This is a table of type bigram and their frequencies. Use it to search & browse the list to learn more about your study carrel.
bigram | frequency |
---|---|
novel coronavirus | 218 |
differential equations | 173 |
reproduction number | 151 |
sir model | 146 |
infected individuals | 144 |
optimal control | 141 |
fractional order | 141 |
infectious diseases | 125 |
fractional derivative | 123 |
social distancing | 121 |
basic reproduction | 118 |
confirmed cases | 112 |
mathematical model | 111 |
coronavirus disease | 106 |
epidemic model | 105 |
time series | 91 |
transmission dynamics | 90 |
public health | 90 |
infected population | 86 |
free equilibrium | 84 |
epidemic spreading | 83 |
new cases | 81 |
infected people | 80 |
asymptotically stable | 79 |
endemic equilibrium | 73 |
proposed model | 73 |
total number | 73 |
machine learning | 70 |
infected cases | 67 |
deep learning | 66 |
infectious disease | 62 |
stock markets | 62 |
fractional differential | 59 |
chaos solitons | 59 |
mathematical models | 59 |
solitons fractals | 59 |
doc id | 58 |
fractals doi | 58 |
cord uid | 58 |
disease transmission | 58 |
susceptible individuals | 57 |
control measures | 57 |
model parameters | 56 |
total population | 53 |
seir model | 52 |
initial conditions | 51 |
susceptible population | 50 |
authors declare | 49 |
contact tracing | 49 |
control strategies | 49 |
sensitivity analysis | 47 |
cryptocurrency markets | 47 |
real data | 46 |
respiratory syndrome | 45 |
incubation period | 44 |
recovery rate | 43 |
world health | 42 |
health organization | 42 |
backward bifurcation | 42 |
per day | 40 |
financial interests | 39 |
personal relationships | 39 |
locally asymptotically | 39 |
fractional calculus | 39 |
daily new | 38 |
mathematical modelling | 38 |
mutation rate | 38 |
see table | 37 |
acute respiratory | 37 |
numerical solution | 36 |
mathematical modeling | 36 |
ordinary differential | 36 |
different countries | 36 |
modelling study | 36 |
fractional derivatives | 35 |
information diffusion | 35 |
south korea | 35 |
differential equation | 35 |
caputo fractional | 35 |
social media | 35 |
parameter values | 34 |
numerical simulations | 34 |
infectious individuals | 33 |
brownian motion | 33 |
severe acute | 32 |
death rate | 32 |
neural network | 32 |
health care | 32 |
white noise | 32 |
transmission rate | 32 |
growth rate | 31 |
epidemic threshold | 31 |
high risk | 30 |
competing financial | 30 |
asymptomatic infectious | 30 |
epidemic peak | 29 |
case study | 29 |
globally asymptotically | 29 |
disease information | 29 |
mortality rate | 29 |
equilibrium point | 29 |
model based | 28 |
work reported | 28 |
known competing | 28 |
large number | 28 |
asymptomatic individuals | 27 |
control problem | 27 |
reproductive number | 27 |
epidemic models | 27 |
contact rate | 27 |
risk group | 27 |
slow system | 27 |
disease free | 26 |
epidemic dynamics | 26 |
cumulative number | 26 |
sir epidemic | 26 |
reported cases | 26 |
dynamical systems | 25 |
fractional optimal | 25 |
infected individual | 25 |
hiv aids | 25 |
hubei province | 25 |
incidence rate | 24 |
recovered individuals | 24 |
recovered cases | 24 |
failure rate | 24 |
artificial intelligence | 24 |
next generation | 24 |
parameter estimation | 24 |
epidemiological data | 24 |
exposed individuals | 24 |
coronavirus outbreak | 24 |
mitigation strategies | 23 |
disease spread | 23 |
epidemiological models | 23 |
driven analysis | 23 |
asymptomatic infected | 23 |
vaccine failure | 23 |
new fractional | 23 |
derivative order | 23 |
disease dynamics | 23 |
generation matrix | 23 |
asymptomatic cases | 23 |
training data | 22 |
heilongjiang province | 22 |
equilibrium points | 22 |
strict social | 22 |
global stability | 22 |
mathematical theory | 22 |
early detection | 22 |
control strategy | 21 |
infected person | 21 |
arima model | 21 |
health interventions | 21 |
state variables | 21 |
ray images | 21 |
vaccinated individuals | 21 |
asymptomatic infectives | 21 |
corona virus | 21 |
singular kernel | 20 |
different values | 20 |
social distance | 20 |
see text | 20 |
operational matrix | 20 |
numerical solutions | 20 |
syndrome coronavirus | 20 |
time period | 20 |
pharmaceutical interventions | 20 |
april th | 20 |
second wave | 20 |
time step | 20 |
data set | 19 |
time delay | 19 |
united states | 19 |
numerical simulation | 19 |
international spread | 19 |
asymptomatic patients | 19 |
moving average | 19 |
transmission model | 19 |
initial condition | 19 |
symptomatic infected | 18 |
situation report | 18 |
order model | 18 |
order differential | 18 |
active cases | 18 |
herd immunity | 18 |
population size | 18 |
jacobian matrix | 18 |
fractional white | 18 |
asymptotic stability | 18 |
isolation room | 18 |
low risk | 18 |
infection cases | 18 |
stochastic model | 18 |
susceptible people | 18 |
zone mobilization | 17 |
second equation | 17 |
preventive measures | 17 |
virus disease | 17 |
infectious cases | 17 |
logistic model | 17 |
unique solution | 17 |
stability analysis | 17 |
mean value | 17 |
fractional model | 17 |
recurrent mobility | 17 |
affected countries | 17 |
infection rate | 16 |
public places | 16 |
early phase | 16 |
positive equilibrium | 16 |
spreading dynamics | 16 |
numerical results | 16 |
confirmed covid | 16 |
case fatality | 16 |
riesz wavelets | 16 |
immune system | 16 |
among individuals | 16 |
virus infection | 16 |
new infection | 16 |
characteristic equation | 16 |
sirs model | 16 |
raw data | 16 |
financial crisis | 16 |
infected patients | 16 |
susceptible individual | 16 |
early dynamics | 16 |
time interval | 16 |
social networks | 16 |
wbf model | 15 |
lyapunov function | 15 |
potential domestic | 15 |
social contacts | 15 |
risk assessment | 15 |
initial value | 15 |
fatality rate | 15 |
third equation | 15 |
real world | 15 |
ncov outbreak | 15 |
vaccination behavior | 15 |
epidemic spread | 15 |
neural networks | 15 |
mathematical analysis | 15 |
outbreak originating | 15 |
infected class | 15 |
sidarthe model | 15 |
whole population | 15 |
exposed class | 15 |
two types | 15 |
new covid | 14 |
quarantine control | 14 |
symptomatic infectious | 14 |
global sensitivity | 14 |
time forecasts | 14 |
turning point | 14 |
new coronavirus | 14 |
caputo derivative | 14 |
confirmed infected | 14 |
integer order | 14 |
fractional brownian | 14 |
two different | 14 |
lyapunov exponents | 14 |
convolutional neural | 14 |
latent period | 14 |
stochastic differential | 14 |
compartmental models | 14 |
exponential growth | 14 |
daily growth | 14 |
available data | 14 |
international stock | 14 |
new data | 14 |
complex networks | 14 |
quarantine measures | 14 |
inter zone | 13 |
epidemic disease | 13 |
final size | 13 |
numerical method | 13 |
chest ct | 13 |
order sidarthe | 13 |
asymptotically infected | 13 |
one hand | 13 |
mild cases | 13 |
moulton method | 13 |
new infected | 13 |
infected i | 13 |
different fractional | 13 |
i ic | 13 |
epidemic diseases | 13 |
chaotic behavior | 13 |
apen variance | 13 |
operating procedures | 13 |
various countries | 13 |
rapid test | 13 |
beltrami operator | 13 |
fabrizio fractional | 13 |
positive cases | 13 |
observed data | 13 |
become susceptible | 13 |
adaptive process | 13 |
initial values | 13 |
infected density | 13 |
given time | 13 |
infection rates | 13 |
compartmental model | 13 |
infected cells | 13 |
policy makers | 13 |
fractional differentiation | 13 |
simulation results | 13 |
apen mean | 12 |
legendre polynomial | 12 |
initial data | 12 |
disease model | 12 |
pandemic lle | 12 |
lstm networks | 12 |
class i | 12 |
markov chain | 12 |
average time | 12 |
time evolution | 12 |
relative cost | 12 |
stochastic epidemic | 12 |
nonlinear fractional | 12 |
fractional integral | 12 |
incremental learning | 12 |
local stability | 12 |
stationary distribution | 12 |
epidemiological model | 12 |
reproduction numbers | 12 |
data sets | 12 |
long time | 12 |
series forecasting | 12 |
pandemic apen | 12 |
lockdown period | 12 |
recorded data | 12 |
wick product | 12 |
causal variables | 12 |
networked population | 12 |
inflection point | 12 |
coupled slow | 12 |
control variables | 12 |
pandemic covid | 12 |
peak number | 12 |
sirsi model | 12 |
published data | 12 |
natural death | 12 |
widely used | 12 |
disease outbreaks | 12 |
days ahead | 12 |
learning techniques | 12 |
dynamical system | 12 |
lle mean | 12 |
health system | 12 |
two positive | 12 |
numerical scheme | 12 |
mobility pattern | 12 |
biological systems | 12 |
condom use | 12 |
lockdown rate | 12 |
competing interests | 12 |
lle variance | 12 |
saturated incidence | 12 |
control problems | 12 |
transfer learning | 11 |
hybrid model | 11 |
equity markets | 11 |
disease will | 11 |
lancet infectious | 11 |
novel corona | 11 |
epidemic data | 11 |
role models | 11 |
viral infection | 11 |
infected players | 11 |
using mathematical | 11 |
following theorem | 11 |
stochastic models | 11 |
total infected | 11 |
containment rate | 11 |
host dynamics | 11 |
model predictions | 11 |
series data | 11 |
state individuals | 11 |
ebola virus | 11 |
negative real | 11 |
new infections | 11 |
clinical characteristics | 11 |
pure birth | 11 |
estimated parameters | 11 |
ct images | 11 |
five different | 11 |
even though | 11 |
necessary conditions | 11 |
order derivative | 11 |
cases data | 11 |
average number | 11 |
population fraction | 11 |
cases reported | 11 |
learning approach | 11 |
decision making | 11 |
update rule | 11 |
without singular | 11 |
first equation | 11 |
global pandemic | 11 |
nucleotide mutation | 11 |
driven adaptive | 11 |
classic sir | 11 |
using machine | 11 |
infectious individual | 11 |
cruise ship | 11 |
lockdown effect | 11 |
health authorities | 11 |
basic reproductive | 11 |
pairwise approach | 11 |
common areas | 11 |
individuals will | 11 |
following result | 11 |
order derivatives | 11 |
using deep | 11 |
boundary arc | 11 |
results show | 11 |
mean time | 11 |
human population | 11 |
different strategies | 11 |
quarantine actions | 11 |
asymptomatic class | 11 |
many countries | 11 |
fractional operators | 10 |
south africa | 10 |
transition rate | 10 |
incubation time | 10 |
series analysis | 10 |
life expectancy | 10 |
baleanu fractional | 10 |
surge periods | 10 |
smoothed pseudo | 10 |
theoretical results | 10 |
east respiratory | 10 |
genomic sequence | 10 |
tridip sardar | 10 |
becomes available | 10 |
approximate entropy | 10 |
global health | 10 |
intensive care | 10 |
cumulative cases | 10 |
detected infected | 10 |
hybrid arima | 10 |
discussion rate | 10 |
first case | 10 |
model using | 10 |
interests personal | 10 |
individuals become | 10 |
spectral radius | 10 |
coronavirus covid | 10 |
epidemic outbreak | 10 |
proposed fractional | 10 |
pandemic period | 10 |
generalized adams | 10 |
myopic update | 10 |
behavioral change | 10 |
aids epidemic | 10 |
corresponding author | 10 |
th may | 10 |
lstm model | 10 |
human transmission | 10 |
transmission risk | 10 |
mutation rates | 10 |
become infected | 10 |
dashed curves | 10 |
people will | 10 |
containment measures | 10 |
based study | 10 |
original draft | 10 |
reported data | 10 |
will eventually | 10 |
cases will | 10 |
epidemic duration | 10 |
incidence data | 10 |
numerical analysis | 10 |
law growth | 10 |
epidemic topic | 10 |
face masks | 10 |
dynamic model | 10 |
following financial | 10 |
type models | 10 |
developing countries | 10 |
power law | 10 |
different types | 10 |
publicly available | 10 |
interactive topics | 10 |
middle east | 10 |
confinement measures | 10 |
cumulative case | 10 |
infected persons | 10 |
isolated slow | 10 |
forecasting model | 10 |
countries around | 10 |
princess cruise | 9 |
learning technique | 9 |
people increases | 9 |
learning based | 9 |
real parts | 9 |
following form | 9 |
attack rate | 9 |
endemic equilibria | 9 |
constant rate | 9 |
tests per | 9 |
epidemic control | 9 |
first days | 9 |
imposed quarantine | 9 |
effective contact | 9 |
will also | 9 |
death toll | 9 |
till may | 9 |
correlation coefficient | 9 |
diamond princess | 9 |
mean square | 9 |
clinical features | 9 |
mathematical epidemiology | 9 |
topic discussion | 9 |
null hypothesis | 9 |
free networks | 9 |
expected number | 9 |
care facilities | 9 |
based models | 9 |
unreported symptomatic | 9 |
term memory | 9 |
female sex | 9 |
probability distribution | 9 |
sei i | 9 |
projection coordinates | 9 |
different scenarios | 9 |
mobility possibility | 9 |
every day | 9 |
total cases | 9 |
data becomes | 9 |
case projection | 9 |
many researchers | 9 |
potential competing | 9 |
zika virus | 9 |
wuhan city | 9 |
infectious rate | 9 |
short time | 9 |
covid epidemic | 9 |
mainland china | 9 |
hiv infection | 9 |
human mobility | 9 |
global asymptotic | 9 |
see appendix | 9 |
time markov | 9 |
memory effect | 9 |
epidemic outbreaks | 9 |
disease burden | 9 |
drug discovery | 9 |
hopkins university | 9 |
network structure | 9 |
informed individuals | 9 |
gene sequence | 9 |
proposed method | 9 |
short term | 9 |
value problem | 9 |
population will | 9 |
novel covid | 9 |
likely due | 9 |
maximum number | 9 |
experimental results | 9 |
autoregressive integrated | 9 |
liouville fractional | 9 |
riesz wavelet | 9 |
time span | 9 |
integrated moving | 9 |
law network | 9 |
early stage | 9 |
sex workers | 9 |
complete lockdown | 9 |
coronavirus infections | 9 |
proposed hybrid | 9 |
distancing measures | 9 |
actual data | 9 |
learning model | 9 |
recent years | 8 |
virus spreads | 8 |
i will | 8 |
first time | 8 |
square error | 8 |
th january | 8 |
rate due | 8 |
gives us | 8 |
time periods | 8 |
dengue fever | 8 |
population dynamics | 8 |
model without | 8 |
epidemic will | 8 |
stereographic projection | 8 |
time dependent | 8 |
optimization problem | 8 |
good agreement | 8 |
receding horizon | 8 |
will help | 8 |
control theory | 8 |
complex dynamics | 8 |
population due | 8 |
learning algorithm | 8 |
stock market | 8 |
effective reproduction | 8 |
situation reports | 8 |
allows us | 8 |
first two | 8 |
collocation method | 8 |
value function | 8 |
images using | 8 |
numerical experiments | 8 |
two time | 8 |
death cases | 8 |
stability results | 8 |
current covid | 8 |
every days | 8 |
transmission rates | 8 |
modified seir | 8 |
partial differential | 8 |
effective way | 8 |
direct contact | 8 |
current pandemic | 8 |
estimated parameter | 8 |
deterministic model | 8 |
network model | 8 |
supplementary material | 8 |
effectiveness analysis | 8 |
term forecasts | 8 |
proposed approach | 8 |
model simulation | 8 |
infected pneumonia | 8 |
adaptive networks | 8 |
three categories | 8 |
susceptible class | 8 |
well known | 8 |
sk shahid | 8 |
initial population | 8 |
preliminary estimation | 8 |
time steps | 8 |
following system | 8 |
objective functional | 8 |
physical distancing | 8 |
following equation | 8 |
second derivative | 8 |
infections generated | 8 |
previous studies | 8 |
vaccine efficacy | 8 |
natural science | 8 |
newly infected | 8 |
caputo sense | 8 |
ongoing covid | 8 |
coronavirus pandemic | 8 |
disease spreading | 8 |
epidemic prevalence | 8 |
early transmission | 8 |
review editing | 8 |
deep convolutional | 8 |
pandemic outbreak | 8 |
least one | 8 |
based transmissibility | 8 |
present study | 8 |
overall india | 8 |
leffler kernel | 8 |
comparative study | 8 |
science foundation | 8 |
refinable functions | 8 |
quarantine effectiveness | 8 |
virus spread | 8 |
bibliometric analysis | 8 |
spreading process | 8 |
lyapunov functions | 8 |
symptomatic individuals | 8 |
based forecasting | 8 |
intervention strategies | 8 |
cases per | 8 |
epidemic peaks | 8 |
protective measures | 8 |
adaptive network | 8 |
johns hopkins | 8 |
known infectives | 8 |
long short | 8 |
acquired immunity | 8 |
cumulative confirmed | 8 |
case incidence | 8 |
estimated value | 8 |
big data | 8 |
codon mutation | 8 |
computational simulations | 8 |
recovered person | 8 |
real time | 8 |
patients infected | 8 |
using real | 8 |
seir epidemic | 8 |
logistic curve | 8 |
permanent immunity | 8 |
major role | 7 |
observed daily | 7 |
derivative without | 7 |
largest lyapunov | 7 |
disease caused | 7 |
control measure | 7 |
infection probability | 7 |
sir models | 7 |
three different | 7 |
infected case | 7 |
become cure | 7 |
social network | 7 |
section presents | 7 |
versus time | 7 |
search volume | 7 |
forecasting covid | 7 |
first day | 7 |
support vector | 7 |
isolation control | 7 |
incidence curve | 7 |
real part | 7 |
generalized logistic | 7 |
effective strategy | 7 |
hiv disease | 7 |
approach based | 7 |
adaptive behavior | 7 |
human behavior | 7 |
factors affecting | 7 |
clinical symptoms | 7 |
prediction error | 7 |
see also | 7 |
statistical methods | 7 |
fast time | 7 |
recurrent neural | 7 |
epidemiological parameters | 7 |
sensitivity indices | 7 |
nonlinear incidence | 7 |
new model | 7 |
regression tree | 7 |
quarantined individuals | 7 |
necessary optimality | 7 |
model will | 7 |
using lstm | 7 |
deep transfer | 7 |
new mathematical | 7 |
optimal policy | 7 |
partial rank | 7 |
time window | 7 |
different parameters | 7 |
stable whenever | 7 |
new york | 7 |
dependent control | 7 |
best fit | 7 |
mild symptoms | 7 |
active infected | 7 |
healthy cells | 7 |
sars coronavirus | 7 |
rapid testing | 7 |
bifurcation analysis | 7 |
order systems | 7 |
contact network | 7 |
reduce social | 7 |
present paper | 7 |
residual series | 7 |
infected compartment | 7 |
lipschitz condition | 7 |
stochastic process | 7 |
recovered population | 7 |
fast system | 7 |
epidemic starts | 7 |
million people | 7 |
maximum principle | 7 |
nonsingular kernel | 7 |
commonly used | 7 |
references therein | 7 |
unique endemic | 7 |
strict lockdown | 7 |
isolated individuals | 7 |
baleanu derivative | 7 |
learning algorithms | 7 |
laplace transform | 7 |
viral load | 7 |
projection using | 7 |
population density | 7 |
vaccination cost | 7 |
may th | 7 |
dimensional sphere | 7 |
data available | 7 |
birth process | 7 |
previous section | 7 |
risk factors | 7 |
final state | 7 |
recent studies | 7 |
reported symptomatic | 7 |
matrix method | 7 |
optimality conditions | 7 |
boundary control | 7 |
general incidence | 7 |
learning approaches | 7 |
saudi arabia | 7 |
actions taken | 7 |
world data | 7 |
least square | 7 |
boundary value | 7 |
rank correlation | 7 |
optimal controls | 7 |
imperfect lockdown | 7 |
unaware infectives | 7 |
continuously evolving | 7 |
total cumulative | 7 |
regression model | 7 |
total infections | 7 |
major cities | 7 |
leffler function | 7 |
recovery rates | 7 |
unique positive | 7 |
hospitalized patients | 7 |
long term | 7 |
parameter estimates | 7 |
exact solution | 7 |
northern italy | 7 |
statistical analysis | 7 |
distribution function | 7 |
days since | 7 |
updated estimation | 7 |
age group | 7 |
coronavirus epidemic | 7 |
green curve | 7 |
qualitative analysis | 7 |
us consider | 7 |
performance measures | 7 |
face mask | 7 |
unquarantined asymptomatic | 7 |
compulsory containment | 7 |
positive equilibria | 7 |
march th | 7 |
new approach | 7 |
vaccination strategy | 7 |
accumulated number | 7 |
will become | 7 |
logistic growth | 7 |
disease control | 7 |
reported infectious | 7 |
control reproduction | 7 |
exposed people | 7 |
integral equations | 7 |
center manifold | 7 |
random walk | 7 |
per unit | 7 |
forecasting using | 7 |
following fractional | 7 |
order system | 7 |
interaction radius | 7 |
main results | 7 |
global financial | 7 |
interactive topic | 6 |
will lead | 6 |
many governments | 6 |
fourth equation | 6 |
computed tomography | 6 |
memory impact | 6 |
hong kong | 6 |
governmental action | 6 |
two months | 6 |
discrete time | 6 |
learning models | 6 |
induced optimization | 6 |
root mean | 6 |
local asymptotic | 6 |
stochastic simulations | 6 |
data curation | 6 |
will decrease | 6 |
european countries | 6 |
different states | 6 |
continuous curves | 6 |
preventive isolation | 6 |
dt corr | 6 |
without control | 6 |
model parameter | 6 |
human body | 6 |
external factors | 6 |
positive constants | 6 |
integral equation | 6 |
currently infected | 6 |
intellectual property | 6 |
infection dynamics | 6 |
three major | 6 |
population i | 6 |
fractional operator | 6 |
infection transmission | 6 |
sis model | 6 |
control interventions | 6 |
blood samples | 6 |
disease outbreak | 6 |
different ways | 6 |
compartment i | 6 |
forecasting models | 6 |
will increase | 6 |
event rate | 6 |
ten days | 6 |
data points | 6 |
respiratory droplets | 6 |
yule process | 6 |
using reduction | 6 |
order epidemic | 6 |
outbreak size | 6 |
host population | 6 |
quarantine period | 6 |
type fractional | 6 |
epidemic analysis | 6 |
data used | 6 |
induced death | 6 |
fractional integro | 6 |
overall population | 6 |
reproduction ratio | 6 |
series model | 6 |
spread among | 6 |
western countries | 6 |
cases generated | 6 |
dynamical behavior | 6 |
control function | 6 |
hypercube sampling | 6 |
limited resources | 6 |
individual may | 6 |
positive covid | 6 |
driven receding | 6 |
health measures | 6 |
following expression | 6 |
following results | 6 |
three distinct | 6 |
model dynamics | 6 |
african countries | 6 |
infectious class | 6 |
input variables | 6 |
infection will | 6 |
forward sensitivity | 6 |
gaussian process | 6 |
recovered patients | 6 |
different population | 6 |
results found | 6 |
classical sir | 6 |
medical professionals | 6 |
overall number | 6 |
longer time | 6 |
equation model | 6 |
hospital beds | 6 |
cumulative reported | 6 |
asymptomatic duration | 6 |
every time | 6 |
health systems | 6 |
individual reaction | 6 |
adjusted estimation | 6 |
series prediction | 6 |
social connections | 6 |
lyapunov exponent | 6 |
per people | 6 |
controlling covid | 6 |
tamil nadu | 6 |
undocumented infection | 6 |
aerosol sanitizer | 6 |
model predicts | 6 |
steady state | 6 |
waiting time | 6 |
following equations | 6 |
exponential decay | 6 |
individual i | 6 |
poisson distribution | 6 |
coronavirus infection | 6 |
homogeneous mixing | 6 |
fatality rates | 6 |
epidemic growth | 6 |
hand side | 6 |
antiviral drugs | 6 |
sensitive parameters | 6 |
symptomatic cases | 6 |
switching times | 6 |
vkr chimmula | 6 |
noise intensity | 6 |
better understanding | 6 |
seir models | 6 |
based model | 6 |
order models | 6 |
disease persists | 6 |
control policy | 6 |
population phases | 6 |
local outbreak | 6 |
isolation factor | 6 |
chaotic systems | 6 |
wide interventions | 6 |
model used | 6 |
differential operators | 6 |
control analysis | 6 |
time fractional | 6 |
will reduce | 6 |
two groups | 6 |
national natural | 6 |
heterogeneous populations | 6 |
data collected | 6 |
endemic disease | 6 |
two scenarios | 6 |
infected country | 6 |
approximate solution | 6 |
may help | 6 |
three countries | 6 |
vaccine cost | 6 |
various operating | 6 |
sensitivity index | 6 |
late december | 6 |
per capita | 6 |
susceptibility feedback | 6 |
wearing face | 6 |
declared covid | 6 |
order sei | 6 |
spreading processes | 6 |
data driven | 6 |
virus tests | 6 |
factors like | 6 |
critical model | 6 |
five countries | 6 |
disease characteristics | 6 |
new outbreak | 6 |
confirmed case | 6 |
shahid nadim | 6 |
degree distribution | 6 |
significant parameters | 6 |
human liver | 6 |
health emergency | 6 |
induced mortality | 6 |
vaccination policy | 6 |
zero eigenvalue | 6 |
remedial steps | 6 |
research work | 6 |
standard sir | 6 |
distancing rule | 6 |
time system | 6 |
will provide | 6 |
time data | 6 |
following two | 6 |
heat transfer | 6 |
virus will | 6 |
coupling within | 6 |
dynamic behavior | 6 |
novel approach | 6 |
new training | 6 |
evolving training | 6 |
remains constant | 6 |
one month | 6 |
contact networks | 6 |
three months | 6 |
manifold theory | 6 |
stochastic calculus | 6 |
five days | 6 |
social mixing | 6 |
natural immunization | 6 |
mapping function | 6 |
systematic review | 6 |
epidemics trend | 6 |
density function | 6 |
secondary infection | 6 |
logistic equation | 6 |
population biology | 6 |
public opinion | 6 |
virus test | 6 |
restrictive measures | 6 |
hospitalized infected | 6 |
normalized forward | 6 |
systems biology | 6 |
model calibration | 6 |
data analysis | 6 |
epidemic situation | 6 |
rna virus | 6 |
vector regression | 6 |
order calculus | 6 |
lundberg process | 5 |
chaotic system | 5 |
population phase | 5 |
people i | 5 |
control model | 5 |
publicly reported | 5 |
detailed study | 5 |
singular fractional | 5 |
new study | 5 |
new type | 5 |
see figs | 5 |
infection facilitates | 5 |
infection period | 5 |
based approaches | 5 |
greatest potential | 5 |
invariant set | 5 |
testing positive | 5 |
horizon control | 5 |
hemorrhagic fever | 5 |
contacts among | 5 |
us denote | 5 |
day basis | 5 |
wavelet function | 5 |
since april | 5 |
large set | 5 |
numerical approximation | 5 |
weather variables | 5 |
display mode | 5 |
pcr testing | 5 |
multiplex networks | 5 |
driven infectious | 5 |
predicting covid | 5 |
constant value | 5 |
better performance | 5 |
artificial neural | 5 |
analysis method | 5 |
generated using | 5 |
may also | 5 |
air travel | 5 |
data mining | 5 |
potential impact | 5 |
quarantine class | 5 |
will use | 5 |
nonlinear system | 5 |
positive root | 5 |
compartmental mathematical | 5 |
four countries | 5 |
latin hypercube | 5 |
least squares | 5 |
higher compared | 5 |
real contagion | 5 |
influential parameters | 5 |
stochastic sir | 5 |
hybridized mode | 5 |
next section | 5 |
extended legendre | 5 |
sample forecasts | 5 |
epidemic trend | 5 |
limited data | 5 |
host will | 5 |
controlled natural | 5 |
john hopkins | 5 |
bifurcation phenomenon | 5 |
boundary conditions | 5 |
deep neural | 5 |
relevant departments | 5 |
clusters distributed | 5 |
infectious period | 5 |
significantly affect | 5 |
one sentence | 5 |
financial support | 5 |
vanishing moments | 5 |
may occur | 5 |
second interval | 5 |
decision makers | 5 |
segmented poisson | 5 |
parameters using | 5 |
individuals greatly | 5 |
integral operators | 5 |
optimal solution | 5 |
first stage | 5 |
large scale | 5 |
also shown | 5 |
million deaths | 5 |
proposed sirsi | 5 |
unknown parameters | 5 |
results obtained | 5 |
voluntary vaccination | 5 |
vaccine production | 5 |
delayed sir | 5 |
memory cell | 5 |
future cases | 5 |
dynamics model | 5 |
prompt isolation | 5 |
specify contribution | 5 |
deterministic models | 5 |
complex models | 5 |
endemic state | 5 |
time profile | 5 |
two weeks | 5 |
world problems | 5 |
also given | 5 |
wuhan novel | 5 |
using three | 5 |
using fractional | 5 |
model equations | 5 |
nationwide lockdown | 5 |
infected covid | 5 |
natural recovery | 5 |
notified cases | 5 |
transfer model | 5 |
covid cases | 5 |
infectious population | 5 |
organizing maps | 5 |
important parameters | 5 |
strict control | 5 |
birth rate | 5 |
imperfect vaccination | 5 |
peak time | 5 |
research area | 5 |
temporal components | 5 |
individuals increases | 5 |
early epidemic | 5 |
term forecasting | 5 |
advises orders | 5 |
model takes | 5 |
future work | 5 |
sufficiently small | 5 |
respiratory illness | 5 |
algorithm based | 5 |
optimization algorithm | 5 |
model described | 5 |
spreading probability | 5 |
future covid | 5 |
structured models | 5 |
healthy individuals | 5 |
important role | 5 |
singular arc | 5 |
detection probability | 5 |
global dynamics | 5 |
will give | 5 |
asymptomatic carrier | 5 |
poisson process | 5 |
present research | 5 |
vaccination game | 5 |
march nd | 5 |
weakly singular | 5 |
threshold quantity | 5 |
discharged recovered | 5 |
scientific production | 5 |
time fde | 5 |
wavelet systems | 5 |
based tools | 5 |
rapid dissemination | 5 |
classical seir | 5 |
disease models | 5 |
model analysis | 5 |
submission system | 5 |
fractional orders | 5 |
implementing control | 5 |
recovered class | 5 |
deterministic chaos | 5 |
key parameters | 5 |
visualization display | 5 |
ai technology | 5 |
medical experts | 5 |
lockdown efficacy | 5 |
population till | 5 |
pharmaceutical scenarios | 5 |
pandemic due | 5 |
less effective | 5 |
induced errors | 5 |
human behaviour | 5 |
epidemiological characteristics | 5 |
several countries | 5 |
mathematical biology | 5 |
analysis using | 5 |
control set | 5 |
time scale | 5 |
partial derivatives | 5 |
network visualization | 5 |
gets infected | 5 |
scientific databases | 5 |
intra zone | 5 |
also considered | 5 |
distribution functions | 5 |
cfd analysis | 5 |
pcr tests | 5 |
heat kernel | 5 |
actual number | 5 |
spatial spread | 5 |
imported cases | 5 |
state space | 5 |
transition density | 5 |
model considers | 5 |
fixed point | 5 |
drug development | 5 |
active infections | 5 |
different times | 5 |
series datasets | 5 |
susceptible populations | 5 |
agent i | 5 |
mitigation scenarios | 5 |
learning methods | 5 |
test results | 5 |
nonlinear systems | 5 |
spread rate | 5 |
january th | 5 |
significant impact | 5 |
like covid | 5 |
standard deviation | 5 |
poisson model | 5 |
recruitment rate | 5 |
exponential increase | 5 |
random variables | 5 |
simple sir | 5 |
increasing number | 5 |
sufficient conditions | 5 |
substantial undocumented | 5 |
two equations | 5 |
heterogeneous networks | 5 |
longer period | 5 |
stochastic sirs | 5 |
daily tests | 5 |
short period | 5 |
system given | 5 |
spherical coordinates | 5 |
symptomatic class | 5 |
input data | 5 |
monte carlo | 5 |
measures adopted | 5 |
aids people | 5 |
measures taken | 5 |
secondary infections | 5 |
first order | 5 |
better understand | 5 |
purely asymptomatic | 5 |
order sir | 5 |
reduce covid | 5 |
time estimation | 5 |
critical parameters | 5 |
initial phase | 5 |
government actions | 5 |
much faster | 5 |
early stages | 5 |
negative initial | 5 |
viral pneumonia | 5 |
becomes susceptible | 5 |
positive constant | 5 |
registered infected | 5 |
much smaller | 5 |
hospitalized class | 5 |
predictive mathematical | 5 |
travel restrictions | 5 |
please see | 5 |
different time | 5 |
capacity constraints | 5 |
infectious spread | 5 |
countries like | 5 |
rapid tests | 5 |
morocco registers | 5 |
cumulative total | 5 |
social circle | 5 |
vaccination strategies | 5 |
infection age | 5 |
exposed population | 5 |
fractional modelling | 5 |
reported confirmed | 5 |
one root | 5 |
model shows | 5 |
immune response | 5 |
grant nos | 5 |
new year | 5 |
scratch every | 5 |
topic search | 5 |
editorial submission | 5 |
immunization rate | 5 |
scientific literature | 5 |
disease status | 5 |
asymptomatic carriers | 5 |
using data | 5 |
clinical blood | 5 |
volterra integral | 5 |
awareness source | 5 |
optimal quarantine | 5 |
control profile | 5 |
four control | 5 |
model given | 5 |
forget gate | 5 |
host virus | 5 |
contagion dynamics | 4 |
quarantined infectious | 4 |
contacts tracing | 4 |
topic popularity | 4 |
disease prevention | 4 |
jacobo aguirre | 4 |
term predictions | 4 |
cases increases | 4 |
home advises | 4 |
tests applied | 4 |
huge amount | 4 |
without loss | 4 |
government officials | 4 |
decision support | 4 |
getting infected | 4 |
information generation | 4 |
severe respiratory | 4 |
exposed compartment | 4 |
multiplicative fractional | 4 |
also presented | 4 |
scientific community | 4 |
simulations show | 4 |
lockdown measure | 4 |
formal analysis | 4 |
information spreading | 4 |
seira model | 4 |
growth model | 4 |
transition sst | 4 |
image processing | 4 |
method based | 4 |
various fractional | 4 |
stochastic systems | 4 |
will remain | 4 |
develop immunity | 4 |
pcr test | 4 |
individual vaccination | 4 |
united kingdom | 4 |
field approach | 4 |
rows represents | 4 |
aware infected | 4 |
much higher | 4 |
china early | 4 |
laboratory tests | 4 |
among people | 4 |
distancing policy | 4 |
key features | 4 |
mental health | 4 |
sufficient condition | 4 |
model system | 4 |
stochastic mathematical | 4 |
governmental actions | 4 |
different control | 4 |
epidemic processes | 4 |
similar way | 4 |
mutually exclusive | 4 |
statistical tests | 4 |
distance correlation | 4 |
heterogeneous population | 4 |
mobilization factors | 4 |
two states | 4 |
random forest | 4 |
great interest | 4 |
section discusses | 4 |
health concern | 4 |
novel model | 4 |
economic crisis | 4 |
response function | 4 |
influenza epidemics | 4 |
simplest sir | 4 |
ib satisfying | 4 |
mathematical formulation | 4 |
functional form | 4 |
caputo operator | 4 |
pneumonia associated | 4 |
first four | 4 |
simulation result | 4 |
solved using | 4 |
haggle network | 4 |
always exists | 4 |
infection model | 4 |
equations numerical | 4 |
quadratic approximation | 4 |
new fractal | 4 |
initially infected | 4 |
small increase | 4 |
model proposed | 4 |
basic sir | 4 |
mean number | 4 |
worth noting | 4 |
worst hit | 4 |
mathematical study | 4 |
refinable masks | 4 |
hub nodes | 4 |
input gate | 4 |
international journal | 4 |
temporal evolution | 4 |
many cases | 4 |
urn model | 4 |
personal hygiene | 4 |
care unit | 4 |
probability function | 4 |
regular lattice | 4 |
different techniques | 4 |
bifurcation coefficient | 4 |
system response | 4 |
integer derivatives | 4 |
river delta | 4 |
recovered people | 4 |
evaluate non | 4 |
optimal isolation | 4 |
exact solutions | 4 |
disease propagation | 4 |
take place | 4 |
applied quarantine | 4 |
stability theorem | 4 |
current trend | 4 |
rate increases | 4 |
fractional dynamics | 4 |
predicted values | 4 |
automated detection | 4 |
approaches applied | 4 |
fractional integrals | 4 |
linear systems | 4 |
minimum value | 4 |
corrector approach | 4 |
stochastic threshold | 4 |
adjoint variables | 4 |
epidemic evolution | 4 |
air released | 4 |
probability rate | 4 |
operator approach | 4 |
digital contact | 4 |
disease symptoms | 4 |
death evolution | 4 |
accurately predict | 4 |
threshold behavior | 4 |
current data | 4 |
coronavirus spreading | 4 |
expected values | 4 |
travel ban | 4 |
different models | 4 |
positive definite | 4 |
whole world | 4 |
chart representation | 4 |
namely susceptible | 4 |
transmission process | 4 |
many applications | 4 |
real number | 4 |
sirv model | 4 |
three times | 4 |
small influx | 4 |
bibliographic databases | 4 |
conditioned air | 4 |
different regions | 4 |
nonlinear least | 4 |
medical treatment | 4 |
model gives | 4 |
completely susceptible | 4 |
matlab software | 4 |
symptomatic infection | 4 |
models used | 4 |
important parameter | 4 |
eight lyapunov | 4 |
health status | 4 |
state equations | 4 |
disease growth | 4 |
model building | 4 |
spread relationships | 4 |
deaths due | 4 |
making use | 4 |
main objective | 4 |
pandemic date | 4 |
model output | 4 |
invariance principle | 4 |
compartmental epidemic | 4 |
following eigenvalue | 4 |
intellectual content | 4 |
credit author | 4 |
order necessary | 4 |
unlike typical | 4 |
volterra integro | 4 |
survival time | 4 |
rigorous mathematical | 4 |
wide range | 4 |
hospital isolation | 4 |
basic definitions | 4 |
positive initial | 4 |
previous works | 4 |
stochastic processes | 4 |
graph theory | 4 |
sir dynamics | 4 |
sports competitions | 4 |
conceptual model | 4 |
precaution measure | 4 |
biologically feasible | 4 |
series dataset | 4 |
proper precaution | 4 |
sourav rana | 4 |
theoretical framework | 4 |
coronavirus cases | 4 |
model state | 4 |
output array | 4 |
calculated using | 4 |
mobility patterns | 4 |
networks epidemic | 4 |
output gate | 4 |
single topic | 4 |
based approach | 4 |
effective vaccine | 4 |
left eigenvector | 4 |
mutual contacts | 4 |
hiv viral | 4 |
one example | 4 |
coronavirus diseases | 4 |
mean field | 4 |
extinct cases | 4 |
optimal regression | 4 |
isolation rooms | 4 |
forecast model | 4 |
constant parameters | 4 |
many different | 4 |
aware infectives | 4 |
vaccine development | 4 |
disease induced | 4 |
distancing rules | 4 |
transport equations | 4 |
human lives | 4 |
current state | 4 |
full normalizations | 4 |
several epidemic | 4 |
variable importance | 4 |
integer model | 4 |
two parameters | 4 |
solved numerically | 4 |
government interventions | 4 |
based covid | 4 |
moroccan government | 4 |
cumulative infected | 4 |
contacts per | 4 |
cfr estimates | 4 |
airborne transmission | 4 |
early estimation | 4 |
informed susceptible | 4 |
model sipherd | 4 |
following assumptions | 4 |
based dashboard | 4 |
actual incidence | 4 |
six locations | 4 |
growth curves | 4 |
morocco announces | 4 |
case without | 4 |
discharge rate | 4 |
adaptive edge | 4 |
potential spread | 4 |
infection spread | 4 |
particle tracking | 4 |
average level | 4 |
normalized susceptible | 4 |
sars epidemic | 4 |
outbreak forecasting | 4 |
also possible | 4 |
important factors | 4 |
discharge recovery | 4 |
individuals may | 4 |
future time | 4 |
law kernel | 4 |
potential covid | 4 |
healthcare expert | 4 |
called seir | 4 |
named authors | 4 |
february th | 4 |
world using | 4 |
accommodate surges | 4 |
also show | 4 |
care workers | 4 |
similar scenario | 4 |
solving fractional | 4 |
like models | 4 |
wavelet transform | 4 |
using ct | 4 |
dependent mass | 4 |
main contribution | 4 |
model variables | 4 |
infection immunization | 4 |
different disease | 4 |
many mathematical | 4 |
equilibrium state | 4 |
nearest neighbors | 4 |
breaking process | 4 |
asymptomatic proportion | 4 |
disease within | 4 |
linear function | 4 |
known infected | 4 |
continuous dynamic | 4 |
lockdown measures | 4 |
tested positive | 4 |
th day | 4 |
average death | 4 |
analysis will | 4 |
integral operator | 4 |
based modeling | 4 |
outbreak will | 4 |
positive solution | 4 |
arima models | 4 |
bifurcation parameter | 4 |
close contacts | 4 |
transmission parameters | 4 |
significantly different | 4 |
traditional time | 4 |
social contact | 4 |
i i | 4 |
cases using | 4 |
epidemic curve | 4 |
positive roots | 4 |
seafood market | 4 |
fde models | 4 |
severe symptoms | 4 |
interactive web | 4 |
model global | 4 |
within one | 4 |
model states | 4 |
experimental data | 4 |
probability theory | 4 |
using social | 4 |
eigenvalue problem | 4 |
current situation | 4 |
medical resources | 4 |
linear growth | 4 |
crude oil | 4 |
author statement | 4 |
may reduce | 4 |
ic units | 4 |
gamma function | 4 |
infected female | 4 |
ai prediction | 4 |
canada using | 4 |
controlled interventions | 4 |
long run | 4 |
severe covid | 4 |
dl models | 4 |
many important | 4 |
new numerical | 4 |
care systems | 4 |
diagnose covid | 4 |
lockdown save | 4 |
reaction mechanism | 4 |
present work | 4 |
cool air | 4 |
specific vaccine | 4 |
dynamic system | 4 |
dirichlet allocation | 4 |
th march | 4 |
equilibrium solutions | 4 |
cases may | 4 |
wide lockdown | 4 |
space gaussian | 4 |
lda model | 4 |
evaluated using | 4 |
using arima | 4 |
contacting among | 4 |
quarantine may | 4 |
future research | 4 |
modelling approach | 4 |
daily life | 4 |
unit root | 4 |
reference gene | 4 |
numerical value | 4 |
three states | 4 |
ncbi genbank | 4 |
slow time | 4 |
stable covid | 4 |
first phase | 4 |
ebola disease | 4 |
models may | 4 |
three compartments | 4 |
control intervention | 4 |
infection seeded | 4 |
order covid | 4 |
newton polynomial | 4 |
parameter describing | 4 |
column list | 4 |
high accuracy | 4 |
type model | 4 |
lstm network | 4 |
model allows | 4 |
hidden layer | 4 |
model exhibits | 4 |
india using | 4 |
countries across | 4 |
si model | 4 |
susceptible case | 4 |
healthcare experts | 4 |
daily covid | 4 |
latent dirichlet | 4 |
type i | 4 |
incidence curves | 4 |
average period | 4 |
obtained using | 4 |
first one | 4 |
term prediction | 4 |
viral infections | 4 |
familial cluster | 4 |
sanitizing machine | 4 |
equations fractional | 4 |
several mathematical | 4 |
financial interest | 4 |
take care | 4 |
across cryptocurrency | 4 |
time since | 4 |
feedback gain | 4 |
collected data | 4 |
ten countries | 4 |
numerical examples | 4 |
transversality conditions | 4 |
daily reported | 4 |
major western | 4 |
people infected | 4 |
analytical solution | 4 |
may change | 4 |
applied mathematics | 4 |
threshold endemic | 4 |
disease threshold | 4 |
abc derivative | 4 |
patients using | 4 |
temporal reclassification | 4 |
current outbreak | 4 |
subthreshold endemic | 4 |
etiological agent | 4 |
pneumonia outbreak | 4 |
cured recovered | 4 |
serious threat | 4 |
immunization ratio | 4 |
continuously differentiable | 4 |
nonlinear dynamical | 4 |
model prediction | 4 |
strict measures | 4 |
ic class | 4 |
effective contacts | 4 |
red curve | 4 |
population compartmentalization | 4 |
conservation law | 4 |
mean absolute | 4 |
treatment strategies | 4 |
sis epidemic | 4 |
telegraph noise | 4 |
healthcare demand | 4 |
error series | 4 |
maximum i | 4 |
confined individuals | 4 |
general fractional | 4 |
current study | 4 |
diffusion process | 4 |
current lockdown | 4 |
new definition | 4 |
novel virus | 4 |
prediction models | 4 |
daily cases | 4 |
scenario assumes | 4 |
one country | 4 |
quarantined infected | 4 |
citations received | 4 |
final time | 4 |
reported covid | 4 |
lockdown scenarios | 4 |
dependent sir | 4 |
error measures | 4 |
unreported infected | 4 |
following scheme | 4 |
unreported cases | 4 |
save mankind | 4 |
prime minister | 4 |
effective treatment | 4 |
derivative operator | 4 |
infectious dynamics | 4 |
generate short | 4 |
different levels | 4 |
vaccination decision | 4 |
daily number | 4 |
different groups | 4 |
polymerase chain | 4 |
random testing | 4 |
serial interval | 4 |
daily notified | 4 |
significant financial | 4 |
decreased due | 4 |
state constraint | 4 |
epidemic predictions | 4 |
shorter time | 4 |
complex dynamical | 4 |
without optimal | 4 |
growth phase | 4 |
half normalizations | 4 |
positively invariant | 4 |
fractional integration | 4 |
analytical results | 4 |
model including | 4 |
quarantined asymptomatic | 4 |
useful tool | 4 |
two cases | 4 |
dynamical modeling | 4 |
people versus | 4 |
compartment models | 4 |
people living | 4 |
free network | 4 |
harmonic oscillator | 4 |
current infected | 4 |
disease covid | 4 |
related coronavirus | 4 |
threshold value | 4 |
till now | 4 |
bimolecular reaction | 4 |
markov process | 4 |
severely infected | 4 |
respiratory disease | 4 |
natural immunity | 4 |
caputo derivatives | 4 |
will last | 4 |
track covid | 4 |
till time | 4 |
major government | 4 |
sexual contact | 4 |
initial time | 4 |
best fits | 4 |
makes sense | 4 |
implemented using | 4 |
antibody responses | 4 |
scientific research | 4 |
previous work | 4 |
community structure | 4 |
family members | 4 |
tracing process | 4 |
home quarantine | 4 |
mutual feedback | 4 |
important intellectual | 4 |
high number | 4 |
go outside | 4 |
countries using | 4 |
infectious equilibrium | 4 |
transmission potential | 4 |
roughly logistic | 4 |
enable people | 4 |
beds per | 4 |
chain reaction | 4 |
infected neighbors | 4 |
bar chart | 4 |
markets exhibit | 4 |
general public | 4 |
topic modeling | 4 |
progression rate | 4 |
possible control | 4 |
fit parameters | 4 |
countries affected | 4 |
notified case | 4 |
sirs epidemic | 4 |
chinese center | 4 |
infected populations | 4 |
logistic regression | 4 |
dynamical behaviors | 4 |
mathematical assessment | 4 |
modeling approaches | 4 |
researchers found | 4 |
unreported individuals | 4 |
available online | 4 |
new zealand | 4 |
affected states | 4 |
including death | 4 |
will take | 4 |
sirvd model | 4 |
initial number | 4 |
infectious players | 4 |
careful reading | 4 |
may conclude | 4 |
compartmental sir | 4 |
will always | 4 |
hybrid approach | 4 |
quarantine rate | 4 |
control covid | 4 |
structured population | 4 |
nonlinear ordinary | 4 |
individuals infected | 4 |
till april | 3 |
declares global | 3 |
containment zones | 3 |
external source | 3 |
substantial increase | 3 |
cabo verde | 3 |
describe biological | 3 |
data considering | 3 |
linking within | 3 |
new registered | 3 |
statistical approaches | 3 |
maintaining social | 3 |
predictions using | 3 |
transmission mechanism | 3 |
southeast asia | 3 |
tracing application | 3 |
full dataset | 3 |
epidemic continues | 3 |
discovery models | 3 |
yet reached | 3 |
isolated fast | 3 |
individuals develop | 3 |
environmental noise | 3 |
ultimately bounded | 3 |
natural mortality | 3 |
economic activities | 3 |
study also | 3 |
different delays | 3 |
will continue | 3 |
two equilibria | 3 |
type mathematical | 3 |
endemic steady | 3 |
negative concavity | 3 |
numerically model | 3 |
based algorithm | 3 |
certain conditions | 3 |
daily data | 3 |
hospitalized covid | 3 |
done per | 3 |
epidemic spreads | 3 |
leffler functions | 3 |
close contact | 3 |
equivalent increase | 3 |
patients based | 3 |
data shows | 3 |
hiv epidemics | 3 |
topic searches | 3 |
around june | 3 |
wide variety | 3 |
various public | 3 |
greatly decrease | 3 |
final approval | 3 |
great variability | 3 |
state system | 3 |
parameter value | 3 |
decisions based | 3 |
vaccine availability | 3 |
intelligence approach | 3 |
relevant literature | 3 |
singular control | 3 |
days april | 3 |
ft tg | 3 |
asymptomatic will | 3 |
death factor | 3 |
search engine | 3 |
next theorem | 3 |
become smir | 3 |
match day | 3 |
exposed individual | 3 |
downward trend | 3 |
one week | 3 |
also performed | 3 |
adjustable parameters | 3 |
anonymous referees | 3 |
among heterogeneous | 3 |
markov models | 3 |
wilt disease | 3 |
model time | 3 |
temporary immunity | 3 |
lyapunov stability | 3 |
disease starts | 3 |
respiratory system | 3 |
estimated using | 3 |
newly developed | 3 |
multiple countries | 3 |
global crisis | 3 |
asian countries | 3 |
carrying capacity | 3 |
substitution mutation | 3 |
value problems | 3 |
second term | 3 |
immunization rates | 3 |
different epidemic | 3 |
person will | 3 |
scientific data | 3 |
second half | 3 |
dashed curve | 3 |
allow us | 3 |
static network | 3 |
analyses based | 3 |
deterministic behavior | 3 |
local sensitivity | 3 |
contagious diseases | 3 |
random variable | 3 |
homotopy analysis | 3 |
time varying | 3 |
basic mathematical | 3 |
high level | 3 |
recovery time | 3 |
using eq | 3 |
fractional mathematical | 3 |
active infectious | 3 |
paper also | 3 |
primary infection | 3 |
target interaction | 3 |
lim inf | 3 |
important lessons | 3 |
model fits | 3 |
football league | 3 |
model eliminates | 3 |
compartment model | 3 |
hospitalized individuals | 3 |
treatment functions | 3 |
operating procedure | 3 |
rate prediction | 3 |
singular operator | 3 |
epidemiologically associated | 3 |
estimate effectiveness | 3 |
canadian health | 3 |
ending point | 3 |
last section | 3 |
leffler derivative | 3 |
valuable comments | 3 |
next wave | 3 |
nonlocal kernel | 3 |
model eq | 3 |
hcv coinfection | 3 |
negative prccs | 3 |
results suggest | 3 |
coupled ordinary | 3 |
epidemic dynamic | 3 |
draft preparation | 3 |
will result | 3 |
potential topics | 3 |
mentioned time | 3 |
epidemic like | 3 |
sequential irregularity | 3 |
model also | 3 |
curve fitting | 3 |
predicted cumulative | 3 |
transitional flows | 3 |
avoid contacts | 3 |
days starting | 3 |
regression models | 3 |
blood test | 3 |
various methodologies | 3 |
epidemic time | 3 |
experience gained | 3 |
future development | 3 |
essential causal | 3 |
practical reasons | 3 |
day i | 3 |
treatment control | 3 |
novel sars | 3 |
i represents | 3 |
autocorrelation function | 3 |
capita rate | 3 |
model date | 3 |
large part | 3 |
spreading significantly | 3 |
pandemic across | 3 |
containment strategies | 3 |
proposed mathematical | 3 |
small number | 3 |
detected including | 3 |
american countries | 3 |
previous models | 3 |
case data | 3 |
term immunity | 3 |
get infected | 3 |
free steady | 3 |
mean incubation | 3 |
time frame | 3 |
based method | 3 |
positive value | 3 |
data collection | 3 |
modeling biological | 3 |
rate given | 3 |
renal syndrome | 3 |
two consecutive | 3 |
infectiousness rate | 3 |
first wave | 3 |
absolutely continuous | 3 |
since infection | 3 |
country lockdown | 3 |
activation function | 3 |
using different | 3 |
model reduction | 3 |
better control | 3 |
show symptoms | 3 |
comparatively strict | 3 |
brownian motions | 3 |
potential conflicts | 3 |
linear algebra | 3 |
health organizations | 3 |
dynamic evolution | 3 |
coupled within | 3 |
data stream | 3 |
many others | 3 |
sirs system | 3 |
left hand | 3 |
based time | 3 |
open problem | 3 |
rich dynamics | 3 |
family cluster | 3 |
lockdown till | 3 |
latency period | 3 |
stochastic coronavirus | 3 |
hurwitz conditions | 3 |
linearizing system | 3 |
dark green | 3 |
success rate | 3 |
birth processes | 3 |
fractional sirc | 3 |
similar procedure | 3 |
literature review | 3 |
partial derivative | 3 |
cost function | 3 |
individual becomes | 3 |
dynamical models | 3 |
state concerning | 3 |
many real | 3 |
direct adjoining | 3 |
testing kits | 3 |
right side | 3 |
values used | 3 |
mechanistic models | 3 |
number using | 3 |
positive result | 3 |
minimum principle | 3 |
health ministry | 3 |
people across | 3 |
different approaches | 3 |
mean squared | 3 |
additional condition | 3 |
population growth | 3 |
emotional expressions | 3 |
without detected | 3 |
removal time | 3 |
candidate function | 3 |
control variable | 3 |
fundamental theorem | 3 |
lockdown success | 3 |
modeling results | 3 |
two peaks | 3 |
zero gives | 3 |
test per | 3 |
statistical behaviour | 3 |
fraction derivative | 3 |
time forecasting | 3 |
distributed randomly | 3 |
low death | 3 |
namely april | 3 |
later time | 3 |
population mobility | 3 |
compound poisson | 3 |
memory effects | 3 |
market contagion | 3 |
local coordinates | 3 |
virus spreading | 3 |
slightly different | 3 |
mathematical condition | 3 |
transmissibility multiple | 3 |
airborne infection | 3 |
fredholm integral | 3 |
incidence function | 3 |
real life | 3 |
epidemic modelling | 3 |
gradually decreased | 3 |
group i | 3 |
quarantined class | 3 |
model forecast | 3 |
market risk | 3 |
infected one | 3 |
original distribution | 3 |
drug vaccine | 3 |
organization coronavirus | 3 |
sliding window | 3 |
modified sir | 3 |
susceptible humans | 3 |
first half | 3 |
within communities | 3 |
first consider | 3 |
input parameter | 3 |
key point | 3 |
first infection | 3 |
potentially infected | 3 |
world network | 3 |
many ways | 3 |
many people | 3 |
diagnosed patients | 3 |
academic publications | 3 |
polya process | 3 |
infection clusters | 3 |
lipschitz continuous | 3 |
interactions among | 3 |
population level | 3 |
asymptomatic contact | 3 |
modelling strategies | 3 |
second problem | 3 |
system eight | 3 |
control system | 3 |
time gaps | 3 |
involves four | 3 |
regime switching | 3 |
model suggested | 3 |
human populations | 3 |
dynamics covid | 3 |
squares algorithm | 3 |
strict quarantine | 3 |
measures like | 3 |
network structures | 3 |
may affect | 3 |
susceptible neighbors | 3 |
national health | 3 |
based lstm | 3 |
already infected | 3 |
test days | 3 |
linear approximation | 3 |
profoundly affected | 3 |
populated areas | 3 |
provide insights | 3 |
low incidence | 3 |
unknown etiology | 3 |
virus among | 3 |
infective population | 3 |
first reported | 3 |
similar degree | 3 |
containment explains | 3 |
rate may | 3 |
unstable equilibrium | 3 |
early prediction | 3 |
fractional features | 3 |
local asymptotically | 3 |
tree model | 3 |
geographical structure | 3 |
complex systems | 3 |
easily obtain | 3 |
corresponding jacobian | 3 |
death rates | 3 |
bifurcation diagram | 3 |
delay dynamic | 3 |
keep away | 3 |
better visualization | 3 |
weather data | 3 |
individuals decreases | 3 |
solving nonlinear | 3 |
improve actions | 3 |
metapopulation models | 3 |
total time | 3 |
saturation parameter | 3 |
recent work | 3 |
first covid | 3 |
heterogeneous topology | 3 |
risk compartment | 3 |
treatment given | 3 |
china analysis | 3 |
survival mortality | 3 |
model parameterization | 3 |
derivative model | 3 |
model may | 3 |
nonlinear differential | 3 |
phase plane | 3 |
community containment | 3 |
pandemic spread | 3 |
cumulative distribution | 3 |
two basic | 3 |
term behavior | 3 |
virus pathogen | 3 |
detailed analysis | 3 |
immunity level | 3 |
really work | 3 |
three populations | 3 |
process regression | 3 |
based detergents | 3 |
random networks | 3 |
good hygiene | 3 |
critical patients | 3 |
study will | 3 |
based methods | 3 |
quarantined classes | 3 |
become symptomatic | 3 |
fractional hrsv | 3 |
got infected | 3 |
data sources | 3 |
population distribution | 3 |
social physical | 3 |
one will | 3 |
chaos theory | 3 |
physical interpretation | 3 |
order polynomial | 3 |
saturated treatment | 3 |
optimality system | 3 |
next days | 3 |
recent results | 3 |
local bifurcation | 3 |
epidemic end | 3 |
will apply | 3 |
solid curve | 3 |
four equations | 3 |
highly affected | 3 |
different factors | 3 |
many new | 3 |
engineering applications | 3 |
numerical methods | 3 |
state constraints | 3 |
cauchy problem | 3 |
provide decision | 3 |
value less | 3 |
modelling analysis | 3 |
may fail | 3 |
warning system | 3 |
exponential distribution | 3 |
fluid dynamics | 3 |
following parameter | 3 |
national league | 3 |
first positive | 3 |
immune responses | 3 |
ongoing epidemic | 3 |
deaths caused | 3 |
peak value | 3 |
transmission dynamic | 3 |
epidemiological implication | 3 |
boundary local | 3 |
disease system | 3 |
clinically ill | 3 |
room air | 3 |
hospital capacity | 3 |
wavelet decomposition | 3 |
outside china | 3 |
medical care | 3 |
including stability | 3 |
death time | 3 |
number formula | 3 |
performed well | 3 |
physical contacts | 3 |
stable around | 3 |
may still | 3 |
confidence intervals | 3 |
global positive | 3 |
environment contamination | 3 |
behavioural change | 3 |
always positive | 3 |
first confirmed | 3 |
control policies | 3 |
sirc model | 3 |
major public | 3 |
last part | 3 |
numerical computations | 3 |
uniform stability | 3 |
testing strategy | 3 |
nonlinear function | 3 |
model solutions | 3 |
numerical approach | 3 |
recent covid | 3 |
outbreak period | 3 |
undetected infections | 3 |
predicted cases | 3 |
exponential law | 3 |
lockdown scenario | 3 |
three classes | 3 |
rna viruses | 3 |
west nile | 3 |
present two | 3 |
average contact | 3 |
first term | 3 |
absolute error | 3 |
easily adapted | 3 |
contact among | 3 |
ran simulations | 3 |
fully understood | 3 |
effective lockdown | 3 |
epidemiology models | 3 |
biological carrier | 3 |
typical deep | 3 |
will go | 3 |
unknown hosts | 3 |
still unknown | 3 |
authors proposed | 3 |
daily basis | 3 |
eventually stabilize | 3 |
baleanu derivatives | 3 |
growth dynamics | 3 |
till today | 3 |
health commission | 3 |
health policies | 3 |
random process | 3 |
epidemic topics | 3 |
effective containment | 3 |
person becomes | 3 |
si links | 3 |
zero mean | 3 |
derivative called | 3 |
preventable diseases | 3 |
key critical | 3 |
officially reported | 3 |
mail address | 3 |
stochastic simulation | 3 |
extensive numerical | 3 |
organization declares | 3 |
transmission coefficient | 3 |
huge number | 3 |
chaotic dynamical | 3 |
covid epidemics | 3 |
solve fractional | 3 |
van den | 3 |
typically reported | 3 |
game theory | 3 |
high activation | 3 |
global analysis | 3 |
distinct days | 3 |
microblog text | 3 |
current results | 3 |
nation wide | 3 |
new births | 3 |
smir model | 3 |
recovered group | 3 |
outbreak predictions | 3 |
production capacity | 3 |
log plot | 3 |
popular topics | 3 |
dl techniques | 3 |
time rt | 3 |
respiratory infections | 3 |
group seira | 3 |
time derivative | 3 |
past outbreaks | 3 |
models mathematical | 3 |
larger fraction | 3 |
singular kernels | 3 |
generalized legendre | 3 |
test intervention | 3 |
model approach | 3 |
lockdown policies | 3 |
component models | 3 |
also note | 3 |
cumulative incidence | 3 |
analytical model | 3 |
studied using | 3 |
condition given | 3 |
dry cough | 3 |
epidemic curves | 3 |
will spread | 3 |
alive non | 3 |
using self | 3 |
confirmed patients | 3 |
undetected cases | 3 |
pulmonary disease | 3 |
wavelet transformation | 3 |
extremely important | 3 |
initial states | 3 |
particular country | 3 |
deaths worldwide | 3 |
i class | 3 |