trigram

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

trigram	frequency
of the epidemic	431
the number of	294
as well as	168
the impact of	155
in order to	126
the spread of	119
porcine epidemic diarrhea	112
the effect of	109
based on the	106
of the covid	97
in terms of	95
epidemic diarrhea virus	94
of the population	88
the epidemic threshold	86
of the disease	85
of porcine epidemic	82
the role of	81
of an epidemic	76
on the other	75
according to the	74
such as the	74
the case of	72
due to the	71
porcine epidemic diarrhoea	69
the other hand	67
online social presence	66
the use of	66
of infectious diseases	65
the epidemic spread	61
the end of	59
the presence of	59
of the virus	57
severe acute respiratory	57
the epidemic in	56
the effects of	56
the united states	56
the value of	56
the course of	55
at time t	55
during an epidemic	55
in this paper	54
acute respiratory syndrome	54
world health organization	53
in the case	53
one of the	51
there is a	51
number of infected	51
the epidemic spreading	50
in this study	49
total number of	49
epidemic diarrhoea virus	49
be used to	48
on the epidemic	48
the total number	48
that the epidemic	48
the beginning of	48
the evolution of	47
the development of	46
the risk of	46
the dynamics of	46
course of the	46
shown in fig	46
epidemic spreading in	46
there is no	44
of the world	43
in this case	43
it is not	42
the emergence of	42
role of e	41
the epidemic is	41
in the united	41
a number of	41
can be used	40
the epidemic and	40
beginning of the	39
the diffusion of	39
spread of the	39
as a result	39
in the population	39
in the early	38
to the epidemic	38
analysis of the	38
in the same	38
end of the	38
evolution of the	38
an epidemic outbreak	37
as shown in	37
number of cases	37
of the infection	37
the fraction of	36
at the same	36
the influence of	36
in response to	36
value of the	36
of the network	36
the fact that	35
the probability of	35
of the infected	35
the incubation period	35
the level of	34
dynamics of the	34
related to the	34
the distribution of	34
it has been	34
impact of the	33
the same time	33
at the time	33
to predict the	33
and public health	32
impact on the	32
the epidemic peak	32
size of the	32
of epidemic spreading	32
of the outbreak	32
in other words	32
well as the	32
to be the	32
in the s	32
of novel coronavirus	32
the results of	31
the absence of	31
in the context	31
distribution of the	31
of public health	31
values of the	31
the values of	31
a set of	31
the context of	31
around the world	31
the source region	31
depending on the	31
ebola virus disease	31
the proportion of	30
it can be	30
depends on the	30
with respect to	30
during the covid	29
the basis of	29
p r o	29
u r n	29
during the epidemic	29
duration of the	29
part of the	29
j o u	29
to describe the	29
a l p	29
o u r	29
l p r	29
we assume that	29
r o o	29
the importance of	29
most of the	29
the size of	29
the start of	29
n a l	29
r n a	29
of the first	29
p r e	29
o o f	29
the probability that	29
the time of	28
more likely to	28
epidemic in the	28
a function of	28
of epidemic outbreaks	28
in the usa	28
epidemic spread rate	28
the increase of	28
the possibility of	28
of the pandemic	27
at the end	27
the epidemic outbreak	27
regions of the	27
compared to the	27
and control of	27
the duration of	27
of the most	27
on the basis	27
the copyright holder	27
at the beginning	26
found that the	26
infectious disease outbreaks	26
in the source	26
in the epidemic	26
of infectious disease	26
that it is	26
that can be	26
the epidemic of	26
lead to a	26
that of the	26
for the first	26
take into account	26
a series of	26
in complex networks	26
understanding of the	25
need to be	25
in addition to	25
needs to be	25
the existence of	25
we find that	25
the cb model	25
in which the	25
to estimate the	25
the effectiveness of	25
phase of the	25
spread of epidemic	25
of the model	25
likely to be	25
in the following	25
used in the	24
large number of	24
of the two	24
and in the	24
the sir model	24
to assess the	24
is that the	24
an important role	24
the state of	24
for disease control	24
in south korea	24
the density of	24
on epidemic spreading	24
into account the	24
to understand the	24
prevention and control	24
on the one	24
the transmission of	24
adopt epidemic prevention	23
to analyze the	23
the one hand	23
is defined as	23
with the increase	23
the infection rate	23
in case of	23
of the total	23
the rate of	23
response to the	23
to adopt epidemic	23
the peak of	23
show that the	23
number of people	22
characteristics of the	22
some of the	22
epidemic spreading and	22
on the spread	22
focus on the	22
in a population	22
people in the	22
this version posted	22
the severity of	22
corresponds to the	22
to model the	22
of infected individuals	22
of a disease	22
the author funder	21
the preprint in	21
as a function	21
medrxiv a license	21
the two regions	21
in the two	21
display the preprint	21
changes in the	21
the study of	21
to have a	21
important role in	21
to identify the	21
to evaluate the	21
granted medrxiv a	21
a result of	21
which was not	21
who has granted	21
the h n	21
copyright holder for	21
the ebola virus	21
in the absence	21
for public health	21
in relation to	21
license to display	21
a license to	21
of the number	21
the ripple effect	21
this is a	21
of the sars	21
the decay ratio	21
than that of	21
and information diffusion	21
to display the	21
shows that the	21
holder for this	21
has granted medrxiv	21
is based on	21
number of infections	20
similar to the	20
the novel coronavirus	20
intensity of knowledge	20
of the intervention	20
of infected people	20
increase in the	20
the introduction of	20
is shown in	20
social distancing measures	20
to reduce the	20
emerging infectious diseases	20
assume that the	20
of social distancing	20
the epidemic dynamics	20
in recent years	20
intention to adopt	20
can also be	20
the nature of	20
respiratory syndrome coronavirus	20
is given by	20
basic reproduction number	20
it should be	19
the basic reproduction	19
of coronavirus disease	19
disease control and	19
observed in the	19
is related to	19
the outbreak of	19
to calculate the	19
the range of	19
control and prevention	19
in hong kong	19
in west africa	19
early detection of	19
on complex networks	19
in the model	19
diarrhea virus in	19
stages of the	19
in this section	19
attitude toward epidemic	19
affected by the	19
can be seen	19
of knowledge into	19
is the number	19
the first time	19
of official policies	19
number of the	18
is the author	18
increase of the	18
function of the	18
which can be	18
knowledge into rumor	18
found to be	18
that there is	18
early stages of	18
of the lockdown	18
what is the	18
to control the	18
spreading and information	18
for the epidemic	18
penetration intensity of	18
on the sc	18
in the community	18
epidemic and the	18
estimation of the	18
of the ebola	18
information about the	18
a and b	18
start of the	18
in the first	18
a modelling study	18
and can be	18
preparedness and response	18
of a pandemic	18
depend on the	18
the design of	18
the history of	18
the availability of	18
to be a	18
to the number	18
an epidemic is	18
and online social	18
the form of	17
the th century	17
the sc performance	17
is used to	17
on epidemic prevention	17
number of individuals	17
was used to	17
the timing of	17
can be found	17
to explore the	17
role in the	17
peak of the	17
the early stages	17
it is possible	17
the transmission rate	17
with a large	17
birth and death	17
point of view	17
of a new	17
find that the	17
toward epidemic outbreak	17
the onset of	17
partial least squares	17
each of the	17
structure of the	17
transmission dynamics of	17
the appearance of	17
a range of	17
is the most	17
cumulative number of	17
to determine the	17
an increase in	17
at that time	17
the world health	17
according to their	17
is able to	17
are shown in	16
the average number	16
on the decay	16
in the last	16
the accuracy of	16
number of contacts	16
it possible to	16
between the two	16
proportional to the	16
the analysis of	16
ratio of the	16
to an epidemic	16
on temporal networks	16
word of mouth	16
average number of	16
of the second	16
impact of covid	16
genome sequence of	16
spread of infectious	16
of the whole	16
of the time	16
per cent of	16
influence of the	16
which is the	16
time of the	16
is necessary to	16
an influenza pandemic	16
shown in figure	16
a systematic review	16
there was no	16
on online social	16
each time step	16
in our model	16
the population of	16
it is important	16
to the first	16
the s gene	16
direct and indirect	16
is difficult to	16
virus in the	16
there was a	16
this is the	16
the set of	16
the infectivity of	15
we show the	15
this means that	15
defined as the	15
the ratio of	15
transmission of the	15
structural equation modeling	15
so as to	15
respiratory syncytial virus	15
we do not	15
to study the	15
that is to	15
the virus and	15
epidemic outbreaks on	15
indicates that the	15
increases with the	15
the concept of	15
stage of the	15
showed that the	15
outbreaks on the	15
proportion of the	15
the need for	15
nature of the	15
the infected density	15
as long as	15
of people in	15
the epidemic process	15
data from the	15
total epidemic size	15
of the three	15
by peer review	15
wang et al	15
of the sir	15
the s protein	15
first digit distribution	15
the total population	15
considered to be	15
the ebola epidemic	15
not certified by	15
the ability to	15
in the network	15
the degree of	15
was not certified	15
epidemic in china	15
density of the	15
certified by peer	15
case of the	15
the control of	15
be able to	15
during the first	15
of an outbreak	15
bound on the	15
of the influenza	15
in the number	15
during the outbreak	15
we consider the	15
complete genome sequence	15
respect to the	15
as in the	15
fraction of infected	15
a large number	15
epidemic model with	15
may not be	14
and so on	14
we refer to	14
because of the	14
of asymptomatic infectives	14
an outbreak of	14
of information diffusion	14
have shown that	14
effect on the	14
onset of symptoms	14
the black death	14
to this end	14
relationship between the	14
in different countries	14
the exponential growth	14
of the th	14
be noted that	14
which is a	14
with each other	14
of the parameters	14
close to the	14
guidelines on epidemic	14
associated with the	14
for infectious disease	14
epidemic spreading on	14
we use the	14
and the epidemic	14
for pandemic influenza	14
of the epidemics	14
the epidemic period	14
sis epidemic model	14
the simulation results	14
by the epidemic	14
this is not	14
development of the	14
the model is	14
in the field	14
in public health	14
the impacts of	14
is assumed that	14
cases in the	14
than the first	14
the magnitude and	14
is the first	14
are needed to	14
probability that a	14
the usage probability	14
at least one	14
probability of a	14
epidemic threshold is	14
period of time	14
in the incubation	14
of the pathogen	14
is important to	14
in northern italy	14
the intensity of	14
in the at	14
of pandemic influenza	14
in the next	14
effects of the	14
and disease information	14
in the covid	14
the final size	14
the coronavirus disease	14
the second wave	14
is not a	14
have been used	14
effect of the	14
the epidemic curve	14
it is a	14
it is assumed	14
the management of	14
was observed in	14
and epidemic spreading	14
we propose a	14
different types of	13
to public health	13
impacts of epidemic	13
may be used	13
the structure of	13
the twentieth century	13
report of the	13
and the number	13
half of the	13
the disease is	13
rumor and epidemic	13
would like to	13
dynamics and control	13
will lead to	13
the influenza season	13
rumor and knowledge	13
this this version	13
the association between	13
different values of	13
they do not	13
has not been	13
the public health	13
a combination of	13
a second wave	13
epidemic spread in	13
to find the	13
model for the	13
allows us to	13
in the us	13
of cases and	13
are used to	13
at each time	13
history of the	13
in the second	13
is to say	13
in the contact	13
influenced by the	13
of disease and	13
the nineteenth century	13
reproduction number of	13
parts of the	13
influence of official	13
the outcome of	13
and to the	13
taking into account	13
to that of	13
the contact network	13
it is necessary	13
over the whole	13
terms of the	13
to characterize the	13
the epidemic size	13
the infected population	13
the epidemiology of	13
be caused by	13
for the covid	13
the language categories	13
for a long	13
there is an	13
the cumulative number	13
they found that	13
when the epidemic	13
transmissible gastroenteritis virus	13
outbreak in china	13
store infection threat	13
caused by a	13
our model is	13
early phase of	13
in the future	13
should be noted	13
across the world	13
node v i	13
appears to be	13
and it is	13
v i is	13
the transmission dynamics	13
there has been	13
lower than the	13
the response to	13
the early phase	13
members of the	13
role of the	13
we focus on	13
as a consequence	13
the age of	13
the chinese government	13
it is the	13
preprint in perpetuity	12
resulting in a	12
is possible to	12
of the entire	12
influence on the	12
the authors declare	12
the population distribution	12
a case study	12
jung et al	12
case fatality rate	12
the functional configuration	12
of severe acute	12
this was the	12
it is also	12
the average epidemic	12
the implementation of	12
with the same	12
with community structure	12
the interplay between	12
taken into account	12
of the state	12
reduction in transmission	12
inflow from wuhan	12
early in the	12
shown in the	12
fraction of the	12
in the public	12
play an important	12
on social media	12
results of the	12
a period of	12
in the literature	12
of the current	12
population inflow from	12
a susceptible node	12
assumed that the	12
the cause of	12
outbreak in the	12
its impact on	12
information diffusion and	12
the most effective	12
in other countries	12
the theory of	12
consistent with the	12
to deal with	12
the whole population	12
an infected individual	12
cent of the	12
the parameters of	12
epidemic can be	12
of those who	12
three social epidemics	12
in the course	12
the behaviour of	12
is the same	12
of the s	12
each of these	12
economic connection intensity	12
rate of the	12
least squares structural	12
shown in table	12
to account for	12
due to a	12
to the covid	12
severity of the	12
the current pandemic	12
morbidity and mortality	12
diseases such as	12
squares structural equation	12
the prevalence of	12
the disease information	12
we need to	12
the assumption that	12
estimates of the	12
in epidemic control	12
with recurrent mobility	12
the first wave	12
value of a	12
whether or not	12
spatial correlation characteristics	12
based on a	12
form of the	12
social and economic	12
to the disease	12
in this work	12
in the area	12
for this preprint	12
the spreading of	12
two types of	12
to obtain the	12
an analysis of	12
first time in	12
in this way	12
diffusion of knowledge	12
in the transmission	12
international spread of	12
in the presence	12
rather than the	11
of all the	11
the epidemic has	11
pattern of the	11
an infectious disease	11
to examine the	11
hair et al	11
for the number	11
timing of the	11
social media and	11
properties of the	11
the characteristics of	11
emergence of a	11
layer network is	11
of this paper	11
to improve the	11
the virus is	11
number of contagious	11
measures on the	11
is smaller than	11
use of the	11
the face of	11
the most common	11
can be obtained	11
of the susceptible	11
dynamics of epidemic	11
may lead to	11
in the dangs	11
it is more	11
the early stage	11
the adivasis of	11
liu et al	11
as soon as	11
during an outbreak	11
that they have	11
the infection probability	11
for an epidemic	11
to quantify the	11
the efficiency of	11
results show that	11
second wave of	11
disease information diffusion	11
with degree k	11
the same as	11
sequence of a	11
the reproduction number	11
be the most	11
many of the	11
figure shows the	11
prior epidemic experience	11
of epidemic spread	11
of infection and	11
incubation period of	11
it is made	11
referred to as	11
i pf j	11
for each of	11
the rest of	11
of transmission and	11
international license it	11
during the early	11
is less than	11
hospitalizations and deaths	11
processes in complex	11
a role in	11
the who method	11
recurrent mobility pattern	11
the whole country	11
in contrast to	11
knowledge of the	11
after onset of	11
public health interventions	11
the tendency of	11
the areas of	11
the penetration intensity	11
of the public	11
parameters of the	11
the case in	11
led to the	11
the epidemic was	11
the general public	11
the same way	11
it is difficult	11
the sum of	11
license it is	11
the first one	11
there are two	11
state of the	11
a lack of	11
of infected cases	11
number of deaths	11
available under a	11
the majority of	11
is made available	11
pf i pf	11
the small intestine	11
included in the	11
the goal of	11
the severe acute	11
the capacity of	11
primary care system	11
the fight against	11
transmission dynamics and	11
is similar to	11
of the novel	11
epidemics on networks	11
the pairwise approach	11
the most important	11
we used the	11
smaller than the	11
to understand how	11
and severity of	11
of this study	11
that the disease	11
to the real	11
the mc model	11
the detection of	11
and that the	11
to simulate the	11
the process of	11
occurred in the	11
shown that the	11
was the case	11
li et al	11
a long time	11
in this model	11
to compare the	11
there will be	11
basic reproductive number	11
all of the	11
in what follows	11
the infection is	11
model of the	11
epidemic processes in	11
presence of a	11
from january to	11
in real time	11
downstream the sc	11
do not have	11
were able to	11
center for disease	11
made available under	11
in primary care	11
the information diffusion	11
caused by the	11
of social media	11
so that the	11
the first digit	11
the mem method	11
the numbers of	11
of a second	11
it may be	11
of control measures	11
of ebola virus	11
been used to	11
compared with the	11
the median delay	11
allowed to vary	10
the current covid	10
of the main	10
spread of covid	10
epidemic in wuhan	10
of the final	10
of epidemics and	10
into the epidemic	10
org journal rspb	10
moving epidemic method	10
networks epidemic spreading	10
information on the	10
epidemic dynamics on	10
the rise of	10
that it was	10
as for the	10
x and x	10
after the epidemic	10
population of the	10
should be considered	10
individuals in the	10
length of the	10
the awareness of	10
has been reported	10
a total of	10
authors declare that	10
model based on	10
to investigate the	10
the likelihood of	10
of the average	10
is consistent with	10
after the first	10
may be a	10
spread of disease	10
the proposed model	10
of contagious people	10
of the country	10
public health and	10
to be more	10
of the system	10
epidemic threshold and	10
the results in	10
the lack of	10
view of the	10
a population of	10
result of the	10
the ib model	10
the results are	10
during the initial	10
number of confirmed	10
have to be	10
control of the	10
aware of the	10
china except hubei	10
of infected nodes	10
of the contact	10
of awareness on	10
how the epidemic	10
wide range of	10
the potential to	10
of this research	10
the mediation effect	10
the mortality rate	10
down the epidemic	10
the behavior of	10
water and sanitation	10
tendency of the	10
to the data	10
slow down the	10
leads to a	10
a reduction in	10
to the virus	10
the critical value	10
seems to be	10
of new infections	10
be divided into	10
have been reported	10
journal rspb proc	10
of the underlying	10
can be observed	10
critical infection probability	10
on the number	10
the main text	10
the aim of	10
infectious diseases have	10
to mitigate the	10
clinical characteristics of	10
in the late	10
of epidemic propagation	10
who do not	10
and impact of	10
eradicate the epidemic	10
serial interval of	10
the intervention is	10
we consider a	10
medical facilities conditions	10
we want to	10
the interaction between	10
prior to the	10
as epidemic villains	10
in the real	10
to the same	10
more or less	10
where there is	10
case of a	10
and duration of	10
presence of the	10
the infected and	10
the definition of	10
transmission and the	10
epidemic threshold in	10
the application of	10
the speed of	10
economic impact of	10
the benford distribution	10
the relationship between	10
and the use	10
has also been	10
fact that the	10
of being infected	10
were used to	10
east respiratory syndrome	10
there are no	10
to some extent	10
is more likely	10
extent to which	10
for all the	10
s and s	10
the herd immunity	10
a is the	10
which in turn	10
the ebola outbreak	10
can be easily	10
number of secondary	10
of a large	10
transmission of covid	10
mathematical theory of	10
that epic m	10
different regions of	10
description of the	10
ministry of health	10
in our study	10
from to days	10
this is an	10
the consequences of	10
a few months	10
are more likely	10
this type of	10
on the scs	10
the propagation of	10
of the spread	10
on march th	10
focused on the	10
dynamics on complex	10
and the impact	10
be found in	10
number of covid	10
relevant to the	10
risk of infection	10
sections of the	10
a h n	10
across the globe	10
can be applied	10
we set the	10
research on the	10
symptomatic and asymptomatic	10
see that the	10
spatial and temporal	10
there are many	10
growth of the	9
the epidemic prevalence	9
the composite parameter	9
by means of	9
aspects of the	9
for this this	9
have been proposed	9
the mathematical theory	9
spread rate was	9
the quarantine factor	9
will not be	9
held in place	9
and medical facilities	9
of the government	9
to take into	9
of the basic	9
h n pandemic	9
the choice of	9
equal to the	9
behavior of the	9
the second one	9
we can see	9
focuses on the	9
can be divided	9
the suspended reality	9
middle east respiratory	9
investigate the impact	9
different from the	9
of the cumulative	9
the basic reproductive	9
cases and deaths	9
since the s	9
higher than the	9
as can be	9
positive impact on	9
the economic impact	9
the three social	9
review of the	9
could be used	9
in a given	9
models of the	9
the s and	9
the united kingdom	9
the occurrence of	9
in social networks	9
in this area	9
the world have	9
spreading in scale	9
from that of	9
detection of porcine	9
corresponding to the	9
the chance of	9
behaviour of the	9
which may be	9
has been the	9
the us outbreak	9
as compared to	9
the ongoing covid	9
between individuals and	9
consequences of the	9
the population is	9
of differential equations	9
epidemic spreading process	9
be difficult to	9
which are not	9
on epidemic outbreaks	9
the question of	9
there are also	9
the infected individuals	9
that the number	9
results of a	9
infectious diseases and	9
addition to the	9
an infected traveler	9
on the impact	9
toward an epidemic	9
estimated by the	9
of the quarantine	9
suspended reality phase	9
networked population with	9
asymptomatic and symptomatic	9
number of new	9
is found to	9
a local epidemic	9
on the dynamics	9
measures such as	9
as a whole	9
can be interpreted	9
has to be	9
networks with community	9
rest of the	9
the scope of	9
in an epidemic	9
of the research	9
a variety of	9
but it is	9
is clear that	9
in the small	9
ways in which	9
of the us	9
usage probability of	9
period of the	9
the acceptance phase	9
upper bound on	9
in the present	9
at the start	9
in china and	9
version posted june	9
the spatial and	9
note that the	9
the ib approach	9
of asymptomatic cases	9
and it was	9
of a population	9
in social media	9
to illustrate the	9
among elderly people	9
of an infectious	9
exponential growth rate	9
intentionally directing attacks	9
countries that have	9
the moving epidemic	9
deletion in the	9
differences in the	9
in real networks	9
this can be	9
to break out	9
an example of	9
per cent for	9
been shown to	9
the recovery rate	9
the data of	9
preprint this version	9
reduce the number	9
final epidemic size	9
of epidemic control	9
a novel coronavirus	9
shown to be	9
in the form	9
given by the	9
awareness of the	9
monte carlo simulation	9
majority of the	9
of epidemic protection	9
as the epidemic	9
in the face	9
have not been	9
is not the	9
a major epidemic	9
described by the	9
not only to	9
to measure the	9
the example of	9
effects on the	9
the initial number	9
when it is	9
to the development	9
of influenza in	9
social presence during	9
a wide range	9
the epidemic outbreaks	9
social distancing and	9
that an infected	9
epidemic thresholds in	9
be seen from	9
interpretation of the	9
the need to	9
of h n	9
the epidemic will	9
of virus transmission	9
data of the	9
between epidemic spreading	9
to increase the	9
member of the	9
research can be	9
in the sc	9
to ensure that	9
initial number of	9
large numbers of	9
expected number of	9
of epidemic disease	9
as in fig	9
the vulnerability of	9
can lead to	9
conflict of interest	9
age and gender	9
are likely to	9
who have been	9
correspond to the	9
the states of	9
diarrhoea virus in	9
a lot of	9
of a node	9
of the individuals	9
in the communication	9
it is clear	9
starting from the	9
and after the	9
the influenza epidemic	9
this preprint this	9
in the study	9
response to epidemics	9
us pedv strains	9
of the results	9
directing attacks against	9
on the population	9
response to an	9
of the adivasis	9
epidemic outbreak in	9
of this work	9
less likely to	9
study of the	9
can be described	9
of pedv in	9
day of the	9
and prevention of	9
assessment of the	9
the spectral radius	9
the threat of	9
local spatial correlation	9
the critical infection	9
very close to	9
but also on	9
of our study	9
of the above	9
spread of a	9
data on the	9
to contain the	9
of infected travelers	8
the disease in	8
they are not	8
of the host	8
more than one	8
the infection and	8
for all nodes	8
only on the	8
high level of	8
by using the	8
of a given	8
we present the	8
a part of	8
dependent on the	8
magnitude and intensity	8
the default model	8
the disease will	8
the health system	8
the period of	8
the daily incidence	8
infection threat has	8
a matter of	8
v j is	8
ivanov et al	8
with probability p	8
can be implemented	8
is close to	8
in multiplex networks	8
nowcasting and forecasting	8
reduction in the	8
is in the	8
i equals to	8
most likely to	8
applied to the	8
trend of the	8
in contact with	8
let us consider	8
for epidemic control	8
basis of the	8
song et al	8
the virus was	8
it is worth	8
plays an important	8
the expected number	8
awareness on epidemic	8
the scientific community	8
be explained by	8
performance of the	8
in the final	8
is larger than	8
diffusion of the	8
process of the	8
and peak prevalence	8
the epidemic on	8
we believe that	8
model with a	8
n is the	8
presented in the	8
the practice of	8
of rumor and	8
infected at time	8
in the range	8
responses to the	8
contribute to the	8
it is still	8
it would be	8
and of the	8
a recent study	8
i is the	8
of disease transmission	8
the transmission parameter	8
spatial distribution of	8
difference between the	8
determined by the	8
cb and ib	8
saif et al	8
threat has positive	8
node in the	8
the epidemic growth	8
over the past	8
the west african	8
outbreak on the	8
in the th	8
in accordance with	8
driven adaptive process	8
peaks in the	8
in suckling piglets	8
and the second	8
the inflection point	8
as much as	8
propagation of the	8
epidemic protection and	8
prior experience with	8
such that the	8
the strength of	8
natural language processing	8
public health systems	8
the scale of	8
of dengue fever	8
n m e	8
democratic republic of	8
it could be	8
during the whole	8
to check the	8
the largest eigenvalue	8
back to the	8
is a major	8
static and dynamic	8
the general population	8
can be explained	8
the reduction in	8
epidemic threshold for	8
epidemic size and	8
pandemic influenza a	8
some of these	8
other parts of	8
as spreaders of	8
tended to be	8
influenza a h	8
and social distancing	8
and forecasting the	8
appear to be	8
to explain the	8
results in a	8
the democratic republic	8
the dynamic equations	8
in the infection	8
the disease was	8
our understanding of	8
and h n	8
of product functions	8
population with recurrent	8
of epidemic outbreak	8
obtained from the	8
of all ages	8
n m n	8
epidemic preparedness and	8
national and international	8
in this scenario	8
is considered to	8
estimate of the	8
noted that the	8
different levels of	8
of the data	8
and the other	8
infection in the	8
effect of control	8
has become a	8
we have a	8
part of a	8
of the pedv	8
m n m	8
infected population in	8
decrease in the	8
from the chinese	8
of the a	8
a public health	8
and transmission of	8
in the region	8
in mainland china	8
total population of	8
of supply chain	8
because it is	8
based influence factors	8
model can be	8
a susceptible individual	8
means that the	8
are difficult to	8
the product of	8
the ministry of	8
attention to the	8
result in a	8
the upper bound	8
to make the	8
change in the	8
and the united	8
that of a	8
was the first	8
the spreading dynamics	8
the epidemic season	8
could not be	8
the virulence of	8
in the who	8
in hubei and	8
as opposed to	8
areas were located	8
on multiplex networks	8
with and without	8
under a is	8
origin of the	8
data collection and	8
be applied to	8
the adaptable design	8
is also the	8
a review of	8
the advantage of	8
implications for the	8
will be the	8
assumed to be	8
this paper is	8
bmj global health	8
in china the	8
the efficacy of	8
in developing countries	8
were reported in	8
close contact with	8
and response to	8
to respond to	8
and intensity of	8
were located in	8
has positive impact	8
of disease in	8
a mathematical model	8
of our model	8
extension of the	8
the entire population	8
outcomes of the	8
other types of	8
time evolution of	8
free networks with	8
impact of awareness	8
the reason for	8
and its impact	8
a source of	8
and the infection	8
in cases of	8
we have to	8
have been developed	8
impacts of the	8
leads to the	8
absence of any	8
it does not	8
and on the	8
epidemic spreading is	8
should not be	8
is required to	8
in the supply	8
while it is	8
can be considered	8
final size of	8
not depend on	8
of the superspreaders	8
of epidemic prevention	8
model to describe	8
this work was	8
results indicate that	8
early transmission dynamics	8
is set as	8
virus disease in	8
of cases in	8
the refusal phase	8
the height of	8
the endemic equilibrium	8
component of the	8
found in the	8
chen et al	8
to consider the	8
systematic review of	8
we study the	8
used to model	8
as an example	8
the serial interval	8
for the detection	8
variant of the	8
we compare the	8
the burden of	8
is likely to	8
are the most	8
of health of	8
the results for	8
it will be	8
and spread of	8
at the early	8
diarrhea virus strains	8
on the contrary	8
makes it possible	8
of the dynamics	8
peak in the	8
the healthcare system	8
for the most	8
and effectiveness of	8
the study was	8
health of morocco	8
by the who	8
prior epidemic exposure	8
and found that	8
in the world	8
the death rate	8
with regard to	8
diffusion and epidemic	8
and the time	8
in many cases	8
according to a	8
calculated probability of	8
what are the	8
insight into the	8
in the uk	8
not only on	8
up to the	8
h n influenza	8
lead to the	8
leading to the	8
be used by	8
in the forest	8
of the role	8
only a few	8
we obtain the	8
of the twentieth	8
the identification of	8
to eradicate the	8
declare that they	8
ili patients among	7
an epidemic and	7
centers for disease	7
number of links	7
the potential domestic	7
account for the	7
the dummy variable	7
since the epidemic	7
and contact tracing	7
of the simulation	7
does not affect	7
strength of the	7
time since infection	7
be associated with	7
to the one	7
is one of	7
border control measures	7
upper limit of	7
when the number	7
of epidemic threshold	7
an adaptive network	7
value of k	7
during the sars	7
the absolute value	7
in the system	7
to stop the	7
the disease spreading	7
equilibrium number of	7
analysis of epidemic	7
mixing on outcomes	7
and thus the	7
the forest region	7
epidemic prevention is	7
in most of	7
the performance of	7
the primary care	7
and porcine epidemic	7
in all the	7
epidemic dynamics and	7
dynamics on the	7
the case for	7
followed by the	7
of this article	7
propagation in the	7
the seasonal threshold	7
on porcine epidemic	7
on the covid	7
in the u	7
represents the number	7
is not yet	7
in different stages	7
about the epidemic	7
a large fraction	7
not affect the	7
in this context	7
number of susceptible	7
the first peak	7
hubei and china	7
overview of the	7
of the response	7
out to be	7
and do not	7
to try to	7
simulation results and	7
the result of	7
of epidemic in	7
can be a	7
the cb and	7
the network is	7
out of the	7
the infected nodes	7
and ib models	7
data for the	7
the magnitude of	7
research part e	7
originating in wuhan	7
on the health	7
may be more	7
time of writing	7
on outcomes of	7
of south gujarat	7
within and between	7
sensitivity and specificity	7
the difference between	7
epidemic in west	7
on the analysis	7
fuzzy rule induction	7
of household anti	7
region of the	7
west african ebola	7
that in the	7
of a product	7
the spatial distribution	7
small number of	7
responsible for the	7
and may be	7
is proportional to	7
of infected neighbors	7
the virus in	7
the variation in	7
reproduction number r	7
on the network	7
there were no	7
to better understand	7
of the group	7
after the quarantine	7
and its applications	7
of the variables	7
the quality of	7
ncov outbreak originating	7
results from the	7
the health of	7
are based on	7
there may be	7
of knowledge is	7
of control strategies	7
larger than the	7
of new cases	7
the epidemic reaches	7
to the infection	7
version posted august	7
vertex v i	7
social mixing on	7
mobile phone data	7
to develop an	7
in this volume	7
be due to	7
on how the	7
people aged or	7
the dependence of	7
date of the	7
supported by the	7
we calculate the	7
probability that an	7
the cost of	7
the population and	7
vigilance to the	7
a model for	7
the real world	7
the matching degree	7
the public vigilance	7
human and animal	7
rate and the	7
for the second	7
exponential growth of	7
different from that	7
the human population	7
the case with	7
elt service level	7
to reduce social	7
predict the tendency	7
of asymptomatic carriers	7
in the previous	7
public health measures	7
of the past	7
transmission dynamics in	7
the time the	7
ebola outbreak in	7
value for the	7
diffusion of information	7
derivation of the	7
of nonpharmaceutical interventions	7
indicate that the	7
of the critical	7
this is because	7
of an influenza	7
a member of	7
that may be	7
time in the	7
high risk of	7
data that are	7
such as ebola	7
information diffusion on	7
refer to the	7
outbreak originating in	7
early presentation of	7
in sierra leone	7
epidemic disease on	7
social presence and	7
those of the	7
scale of the	7
in such a	7
a new wave	7
pandemic in the	7
is no longer	7
and how to	7
carried out in	7
features of the	7
the supply chain	7
influenza pandemic in	7
even if the	7
to the case	7
transmission and control	7
and indirect effects	7
in the whole	7
was based on	7
the next section	7
strain of porcine	7
pneumonia in china	7
the past days	7
diarrhea virus strain	7
epidemic control in	7
have been in	7
of animals as	7
isolation of porcine	7
a case of	7
are as follows	7
in random networks	7
is needed to	7
is found that	7
phylogenetic analysis of	7
patients among all	7
version of the	7
is located in	7
the sensitivity of	7
transmission of sars	7
management of epilepsy	7
has been shown	7
removed from the	7
raise the epidemic	7
they have no	7
to provide a	7
effectiveness of the	7
of a network	7
adaptable design method	7
suggests that the	7
the point of	7
january to february	7
of the nineteenth	7
mitigate the impact	7
of complex networks	7
of the effect	7
hypothesized the following	7
public vigilance to	7
the final epidemic	7
transmission of pedv	7
the next pandemic	7
in the development	7
stevenson et al	7
the notion of	7
well as a	7
of sars in	7
epidemic outbreak threshold	7
not only the	7
the peak in	7
the relationships between	7
attitudes toward an	7
growth rate of	7
forecasting the potential	7
the initial phase	7
factors such as	7
when there are	7
be interpreted as	7
of the daily	7
the final state	7
widely used in	7
and the role	7
control strategies to	7
is higher than	7
reduce social mixing	7
herd immunity threshold	7
pensaert and de	7
sequence analysis of	7
potential domestic and	7
the population in	7
proved to be	7
to the ebola	7
such as a	7
speed of epidemic	7
in this figure	7
the extent to	7
disease transmission and	7
models have been	7
in time and	7
the whole epidemic	7
identified as the	7
probability of product	7
vaccine is available	7
of the region	7
infectious diseases in	7
and v j	7
the state vector	7
strain of influenza	7
and in particular	7
number of users	7
the class of	7
with a total	7
domestic and international	7
impact of a	7
the susceptibility of	7
due to their	7
pigs of all	7
for a given	7
the increase in	7
the heart of	7
human animals as	7
by epidemic year	7
and de bouck	7
to have been	7
and china except	7
function of time	7
the first epidemic	7
the quarantine period	7
the disease transmission	7
to develop a	7
individuals who are	7
of the national	7
the trend of	7
has led to	7
can be achieved	7
a more detailed	7
may also be	7
used as a	7
this paper we	7
an epidemic model	7
of the next	7
disease on networks	7
in the analysis	7
mortality rate of	7
in the human	7
the early s	7
theory of planned	7
of population structure	7
epidemics and the	7
correlated with the	7
of influenza a	7
is due to	7
it is found	7
the input variables	7
derived from the	7
to be used	7
health of the	7
number of nodes	7
intensity of the	7
wave of epidemic	7
chain of transmission	7
those who have	7
control of covid	7
to changes in	7
is the time	7
epidemic in this	7
transportation research part	7
animals as epidemic	7
days to peak	7
threshold of epidemic	7
of the initial	7
public health authorities	7
means the daily	7
height of the	7
of the duration	7
social presence is	7
characterization of the	7
one can also	7
of the coronavirus	7
were found to	7
the cure rate	7
of epidemics in	7
there are some	7
involved in the	7
effect of a	7
the higher the	7
the infection period	7
and the world	7
of the survey	7
possibility of a	7
a total population	7
based on this	7
used in this	7
influenza surveillance in	7
aged or above	7
control of epidemics	7
the available data	7
strategies to reduce	7
of confirmed cases	7
control the epidemic	7
the first outbreak	7
spread of pandemic	7
for at least	7
number of days	7
risk of transmission	7
values of r	7
of each individual	7
and international spread	7
to prevent the	7
impact of epidemic	7
a second peak	7
the creative commons	7
theory of epidemics	7
k is the	7
that between the	7
and attitude toward	7
the disease and	7
but also the	7
of the third	7
among all outpatients	7
our study results	7
in the initial	7
logistic growth model	7
by the following	7
towards epidemic prevention	7
has been observed	7
to reduce transmission	7
among people aged	7
we note that	7
spine surgeons worldwide	7
it to the	7
in the history	7
after the peak	7
be used in	7
characterized by a	7
information diffusion can	7
the source of	7
the prevention and	7
creative commons attribution	7
may have been	7
we hypothesized the	7
been reported in	7
the spread and	7
cases per million	7
the same for	7
that this was	7
to fit the	7
economic and social	7
the treatment of	7
as the main	7
believed to be	7
digit distribution of	6
bilateral pitting edema	6
values of a	6
the frequency of	6
because of their	6
to create a	6
under which the	6
or bilateral pitting	6
product of the	6
in table i	6
trend between the	6
upstream the sc	6
solution of the	6
put in place	6
the virus spread	6
from the infected	6
spread of sars	6
case study of	6
health authorities and	6
rate in the	6
order to better	6
agglutination to o	6
epidemic of the	6
be the same	6
units of time	6
figure has been	6
at least two	6
the effective reproduction	6
sueyoshi et al	6
behavior dynamics on	6
the th of	6
the epidemic may	6
we learn from	6
the state that	6
attitude towards epidemic	6
would be useful	6
be taken into	6
we stress that	6
the growth of	6
of human mobility	6
of more than	6
pcps in china	6
of respondents reported	6
from south korea	6
epidemic is over	6
at any time	6
chinese center for	6
we are interested	6
with at least	6
described in the	6
yan et al	6
in the country	6
between the past	6
from the s	6
and infectious disease	6
variations in the	6
the epidemic are	6
and does not	6
social media data	6
that the adivasis	6
this figure has	6
rate of infection	6
results and the	6
of partial least	6
is greater than	6
the epidemic angle	6
is influenced by	6
of respiratory syncytial	6
median delay from	6
spreading dynamics on	6
dynamics in wuhan	6
network is the	6
is a significant	6
as part of	6
of the product	6
the network topology	6
to test the	6
is only a	6
and access to	6
risk factors for	6
supply chain network	6
in new zealand	6
we use a	6
prevent the epidemic	6
the properties of	6
number of patients	6
the diamond princess	6
and control measures	6
large set of	6
with the number	6
in guangdong province	6
in the suspended	6
was caused by	6
it was not	6
the situation of	6
the situation in	6
outbreak of the	6
in networked population	6
to facilitate the	6
dynamic contact networks	6
v i and	6
which is consistent	6
version of this	6
of r max	6
the timing and	6
park et al	6
to cope with	6
as to the	6
and the information	6
a consequence of	6
the assumption of	6
the progress of	6
led to a	6
a new coronavirus	6
of the society	6
vary by epidemic	6
all the more	6
the perspective of	6
at the peak	6
in the process	6
in this article	6
for infectious diseases	6
with a certain	6
studies on the	6
global health security	6
belonging to the	6
european centre for	6
and out of	6
case of covid	6
in some countries	6
use of social	6
mathematics of infectious	6
infection rate of	6
each infected individual	6
to protect themselves	6
in the more	6
which the epidemic	6
the predicted results	6
in the appendix	6
to which the	6
the attitude of	6
and vice versa	6
would have been	6
infected and fatality	6
believe that the	6
version posted september	6
transmission in the	6
which is also	6
is affected by	6
referred to the	6
of the cities	6
model is the	6
we find a	6
of planned behavior	6
infectious disease transmission	6
to make sense	6
on the spatial	6
epidemiology and public	6
quarantine factor q	6
of the small	6
when there is	6
contact with the	6
are related to	6
the epidemic sustains	6
fraction of users	6
conditions of the	6
of epidemics on	6
for further details	6
then the strain	6
detection of pedv	6
point at which	6
similar to those	6
the type of	6
are used in	6
over the world	6
cluster areas were	6
and a new	6
infected in the	6
it is no	6
relative to the	6
in all cases	6
increases the median	6
this study is	6
creative commons licence	6
public health problem	6
can be calculated	6
followed by a	6
to the current	6
contrast to the	6
is assumed to	6
in rural areas	6
the solution of	6
the delay is	6
transition probability from	6
the detection factors	6
international health regulations	6
are responsible for	6
well as to	6
from the perspective	6
the long run	6
to the present	6
of the original	6
average epidemic curve	6
pf j is	6
is used as	6
decreases with the	6
which we can	6
is characterized by	6
the time evolution	6
for a more	6
i and j	6
burden of disease	6
state that people	6
to the population	6
in certain industry	6
in place for	6
from the above	6
epidemic diarrhea in	6
is an important	6
there is also	6
is organized as	6
rapid epidemic intelligence	6
at least a	6
may result in	6
the topology of	6
we would like	6
of the situation	6
seasonal epidemic characteristics	6
the reduction of	6
we analyze the	6
above the threshold	6
have not yet	6
be observed in	6
clinical features of	6
in many countries	6
from different countries	6
and animal health	6
organized as follows	6
it is extremely	6
denotes the state	6
at the level	6
the date of	6
is composed of	6
a new virus	6
of information and	6
and the public	6
of emerging infectious	6
of infected population	6
disease carrier species	6
it will also	6
on an adaptive	6
a focus on	6
epidemic curves for	6
personal protective equipment	6
in this sense	6
are associated with	6
the time since	6
of r t	6
seen that the	6
we compared the	6
magnitude of the	6
the network structure	6
age of the	6
with prior epidemic	6
are known to	6
in the adivasi	6
refers to the	6
have already been	6
of the epidemiological	6
the principle of	6
there have been	6
endemic equilibrium number	6
this may be	6
the growth rate	6
does not depend	6
are presented in	6
r is the	6
presentation of cases	6
that if the	6
intensity thresholds were	6
even when the	6
princess cruise ship	6
the covid epidemic	6
a collection of	6
strains in the	6
primary health care	6
been used in	6
weeks into the	6
management of the	6
of confined people	6
the adoption of	6
term impacts of	6
has been used	6
of confirmed covid	6
corresponding to a	6
long as the	6
a global pandemic	6
the whole period	6
if this is	6
in the past	6
days of the	6
to the public	6
serve as a	6
preprint the copyright	6
along with the	6
that the first	6
the epidemic data	6
disease and disease	6
correlated transmission rates	6
the average temperature	6
we have shown	6
with novel coronavirus	6
suppress epidemic spreading	6
was conducted in	6
predictions of the	6
the epidemic setting	6
of the probability	6
where this figure	6
mediates the association	6
all over the	6
can be eradicated	6
an approximating function	6
the standard sir	6
changes in response	6
less than one	6
is to be	6
there are few	6
analysis in the	6
the purpose of	6
in different contexts	6
are unlikely to	6
is the total	6
the early outbreak	6
influence factors of	6
the deterministic continuous	6
based on their	6
attitude of the	6
the public and	6
and early detection	6
of infection in	6
the ib and	6
been found to	6
the prevention of	6
with a mean	6
response to covid	6
the vector of	6
population of n	6
south korea and	6
the threshold for	6
in spite of	6
of the health	6
population in the	6
course of an	6
ib and cb	6
the idea of	6
no conflict of	6
and per cent	6
the strain is	6
before and after	6
case notification data	6
the change of	6
for the control	6
epidemic may be	6
of pedv has	6
variation of the	6
the seriousness of	6
the coefficient of	6
mc model is	6
the lower the	6
to discuss the	6
psychological impact of	6
of the emergence	6
predictor of mortality	6
dependence of the	6
public health institutions	6
infected by a	6
of the nodes	6
we derive the	6
of the severity	6
of influenza activity	6
that the model	6
introduction of the	6
interval of the	6
the global health	6
the scs and	6
millions of people	6
the mode of	6
a group of	6
months after the	6
reported confirmed cases	6
the emergence and	6
the last years	6
a handful of	6
the convergence of	6
in a network	6
in early modern	6
the reproductive number	6
the current situation	6
is a constant	6
the dark web	6
control measures on	6
number of asymptomatic	6
to connect to	6
study showed that	6
countries of the	6
disruption in china	6
the population density	6
the susceptible population	6
epidemic spreading with	6
severity of covid	6
but also to	6
on social networks	6
can be identified	6
to the time	6
more difficult to	6
that a node	6
lessons from the	6
of the pifs	6
be greater than	6
part in the	6
the erg approach	6
prevalence of malnutrition	6
the potential for	6
absolute value of	6
h n in	6
as it is	6
as a key	6
within the same	6
of the i	6
such as those	6
affecting the epidemic	6
is used for	6
like to thank	6
used to assess	6
the roles of	6