	3
which is close to	3
between individuals and common	3
one and only one	3
pcr testing in coronavirus	3
day of major government	3
of a sir epidemic	3
the following initial conditions	3
estimated parameter values are	3
definition of the new	3
after the day of	3
for confirmation of covid	3
the roots of the	3
the dynamics and mitigation	3
conditions for the optimality	3
the mild cases increases	3
the local and global	3
to a value less	3
the effect of quarantine	3
a new fractional hrsv	3
takes the following form	3
is expected to take	3
the mild cases as	3
a longer period of	3
newly developed optimization algorithm	3
of global health concern	3
slow down the epidemic	3
the first case of	3
for fractional order systems	3
individuals in the i	3
the novel coronavirus indicating	3
of the control strategies	3
been proposed to model	3
optimal control of an	3
differential equations with fractional	3
changing the infection rates	3
to fit the model	3
cases infected with covid	3
hypothesis of equality of	3
control of a delayed	3
changes in the state	3
early detection of asymptomatic	3
the null that the	3
eigenvalues of this system	3
an optimal control problem	3
the rmse error is	3
model with nonlinear incidence	3
the epidemiological implication of	3
and disease information diffusion	3
the stereographic projection coordinates	3
exponential increase in the	3
of the novel covid	3
can be regarded as	3
significant impact on the	3
ft tg p r	3
one of the main	3
number of contacts per	3
time profile of the	3
number of deaths in	3
to international stock markets	3
the time of dfe	3
final size of the	3
world health organization coronavirus	3
of infectious disease dynamics	3
number of secondary infections	3
by tang et al	3
n n a n	3
of being detected as	3
used to solve the	3
the positivity and boundedness	3
is taken from the	3
i and a d	3
of the infected people	3
the main contributions of	3
a harmonic oscillator with	3
secondary infections generated by	3
caputo fractional derivative of	3
effect of the control	3
for a safe return	3
and stock markets exhibit	3
size of the outbreak	3
novel coronavirus outbreak of	3
set of asymptomatic infectives	3
in view of these	3
in the class and	3
of the brownian motion	3
peak number of cases	3
the spread of this	3
size on the diamond	3
that the contact rate	3
using three different techniques	3
cryptocurrency markets before the	3
of infection in the	3
during the pandemic time	3
interval of novel coronavirus	3
most effective way of	3
infectious disease caused by	3
is much faster than	3
if there is a	3
time series of covid	3
has been used to	3
is set to be	3
is shown in the	3
the novel corona virus	3
well as the contact	3
be noted that we	3
as remedial steps in	3
the set of parameters	3
the incubation period and	3
dynamics on the network	3
the day of major	3
of novel coronavirus with	3
of pneumonia of unknown	3
unquarantined asymptomatic infectious to	3
quantifying the effect of	3
india is shown in	3
case study of the	3
leads to a contradiction	3
an asymptomatic contact in	3
possible control of the	3
in the novel coronavirus	3
given in the table	3
analyse the impact of	3
that the solution of	3
spread the disease to	3
the eigenvalues of the	3
mathematical argument of theorem	3
due to the time	3
on the effect of	3
be efficient if more	3
u is globally asymptotically	3
international journal of infectious	3
effectiveness of the quarantine	3
rate of infection from	3
enable people to have	3
cumulative number of symptomatic	3
relaxing the strict social	3
the delay between the	3
of this disease is	3
of asymptomatic cases is	3
results of the disease	3
of coupled ordinary differential	3
as long as the	3
to be noted that	3
for the treatment of	3
which corresponds to the	3
disease there is need	3
third equation of system	3
in rio de janeiro	3
sphere s of radius	3
the outbreak of novel	3
an euler type numerical	3
in this group are	3
the contact and containment	3
population phases at day	3
of extended legendre polynomial	3
a modified sir model	3
smooth function f on	3
of the effective reproduction	3
the graphs of the	3
travel restrictions on the	3
assess the impact of	3
sir model for covid	3
asymptotic stability of disease	3
endemic equilibrium when r	3
of infection from the	3
each other for a	3
accommodate surges in the	3
by the national natural	3
health organization declares global	3
of social distancing measures	3
of public health interventions	3
presented in the table	3
the necessary optimality conditions	3
in any of the	3
and the uk are	3
the number of secondary	3
forecasting of the covid	3
were obtained from the	3
system with external source	3
of basic reproduction number	3
that significantly affect the	3
external source for the	3
with the total number	3
of the parameters of	3
on the epidemic spreading	3
or knowledge of individuals	3
of pneumonia associated with	3
human liver with caputo	3
the relationship between the	3
by the time s	3
values of relative cost	3
the success of the	3
the stability of the	3
wuhan city of china	3
the topic of origin	3
using a newly developed	3
the experience or knowledge	3
there is equivalent increase	3
model to understand the	3
ncov infection from an	3
the most effective way	3
the partial rank correlation	3
reduce the risk of	3
of the caputo fractional	3
the number of exposed	3
essential causal variables that	3
without detected including death	3
during the h n	3
in the first half	3
with the aim to	3
there is a unique	3
end of this year	3
novel coronavirus has been	3
dependent on the number	3
the proposed model and	3
dimensional sphere s of	3
used in the mathematical	3
have been used in	3
an seir infectious disease	3
the phase plane of	3
to the vaccine failure	3
and drug vaccine development	3
into the environment by	3
hrsv model and its	3
a smooth function f	3
for the optimality of	3
listed in the table	3
number of susceptible people	3
in stability and irregularity	3
sufficient condition for the	3
could be used to	3
control strategy for the	3
be found in the	3
the diffusion of disease	3
virus pathogen in the	3
of changing the infection	3
individuals move to the	3
help the government to	3
governed by the following	3
disease starts to spread	3
if there were no	3
caused by the virus	3
a novel coronavirus outbreak	3
is obvious that the	3
of infection of the	3
individuals in the society	3
will be applied to	3
rebuild the model from	3
we deal with the	3
c and vaccine failure	3
this class of models	3
trapped into an isolated	3
of spatial spread relationships	3
and the dynamics of	3
reached at time t	3
corresponding to the zero	3
need to take care	3
the number of persons	3
here we see that	3
duration of the pandemic	3
presumed asymptomatic carrier transmission	3
of the function f	3
for the mean value	3
findings of this study	3
coupled ordinary differential equations	3
of a change in	3
predictive mathematical study on	3
the dynamics of transmission	3
optimal control problem by	3
peak of coronavirus disease	3
global dynamics of a	3
fractional optimal control of	3
stability in price dynamics	3
globally asymptotically stable in	3
scenario of continuously evolving	3
level of regularity compared	3
for disease prevention and	3
containment can be efficient	3
application of the arima	3
and active immune system	3
analysis with a case	3
important to note that	3
and then the expectation	3
to numerically model the	3
the fractional order necessary	3
spread relationships of coronavirus	3
neural network model for	3
of the country and	3
to have more communication	3
disease caused by a	3
days from the onset	3
evolution of the pandemic	3
number of asymptomatic patients	3
the size of r	3
in an increase in	3
and analysis of the	3
pathogen in the environment	3
of equality of means	3
correlation of chest ct	3
spreading of the coronavirus	3
the dynamics on the	3
network model for covid	3
can be efficient if	3
of stochastic differential equations	3
was found to be	3
of this system are	3
spread the virus to	3
to adopt the vaccine	3
the operational matrix of	3
differential equations with applications	3
then the endemic equilibrium	3
indicating that the increase	3
numerically model the laminar	3
application of the proposed	3
parameters such as the	3
to reduce the risk	3
only a moderate effect	3
can be explored in	3
infected people i p	3
the direct adjoining approach	3
rest of the paper	3
and important lessons from	3
fractional derivative with mittag	3
activity in the system	3
relaxing the social distancing	3
in the mathematical argument	3
from the first day	3
we can get following	3
and references therein for	3
is presented in section	3
for the purpose of	3
arima model on the	3
serial interval of novel	3
the per capita rate	3
the impact of various	3
the parameters of fig	3
below which the epidemic	3
to assume that the	3
patient in future time	3
before and during covid	3
in this paper is	3
the rate of disease	3
input deep convolutional neural	3
stock markets before and	3
value less than unity	3
of the system is	3
order to numerically model	3
derivative have rich dynamics	3
for systems science and	3
infection of the mild	3
of regularity compared to	3
lessons from the coronavirus	3
data from different countries	3
in some states and	3
outbreak size on the	3
the propagation of the	3
that an infected individual	3
recovered person becomes susceptible	3
transition density function p	3
of an sir epidemic	3
of virus y at	3
the epidemic and the	3
individuals in a population	3
is the time when	3
susceptible individuals can be	3
account the undetected infections	3
familial cluster of pneumonia	3
the extinction and the	3
of the virus is	3
the number of healthy	3
numerical simulation of the	3
model is presented in	3
dg p r n	3
outbreaks as well as	3
in future time has	3
epidemic topic search volume	3
condom use for exposed	3
a predictive mathematical study	3
extinction and the persistence	3
the specific optimal policy	3
much faster than that	3
every n test days	3
of infected people and	3
after the incubation period	3
of and important lessons	3
an epidemic like covid	3
different population phases at	3
represent the number of	3
each of the three	3
italy and the state	3
for the simulation of	3
what would be the	3
the assumption that the	3
the center for systems	3
description of the model	3
modelling the influence of	3
on the asymptomatic duration	3
to the analysis of	3
the white noise intensity	3
in the transmission dynamics	3
mathematical analysis of a	3
of the disease is	3
provide decision support for	3
and deaths in the	3
there is no effective	3
represents the spectral radius	3
due to the high	3
a familial cluster of	3
we can obtain the	3
of conditioned air released	3
the estimated parameter values	3
curves are shown in	3
has been extended to	3
harmonic oscillator with position	3
individuals from the disease	3
daily deaths due to	3
quarantine control in covid	3
affected by the pandemic	3
before the pandemic is	3
of the disease by	3
the polya urn model	3
as a route that	3
and boundedness of solutions	3
unlike typical deep learning	3
has one and only	3
from the outbreak of	3
vaccine under the myopic	3
of the jacobian matrix	3
transmission of the infection	3
decrease after implementing control	3
testing and contact tracing	3
supported by the natural	3
reproduction number and the	3
in order to numerically	3
people compared with the	3
shows that the compulsory	3
assume that the function	3
fractional differential equations with	3
considering both intra and	3
taken into consideration in	3
causal variables that significantly	3
system of fractional order	3
mathematical analysis of the	3
of this study are	3
duration as well as	3
fever with renal syndrome	3
the implementation of the	3
are reported in the	3
compulsory containment can be	3
the variance of apen	3
showed that the covid	3
more data becomes available	3
as a generalization of	3
we can conclude that	3
fractional order sir model	3
is well known that	3
about of the total	3
is to minimize the	3
sirs epidemic model with	3
the time window february	3
before pandemic apen mean	3
chest ct and rt	3
is the solution of	3
individuals in which the	3
in order to get	3
value of vaccine failure	3
the population is divided	3
it is not possible	3
from the disease and	3
the mobility and contact	3
matrix is given by	3
of total number of	3
to be in the	3
existence of the optimal	3
consider the following fractional	3
death at a rate	3
the different population phases	3
for the control of	3
to be susceptible again	3
is expected that the	3
of a harmonic oscillator	3
time periods is rejected	3
has been read and	3
first days of the	3
there is no vaccination	3
daily new infection cases	3
the present study is	3
makes it easy to	3
and vaccine failure rate	3
impact on the disease	3
that the compulsory containment	3
the system has a	3
a new fractional modelling	3
is the first country	3
the number of patients	3
experience or knowledge of	3
transmission dynamics and control	3
curtail the spread of	3
the control reproduction number	3
the center manifold theorem	3
the infectiousness rate due	3
and maintaining social distance	3
with this in mind	3
into the fourth equation	3
the optimal control theory	3
number of infections will	3
the last three months	3
exists in the population	3
to the increase in	3
have no conflict of	3
the jacobian at the	3
for the month of	3
each of the parameters	3
of the population in	3
fractional order necessary conditions	3
of vaccination c and	3
more details about the	3
the authors declare no	3
techniques have been used	3
to curtail the spread	3
the jacobian of the	3
the infection in the	3
review of the novel	3
the asymptomatic ratio of	3
additive fractional white noise	3
if the acquired immunity	3
have rich dynamics and	3
with fractional order derivative	3
the disease has been	3
journal of infectious diseases	3
the aware infected individuals	3
of infectious diseases in	3
the proposed approach is	3
plane of state variables	3
that the most effective	3
order to prove the	3
tg p r a	3
qualitative analysis of the	3
order to obtain the	3
in terms of a	3
a mathematical model of	3
first case of covid	3
and describe biological systems	3
t is given by	3
and stability analysis of	3
mathematical model on covid	3
of infected individuals will	3
a constant rate and	3
similar degree of stability	3
in terms of their	3
model with vaccination and	3
is to investigate the	3
before pandemic lle mean	3
study is to analyze	3
the published data and	3
using the least square	3
only slow down the	3
asymptomatic contact in germany	3
traditional time series forecasting	3
are presented in table	3
disease and to prevent	3
the disease with a	3
period from january to	3
the lyapunov candidate function	3
by the following expression	3
time series forecasting of	3
this jacobian to zero	3
model to evaluate the	3
then the system has	3
the numerical solutions of	3
help to understand the	3
analysis and optimal control	3
can slow down the	3
be caused by the	3
need to retrain or	3
rich dynamics and describe	3
is the average number	3
been read and approved	3
to quantify the most	3
this paper is the	3
a theoretical framework to	3
involves four transport equations	3
confinement is not perfect	3
subjected to different disease	3
the rate of decay	3
there exists not any	3
detected infected population are	3
for five different countries	3
completed the incubation period	3
the legendre operational matrix	3
the interactive topics of	3
epidemic model with vaccination	3
all individuals are equivalent	3
the model is able	3
homogeneous mixing of the	3
the fraction of the	3
and the predicted responses	3
that the manuscript has	3
has been investigated in	3
of the model for	3
window of length days	3
the last part of	3
of stability in price	3
r a ft tg	3
with the inclusion of	3
small influx of infected	3
the null hypothesis of	3
the novel coronavirus covid	3
we find that the	3
order derivative have rich	3
notified case projection using	3
from february to february	3
and forecasting of epidemic	3
equilibrium w is unstable	3
obtaining the numerical solution	3
of the virus into	3
parameters with respect to	3
convolutional neural network for	3
to the total population	3
epidemic spreading in networked	3
the acquired immunity is	3
disease induced death rate	3
derivative can be defined	3
been estimated to be	3
of r in the	3
to protect the lives	3
population of various countries	3
countries across the globe	3
for the existence of	3
as seen in the	3
the arima model on	3
be globally asymptotically stable	3
the month of aug	3
to the absence of	3
vaccinated individuals become susceptible	3
to be able to	3
new approach for the	3
convolutional neural network model	3
is defined as where	3
forecast during the mentioned	3
in order to improve	3
induced errors in the	3
confirmed cases in the	3
of chest ct and	3
behavior of the infected	3
a stochastic sir model	3
health organization coronavirus disease	3
parts of the world	3
has been applied to	3
disease caused by the	3
also depends on the	3
dynamics of the parameter	3
stock markets has not	3
supported by the national	3
from the proposed model	3
it is estimated that	3
the female sex workers	3
interact with each other	3
that the model dynamics	3
strategies i and j	3
the forecast during the	3
the proposed model can	3
with multiplicative fractional white	3
the seir model is	3
of the outbreak of	3
of population are confined	3
the hiv aids epidemic	3
of the disease there	3
with saturated incidence and	3
a completely susceptible population	3
world health organization declares	3
dynamics of the network	3
then the caputo derivative	3
of the pairwise approach	3
for numerical solutions of	3
can not only slow	3
from country to country	3
new cases infected with	3
in the low risk	3
for ordinary differential equations	3
the point of view	3
of the country lockdown	3
in figure shows the	3
in order to take	3
spreading dynamics of covid	3
the proposed model to	3
three states of india	3
distancing and community containment	3
model has been applied	3
this paper is organized	3
of a mathematical model	3
and approved by all	3
safe return to work	3
of infectious diseases mathematical	3
from the city of	3
d is the total	3
markets has not been	3
consider the following lyapunov	3
other for a certain	3
the sir model that	3
to mix with aerosol	3
the decrease of the	3
developed optimization algorithm based	3
and its optimal control	3
inflection point in the	3
rest of the season	3
dynamical models of tuberculosis	3
natural death at a	3
with fractional white noise	3
there is no disease	3
case was reported on	3
model and machine learning	3
using data up to	3
equating characteristic equation for	3
the time when the	3
model shows that the	3
of the daily growth	3
possibly contributes to the	3
motion on s in	3
efficiency of treatment given	3
markets during the pandemic	3
and unreported symptomatic infected	3
should be taken into	3
during the imposed quarantine	3
from march nd to	3
a newly developed optimization	3
individuals by the new	3
into account the undetected	3
contact and containment rates	3
and possible control of	3
of the parameters and	3