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 |
---|---|
public health | 274 |
epidemic spreading | 199 |
porcine epidemic | 188 |
infectious diseases | 146 |
epidemic threshold | 128 |
epidemic outbreak | 116 |
epidemic diarrhea | 116 |
infectious disease | 115 |
epidemic spread | 99 |
diarrhea virus | 96 |
information diffusion | 85 |
epidemic outbreaks | 85 |
social presence | 85 |
social media | 84 |
social distancing | 83 |
novel coronavirus | 81 |
united states | 74 |
epidemic prevention | 73 |
online social | 71 |
epidemic diarrhoea | 69 |
epidemic size | 66 |
per cent | 65 |
respiratory syndrome | 65 |
sir model | 65 |
infected individuals | 63 |
epidemic model | 63 |
complex networks | 63 |
acute respiratory | 62 |
cord uid | 62 |
ebola virus | 62 |
doc id | 62 |
world health | 61 |
incubation period | 60 |
coronavirus disease | 59 |
severe acute | 57 |
epidemic control | 56 |
pandemic influenza | 56 |
herd immunity | 55 |
reproduction number | 55 |
total number | 53 |
health organization | 53 |
control measures | 53 |
global health | 51 |
epidemic models | 49 |
supply chain | 49 |
layer network | 49 |
diarrhoea virus | 49 |
exponential growth | 48 |
disease outbreaks | 47 |
health care | 47 |
epidemic dynamics | 47 |
time step | 45 |
social networks | 44 |
south korea | 44 |
disease control | 43 |
epidemic peak | 43 |
influenza pandemic | 42 |
infection rate | 41 |
pf i | 40 |
differential equations | 40 |
disease information | 39 |
transmission dynamics | 39 |
disease transmission | 38 |
source region | 38 |
primary care | 38 |
death rate | 38 |
contact tracing | 38 |
free networks | 38 |
infected people | 37 |
infected nodes | 36 |
virus disease | 36 |
second wave | 35 |
asymptomatic cases | 35 |
data collection | 35 |
epidemic products | 35 |
pedv strains | 34 |
exit strategies | 34 |
different countries | 34 |
temporal networks | 34 |
epidemic protection | 33 |
community structure | 33 |
peak prevalence | 33 |
early detection | 33 |
health system | 33 |
hong kong | 32 |
contact networks | 32 |
spread rate | 32 |
epidemic period | 32 |
official policies | 31 |
infection probability | 31 |
per day | 30 |
health systems | 30 |
language categories | 30 |
west africa | 29 |
risk country | 29 |
distancing measures | 29 |
adaptable design | 29 |
mortality rates | 29 |
epidemic propagation | 28 |
one hand | 28 |
sis epidemic | 28 |
sc performance | 28 |
usage probability | 28 |
human transmission | 28 |
mortality rate | 28 |
copyright holder | 27 |
confirmed cases | 27 |
fatality rate | 27 |
ebola epidemic | 27 |
important role | 27 |
two regions | 27 |
sis model | 27 |
elderly people | 27 |
risk perception | 27 |
ripple effect | 26 |
epidemic curve | 26 |
attitude toward | 26 |
susceptible individuals | 26 |
mc model | 26 |
total population | 26 |
epidemic events | 26 |
infected individual | 26 |
decay ratio | 25 |
cb model | 25 |
epidemic process | 25 |
age groups | 25 |
disease dynamics | 25 |
serial interval | 25 |
mental health | 25 |
th century | 25 |
risk analysis | 25 |
epidemic data | 25 |
transmission rate | 25 |
disease spreading | 24 |
basic reproduction | 24 |
contact network | 24 |
spreading dynamics | 24 |
high mortality | 24 |
large number | 24 |
network structure | 24 |
big data | 24 |
adopt epidemic | 23 |
degree distribution | 23 |
yellow fever | 23 |
epidemic processes | 23 |
pedv infection | 23 |
future research | 23 |
disease surveillance | 23 |
believed nodes | 23 |
human animals | 23 |
modelling study | 23 |
reproductive number | 23 |
mathematical models | 22 |
influenza season | 22 |
will also | 22 |
early stages | 22 |
household anti | 22 |
version posted | 22 |
emerging infectious | 21 |
avian influenza | 21 |
recent years | 21 |
networks epidemic | 21 |
growth rate | 21 |
simulation results | 21 |
asymptomatic infectives | 21 |
author funder | 21 |
toward epidemic | 21 |
systematic review | 21 |
risk factors | 21 |
prior epidemic | 21 |
granted medrxiv | 21 |
epidemic experience | 21 |
case fatality | 21 |
behavioral changes | 20 |
infected population | 20 |
syndrome coronavirus | 20 |
influenza epidemic | 20 |
first time | 20 |
infected density | 20 |
health services | 20 |
mc simulations | 20 |
influenza activity | 20 |
least squares | 20 |
product functions | 20 |
data analysis | 20 |
epidemic thresholds | 20 |
influenza surveillance | 20 |
disease outbreak | 20 |
high risk | 20 |
northern italy | 20 |
partial least | 20 |
driven adaptive | 19 |
mathematical modelling | 19 |
surveillance data | 19 |
transmissible gastroenteritis | 19 |
influenza virus | 19 |
one health | 19 |
epidemic will | 19 |
behavior dynamics | 19 |
monte carlo | 19 |
epidemic disease | 19 |
time series | 19 |
spine surgeons | 19 |
spreading processes | 19 |
health security | 19 |
virus transmission | 19 |
penetration intensity | 19 |
surveillance system | 18 |
clinical signs | 18 |
widely used | 18 |
epidemic growth | 18 |
pedv strain | 18 |
among individuals | 18 |
case study | 18 |
functional configuration | 18 |
structural equation | 18 |
young people | 18 |
infected cases | 18 |
peer review | 18 |
standard deviation | 18 |
infected neighbors | 18 |
multiplex networks | 18 |
ped outbreaks | 18 |
disease spread | 18 |
recurrent mobility | 18 |
seasonal threshold | 18 |
average temperature | 17 |
dengue fever | 17 |
control strategies | 17 |
cumulative number | 17 |
sample size | 17 |
genome sequence | 17 |
seasonal epidemic | 17 |
social epidemics | 17 |
many countries | 17 |
climate change | 17 |
average number | 17 |
animal health | 17 |
pandemic risk | 17 |
available data | 17 |
death rates | 17 |
final size | 17 |
first digit | 17 |
ebola outbreak | 17 |
data sources | 17 |
us pedv | 17 |
asymptomatic carriers | 17 |
machine learning | 17 |
people aged | 17 |
epidemiological data | 17 |
nan doi | 17 |
epidemic risk | 17 |
income countries | 17 |
behavioral responses | 17 |
medical facilities | 17 |
component model | 17 |
care system | 17 |
health authorities | 17 |
compartmental models | 16 |
economic impact | 16 |
risk management | 16 |
infected traveler | 16 |
epidemic season | 16 |
human mobility | 16 |
symptomatic cases | 16 |
real time | 16 |
black death | 16 |
transmission rates | 16 |
world networks | 16 |
dynamic equations | 16 |
contact patterns | 16 |
input variables | 16 |
outbreak threshold | 16 |
spreading process | 16 |
epidemic curves | 16 |
european countries | 16 |
march th | 16 |
adaptive networks | 16 |
suckling piglets | 16 |
new cases | 16 |
spatial correlation | 16 |
recovery rate | 16 |
digit distribution | 16 |
antimicrobial resistance | 16 |
community workers | 16 |
population density | 16 |
healthcare system | 16 |
pandemic preparedness | 16 |
game theory | 16 |
made available | 16 |
data points | 16 |
different types | 16 |
long time | 16 |
upper bound | 15 |
rural areas | 15 |
critical value | 15 |
flight transmission | 15 |
equation modeling | 15 |
nineteenth century | 15 |
economic connection | 15 |
complete genome | 15 |
seasonal influenza | 15 |
respiratory syncytial | 15 |
twentieth century | 15 |
initial conditions | 15 |
may also | 15 |
first wave | 15 |
protective measures | 15 |
empirical data | 15 |
chinese government | 15 |
proposed model | 15 |
hemorrhagic fever | 15 |
vaccination coverage | 15 |
social contacts | 15 |
syncytial virus | 15 |
total epidemic | 15 |
initially infected | 15 |
even though | 15 |
adaptive process | 15 |
epidemic year | 15 |
aedes aegypti | 15 |
quarantine measures | 15 |
whole population | 14 |
warning systems | 14 |
different epidemic | 14 |
early phase | 14 |
infected node | 14 |
disruption risks | 14 |
communicable diseases | 14 |
least one | 14 |
nursing piglets | 14 |
contagious people | 14 |
previous studies | 14 |
social network | 14 |
two different | 14 |
health interventions | 14 |
theoretical analysis | 14 |
endemic equilibrium | 14 |
health information | 14 |
virus infection | 14 |
allows us | 14 |
different strategies | 14 |
infected travelers | 14 |
time interval | 14 |
gastroenteritis virus | 14 |
human behavior | 14 |
epidemic psychology | 14 |
epidemic may | 14 |
susceptible node | 14 |
average epidemic | 14 |
different values | 14 |
adaptive network | 14 |
confidence interval | 14 |
epidemic potential | 14 |
approximating function | 14 |
medrxiv preprint | 14 |
decision making | 13 |
creative commons | 13 |
pandemic vaccine | 13 |
infection threat | 13 |
epidemiological models | 13 |
early warning | 13 |
birth rate | 13 |
local spatial | 13 |
state individuals | 13 |
years old | 13 |
linear regression | 13 |
median delay | 13 |
spatial distribution | 13 |
preventive measures | 13 |
different regions | 13 |
synthetic biology | 13 |
influence factors | 13 |
seir model | 13 |
ib model | 13 |
pairwise approach | 13 |
store infection | 13 |
developing countries | 13 |
will lead | 13 |
data analytics | 13 |
basic reproductive | 13 |
containment measures | 13 |
two types | 13 |
inflection point | 13 |
supply chains | 13 |
symptom onset | 13 |
small intestine | 13 |
years ago | 13 |
middle east | 13 |
statistical physics | 13 |
data collected | 13 |
dashed curve | 13 |
time period | 13 |
mediation effect | 13 |
mobility pattern | 13 |
international license | 13 |
international spread | 13 |
static network | 13 |
epidemic trajectories | 13 |
sierra leone | 13 |
matching degree | 13 |
positive impact | 13 |
zoonotic diseases | 13 |
publicly available | 13 |
i ic | 13 |
certain industry | 13 |
sir epidemic | 13 |
age group | 13 |
vestibule anti | 13 |
population distribution | 13 |
coupled disease | 13 |
new infections | 13 |
connection intensity | 12 |
medical resources | 12 |
dummy variable | 12 |
sars epidemic | 12 |
heterogeneous networks | 12 |
clinical characteristics | 12 |
epidemic villains | 12 |
patient identification | 12 |
correlation characteristics | 12 |
early stage | 12 |
epidemiological parameters | 12 |
large numbers | 12 |
three social | 12 |
local epidemic | 12 |
population inflow | 12 |
results show | 12 |
contact rate | 12 |
observed data | 12 |
numerical simulations | 12 |
population structure | 12 |
time steps | 12 |
fixed point | 12 |
pandemic response | 12 |
random network | 12 |
federal states | 12 |
infected person | 12 |
el tor | 12 |
general public | 12 |
real networks | 12 |
million people | 12 |
current pandemic | 12 |
health crises | 12 |
benford distribution | 12 |
urban areas | 12 |
transmission parameter | 12 |
threshold value | 12 |
authors declare | 12 |
networked population | 12 |
public vigilance | 12 |
fatality rates | 12 |
contact awareness | 12 |
epilepsy management | 12 |
mobile phone | 12 |
squares structural | 12 |
nonpharmaceutical interventions | 12 |
west african | 12 |
moral norms | 12 |
infectious period | 12 |
global epidemic | 12 |
temporal network | 11 |
epidemic prevalence | 11 |
quarantine period | 11 |
refusal phase | 11 |
first one | 11 |
whole country | 11 |
data set | 11 |
april th | 11 |
social contact | 11 |
endemic areas | 11 |
case management | 11 |
bombay presidency | 11 |
forest region | 11 |
public places | 11 |
spectral radius | 11 |
statistical data | 11 |
may lead | 11 |
percolation thresholds | 11 |
infection spread | 11 |
intensity thresholds | 11 |
closely related | 11 |
less likely | 11 |
network structures | 11 |
decision makers | 11 |
first century | 11 |
based model | 11 |
explanatory models | 11 |
detection factors | 11 |
journal rspb | 11 |
disease will | 11 |
adaptive behavior | 11 |
deterministic model | 11 |
spatial structure | 11 |
across different | 11 |
close contact | 11 |
network topology | 11 |
mathematical model | 11 |
field models | 11 |
young adults | 11 |
informed individuals | 11 |
mem method | 11 |
intensive care | 11 |
modularity coefficient | 11 |
human population | 11 |
recent studies | 11 |
siys epidemic | 11 |
i pf | 11 |
differential equation | 11 |
natural language | 11 |
transition probability | 11 |
united kingdom | 11 |
future epidemics | 11 |
many cases | 11 |
zika virus | 11 |
vaccination campaign | 11 |
traffic accessibility | 11 |
epidemic plumes | 11 |
hand washing | 11 |
cell culture | 11 |
contact rates | 11 |
data mining | 11 |
coronavirus outbreak | 11 |
acquired immunity | 11 |
virus strains | 11 |
south gujarat | 11 |
high level | 11 |
composite parameter | 11 |
input data | 11 |
among people | 11 |
disease prevalence | 11 |
homogeneous mixing | 11 |
disruption duration | 11 |
border control | 11 |
attitudes toward | 11 |
suspended reality | 11 |
seasonal epidemics | 11 |
product function | 11 |
except hubei | 11 |
outbreak size | 11 |
protective behavior | 11 |
main text | 11 |
pregnant women | 11 |
confirmed covid | 11 |
pedv vaccines | 11 |
swine flu | 11 |
epidemic response | 11 |
field analysis | 11 |
case numbers | 11 |
local awareness | 11 |
epidemic preparedness | 10 |
service level | 10 |
suspected cases | 10 |
detection fraction | 10 |
moving epidemic | 10 |
new coronavirus | 10 |
mainland china | 10 |
independent variables | 10 |
human populations | 10 |
common areas | 10 |
berger hill | 10 |
making process | 10 |
capital asset | 10 |
amino acid | 10 |
epidemic event | 10 |
reduce transmission | 10 |
mixing patterns | 10 |
health workers | 10 |
population size | 10 |
viral shedding | 10 |
language category | 10 |
susceptible individual | 10 |
social science | 10 |
infection period | 10 |
among elderly | 10 |
grooms cmcm | 10 |
artificial intelligence | 10 |
rspb proc | 10 |
pharmaceutical interventions | 10 |
sf networks | 10 |
south america | 10 |
border screening | 10 |
current covid | 10 |
model based | 10 |
per million | 10 |
acceptance phase | 10 |
china except | 10 |
power law | 10 |
reproduction numbers | 10 |
epidemic method | 10 |
focus group | 10 |
law network | 10 |
travel restrictions | 10 |
infection probabilities | 10 |
molecular epidemiology | 10 |
personal hygiene | 10 |
becomes infected | 10 |
default model | 10 |
average degree | 10 |
epidemic characteristics | 10 |
health emergency | 10 |
random networks | 10 |
virus spread | 10 |
epidemic seasons | 10 |
second one | 10 |
future pandemics | 10 |
information spreading | 10 |
focus groups | 10 |
healthcare providers | 10 |
health centers | 10 |
healthcare workers | 10 |
intervention strategies | 10 |
latency time | 10 |
global awareness | 10 |
epidemic course | 10 |
vaccine strains | 10 |
public awareness | 10 |
type models | 10 |
human health | 10 |
become infected | 10 |
path coefficients | 10 |
percolation threshold | 10 |
wild animals | 10 |
critical infection | 10 |
major epidemic | 10 |
sexually transmitted | 10 |
world network | 10 |
ongoing covid | 10 |
entire population | 10 |
pedv isolates | 10 |
asian countries | 10 |
new zealand | 10 |
sensitivity chart | 10 |
environmental factors | 10 |
mathematical theory | 10 |
economic costs | 10 |
east respiratory | 10 |
random sampling | 10 |
epithelial cells | 10 |
behavioral response | 10 |
much lower | 10 |
dynamic network | 10 |
ib approach | 10 |
random graphs | 10 |
org journal | 10 |
wide range | 10 |
facilities conditions | 10 |
mathematical modeling | 9 |
monitoring data | 9 |
based analysis | 9 |
cases per | 9 |
borne diseases | 9 |
health problem | 9 |
significant difference | 9 |
risk populations | 9 |
commonly used | 9 |
economic losses | 9 |
individual i | 9 |
upper limit | 9 |
social mixing | 9 |
among others | 9 |
epidemic years | 9 |
fuzzy logic | 9 |
final epidemic | 9 |
directing attacks | 9 |
daily incidence | 9 |
new virus | 9 |
newly infected | 9 |
vaccination behavior | 9 |
largest eigenvalue | 9 |
will become | 9 |
different levels | 9 |
pandemic plans | 9 |
surveillance systems | 9 |
model parameters | 9 |
well known | 9 |
transmitted diseases | 9 |
different stages | 9 |
disease prevention | 9 |
covid epidemic | 9 |
voluntary vaccination | 9 |
multilayer networks | 9 |
air travel | 9 |
mixed population | 9 |
health outcomes | 9 |
see table | 9 |
ring vaccination | 9 |
neonatal piglets | 9 |
parameter estimates | 9 |
better understand | 9 |
infection control | 9 |
confined people | 9 |
weekly ili | 9 |
index case | 9 |
public authorities | 9 |
reality phase | 9 |
growth model | 9 |
hubei province | 9 |
immunization strategies | 9 |
virus strain | 9 |
posted june | 9 |
qualitative study | 9 |
randomly selected | 9 |
system size | 9 |
epidemic knowledge | 9 |
nan sha | 9 |
real world | 9 |
pig farms | 9 |
medical treatment | 9 |
functional configurations | 9 |
dynamical processes | 9 |
deterministic continuous | 9 |
reduce social | 9 |
based models | 9 |
viral rna | 9 |
intentionally directing | 9 |
cure rate | 9 |
protective immunity | 9 |
initial number | 9 |
pandemic strain | 9 |
new wave | 9 |
north america | 9 |
weaned pigs | 9 |
quarantine factor | 9 |
prevention measures | 9 |
epidemic situation | 9 |
expected number | 9 |
human behaviour | 9 |
infected patients | 9 |
spread rapidly | 9 |
attenuated pedv | 9 |
carlo simulation | 9 |
second peak | 9 |
flu pandemic | 9 |
effect size | 9 |
explanatory model | 9 |
early epidemic | 9 |
less effective | 9 |
pregnant sows | 9 |
secondary cases | 9 |
three phases | 9 |
may occur | 9 |
infected pigs | 9 |
network models | 9 |
time evolution | 9 |
spatial localisation | 9 |
table i | 9 |
coupling dynamics | 9 |
different scenarios | 9 |
mobility possibility | 9 |
literature review | 9 |
western africa | 9 |
abrupt exit | 9 |
mitigation strategies | 9 |
us outbreak | 9 |
respondents reported | 9 |
national health | 9 |
recent study | 9 |
susceptible population | 9 |
health problems | 9 |
epidemic exposure | 9 |
patient care | 9 |
policy objectives | 9 |
time constant | 9 |
varying networks | 9 |
democratic republic | 8 |
medical care | 8 |
hiv aids | 8 |
acceptance rate | 8 |
based quarantining | 8 |
statistical analysis | 8 |
logistic growth | 8 |
people will | 8 |
cluster areas | 8 |
tropical medicine | 8 |
spatial data | 8 |
cohort study | 8 |
protective measure | 8 |
community health | 8 |
theoretical results | 8 |
total cases | 8 |
human disease | 8 |
cases among | 8 |
health surveillance | 8 |
early modern | 8 |
cumulative infections | 8 |
many years | 8 |
task force | 8 |
health emergencies | 8 |
clustering coefficient | 8 |
general population | 8 |
different contexts | 8 |
real epidemic | 8 |
random graph | 8 |
erg approach | 8 |
emergency response | 8 |
behavioral framework | 8 |
analysis methods | 8 |
international health | 8 |
us consider | 8 |
i will | 8 |
frequently used | 8 |
group approach | 8 |
imported cases | 8 |
guangdong province | 8 |
coping strategies | 8 |
probability matrix | 8 |
salt lake | 8 |
one time | 8 |
prior epidemics | 8 |
crucial role | 8 |
risk mitigation | 8 |
health sector | 8 |
among young | 8 |
virus circulation | 8 |
indirect effects | 8 |
contacts among | 8 |
results indicate | 8 |
outbreak location | 8 |
i equals | 8 |
peak activity | 8 |
degree distributions | 8 |
known infectives | 8 |
logistic model | 8 |
case counts | 8 |
epidemic angle | 8 |
network model | 8 |
collective strategies | 8 |
one participant | 8 |
may still | 8 |
free state | 8 |
policy makers | 8 |
cumulative incidence | 8 |
working group | 8 |
progressive exit | 8 |
many people | 8 |
positive effect | 8 |
bmj global | 8 |
recall period | 8 |
three different | 8 |
also used | 8 |
computer science | 8 |
data using | 8 |
clinical features | 8 |
dv model | 8 |
initial condition | 8 |
calculated probability | 8 |
daily increase | 8 |
language processing | 8 |
ecohealth alliance | 8 |
ao spine | 8 |
based influence | 8 |
epidemiological model | 8 |
information transmission | 8 |
without prior | 8 |
absolute value | 8 |
high uncertainty | 8 |
public transportation | 8 |
influenza vaccination | 8 |
immunity level | 8 |
prior experience | 8 |
infected pneumonia | 8 |
see appendix | 8 |
mc simulation | 8 |
social support | 8 |
communicable disease | 8 |
syndromic surveillance | 8 |
online health | 8 |
shiga toxin | 8 |
southeast asia | 8 |
viral infections | 8 |
trend analysis | 8 |
three main | 8 |
local government | 8 |
markov chain | 8 |
coronavirus covid | 8 |
mixed populations | 8 |
becoming infected | 8 |
communication layer | 8 |
epidemic modeling | 8 |
early transmission | 8 |
almost always | 8 |
annual report | 8 |
spatial relationships | 8 |
disruption propagation | 8 |
epidemic setting | 8 |
among different | 8 |
temporal changes | 8 |
healthy individuals | 8 |
environmental health | 8 |
close contacts | 8 |
time delay | 8 |
scientific community | 8 |
large enough | 8 |
pattern recognition | 8 |
much higher | 8 |
global pandemic | 8 |
crypt cells | 8 |
mobility patterns | 8 |
network analysis | 8 |
previous epidemic | 8 |
strict quarantine | 8 |
early presentation | 8 |
study results | 8 |
open access | 8 |
new york | 8 |
sensitivity analysis | 8 |
primary health | 8 |
outbreak risk | 8 |
patients among | 7 |
also allows | 7 |
previous work | 7 |
time since | 7 |
will affect | 7 |
several countries | 7 |
available online | 7 |
virologic data | 7 |
long run | 7 |
networked populations | 7 |
vaccination policy | 7 |
spatial analysis | 7 |
spreading model | 7 |
supplementary material | 7 |
one region | 7 |
final state | 7 |
chronic diseases | 7 |
different populations | 7 |
initial phase | 7 |
viral load | 7 |
two weeks | 7 |
dynamic small | 7 |
models based | 7 |
coupling effect | 7 |
future work | 7 |
temporal contact | 7 |
based approach | 7 |
state vector | 7 |
potential domestic | 7 |
stem cells | 7 |
disease epidemiology | 7 |
health apps | 7 |
two methods | 7 |
infection dynamics | 7 |
specific vaccine | 7 |
percolation theory | 7 |
every day | 7 |
gnotobiotic pigs | 7 |
surgeons worldwide | 7 |
i state | 7 |
may affect | 7 |
transportation research | 7 |
informed level | 7 |
vaccination campaigns | 7 |
previous epidemics | 7 |
daily counts | 7 |
believed state | 7 |
critically ill | 7 |
small number | 7 |
rsv epidemic | 7 |
psychological impact | 7 |
seed region | 7 |
severe disease | 7 |
see also | 7 |
initial cases | 7 |
social interactions | 7 |
competing interests | 7 |
immune response | 7 |
exact solution | 7 |
stochastic discrete | 7 |
research part | 7 |
initial value | 7 |
critical conditions | 7 |
contact layer | 7 |
since infection | 7 |
low level | 7 |
term intervention | 7 |
early outbreak | 7 |
medical anthropology | 7 |
health measures | 7 |
economic impacts | 7 |
probability distribution | 7 |
rapid spread | 7 |
factors influencing | 7 |
family members | 7 |
epidemic pattern | 7 |
face masks | 7 |
whole epidemic | 7 |
one week | 7 |
elt service | 7 |
law enforcement | 7 |
around march | 7 |
epidemic among | 7 |
medical entities | 7 |
node degree | 7 |
epidemic reaches | 7 |
knowledge gaps | 7 |
sc risks | 7 |
study showed | 7 |
first epidemic | 7 |
spanish flu | 7 |
central africa | 7 |
related corpora | 7 |
natural hosts | 7 |
influenza vaccine | 7 |
ib models | 7 |
initial data | 7 |
statistical methods | 7 |
will help | 7 |
make sense | 7 |
two peaks | 7 |
rule induction | 7 |
wild meat | 7 |
influenza type | 7 |
many others | 7 |
healthcare systems | 7 |
stochastic simulation | 7 |
outbreak will | 7 |
fuzzy rule | 7 |
typical epidemic | 7 |
outbreak thresholds | 7 |
many governments | 7 |
first outbreak | 7 |
ili patients | 7 |
different industries | 7 |
decision rules | 7 |
antiviral drugs | 7 |
high intensity | 7 |
ncov outbreak | 7 |
predictive value | 7 |
phylogenetic analysis | 7 |
management framework | 7 |
equilibrium number | 7 |
composite mc | 7 |
life course | 7 |
healthy people | 7 |
infection arrival | 7 |
sequence analysis | 7 |
countries like | 7 |
human contacts | 7 |
densely populated | 7 |
dark web | 7 |
deep learning | 7 |
epidemic magnitude | 7 |
state transition | 7 |
risk groups | 7 |
paired comparison | 7 |
chronic disease | 7 |
new daily | 7 |
lgr cells | 7 |
epidemic trajectory | 7 |
membership functions | 7 |
towards epidemic | 7 |
two independent | 7 |
psychological distress | 7 |
generation time | 7 |
protective equipment | 7 |
demand disruption | 7 |
porcine circovirus | 7 |
large fraction | 7 |
analysis method | 7 |
three possible | 7 |
last years | 7 |
respiratory viruses | 7 |
many different | 7 |
international travel | 7 |
factors affecting | 7 |
classical sir | 7 |
infected persons | 7 |
social sciences | 7 |
populated areas | 7 |
local health | 7 |
infection propagator | 7 |
discrete time | 7 |
simple sir | 7 |
field theory | 7 |
phone data | 7 |
will reduce | 7 |
continuous solver | 7 |
historical data | 7 |
fuzzy numbers | 7 |
ili activity | 7 |
past days | 7 |
transmission potential | 7 |
commons attribution | 7 |
diagnostic laboratory | 7 |
daily new | 7 |
predicted results | 7 |
design method | 7 |
wash hands | 7 |
health policy | 7 |
random samples | 7 |
outpatient consultations | 7 |
ill patients | 7 |
partial mediation | 7 |
significant differences | 7 |
days later | 7 |
large scale | 7 |
african ebola | 7 |
peer pressure | 7 |
relevant data | 7 |
case definition | 7 |
ebola response | 7 |
data sharing | 7 |
immunity threshold | 7 |
social resistance | 7 |
epidemic start | 7 |
outbreak originating | 7 |
twitter data | 7 |
attitude towards | 7 |
first peak | 7 |
susceptible people | 7 |
next pandemic | 7 |
next section | 7 |
posted august | 7 |
emerging infections | 7 |
consider two | 7 |
personal protective | 7 |
decision support | 7 |
diamond princess | 7 |
new infectious | 7 |
case notification | 7 |
real data | 7 |
infected superspreaders | 7 |
disruption durations | 7 |
disease models | 7 |
hand hygiene | 7 |
china complete | 6 |
known disease | 6 |
interaction type | 6 |
small world | 6 |
planned behavior | 6 |
first infection | 6 |
medical experts | 6 |
almost certainly | 6 |
pandemic potential | 6 |
seven epidemic | 6 |
ordinary differential | 6 |
human animal | 6 |
emerging epidemic | 6 |
epidemic intelligence | 6 |
initial stage | 6 |
effective reproduction | 6 |
networks date | 6 |
network design | 6 |
worth noting | 6 |
future epidemic | 6 |
antibody response | 6 |
rumor may | 6 |
reported confirmed | 6 |
vary across | 6 |
health messages | 6 |
mobile phones | 6 |
global supply | 6 |
contact data | 6 |
logistic regression | 6 |
living conditions | 6 |
time epidemic | 6 |
chain network | 6 |
transformation process | 6 |
recovered cases | 6 |
princess cruise | 6 |
parameter values | 6 |
research area | 6 |
symptomatic individuals | 6 |
health institutions | 6 |
may change | 6 |
across seasons | 6 |
cruise ship | 6 |
disease carrier | 6 |
preventive behavioral | 6 |
live births | 6 |
vaccine efficacy | 6 |
criticality condition | 6 |
epidemic phase | 6 |
health status | 6 |
seidr model | 6 |
disease spreads | 6 |
relevant information | 6 |
will die | 6 |
notification data | 6 |
feedback process | 6 |
health risk | 6 |
rapid epidemic | 6 |
undetected infected | 6 |
control policy | 6 |
also play | 6 |
vaccination decisions | 6 |
pitting edema | 6 |
chinese center | 6 |
intending travelers | 6 |
explanatory power | 6 |
contagious individuals | 6 |
human infection | 6 |
daily infected | 6 |
population heterogeneity | 6 |
one another | 6 |
logistic curve | 6 |
forest guinea | 6 |
epidemic sustains | 6 |
much less | 6 |
national influenza | 6 |
data distributions | 6 |
may influence | 6 |
whole period | 6 |
simulation models | 6 |
precision harm | 6 |
disease vectors | 6 |
mutually exclusive | 6 |
severe diarrhea | 6 |
haiti cholera | 6 |
relatively large | 6 |
recovery probability | 6 |
virus ebola | 6 |
static small | 6 |
two states | 6 |
direct contact | 6 |
medical information | 6 |
possible infections | 6 |
civilian population | 6 |
commons licence | 6 |
italian cities | 6 |
like particles | 6 |
pandemic events | 6 |
influencing factors | 6 |
steady state | 6 |
significant impact | 6 |
cholera epidemic | 6 |
two aspects | 6 |
two equations | 6 |
reservoir animals | 6 |
peak around | 6 |
may cause | 6 |
epidemic threats | 6 |
renormalisation group | 6 |
natural history | 6 |
case reports | 6 |
interdisciplinary research | 6 |
use data | 6 |
nash equilibrium | 6 |
processing statistical | 6 |
epidemic patterns | 6 |
highly pathogenic | 6 |
term impacts | 6 |
two processes | 6 |
early days | 6 |
standard sir | 6 |
classical sis | 6 |
pandemic types | 6 |
sars outbreak | 6 |
static networks | 6 |
slightly different | 6 |
future trends | 6 |
cause disease | 6 |
drinking water | 6 |
prophylactic behavior | 6 |
rapidly evolving | 6 |
latin america | 6 |
personal health | 6 |
will give | 6 |
temperate climates | 6 |
study findings | 6 |
days disruption | 6 |
network theory | 6 |
path length | 6 |
rumor vs | 6 |
fake news | 6 |
spike protein | 6 |
first identified | 6 |
early dynamics | 6 |
three days | 6 |
among us | 6 |
content analysis | 6 |
oral route | 6 |
spreading probability | 6 |
population will | 6 |
compartmental model | 6 |
traveler numbers | 6 |
reducing interventions | 6 |
different time | 6 |
relatively high | 6 |
numerical analysis | 6 |
vaccine uptake | 6 |
standardized fractions | 6 |
top three | 6 |
epidemic diffusion | 6 |
transmitted disease | 6 |
data obtained | 6 |
infection risk | 6 |
collective dynamics | 6 |
highly connected | 6 |
information systems | 6 |
tweets related | 6 |
high prevalence | 6 |
edge number | 6 |
cubic curve | 6 |
relative importance | 6 |
diffusion process | 6 |
second half | 6 |
urgent need | 6 |
virus shedding | 6 |
general practitioners | 6 |
food products | 6 |
transmission patterns | 6 |
dynamic contact | 6 |
surgical treatment | 6 |
simulated results | 6 |
second phase | 6 |
abx resistance | 6 |
coronavirus pneumonia | 6 |
biological weapons | 6 |
believed node | 6 |
analysis tasks | 6 |
conceptual framework | 6 |
animal host | 6 |
depth interviews | 6 |
carrier species | 6 |
outbreak phenomenon | 6 |
aggregated graph | 6 |
high caste | 6 |
similar results | 6 |
regression analysis | 6 |
blue line | 6 |
structural model | 6 |
global spread | 6 |
average value | 6 |
cases may | 6 |
specific antibodies | 6 |
social interaction | 6 |
live attenuated | 6 |
management research | 6 |
study period | 6 |
confinement strategies | 6 |
may become | 6 |
large set | 6 |
susceptible state | 6 |
key epidemiological | 6 |
path modeling | 6 |
unintended consequences | 6 |
phase diagram | 6 |
ethnic group | 6 |
using social | 6 |
growth rates | 6 |
corresponding information | 6 |
geodetector method | 6 |
usa iowa | 6 |
villous atrophy | 6 |
outbreak locations | 6 |
fit index | 6 |
risk information | 6 |
pandemic will | 6 |
suppress epidemic | 6 |
individual level | 6 |
lactogenic immunity | 6 |
node i | 6 |
fuzzy sets | 6 |
epidemiological characteristics | 6 |
key role | 6 |
epilepsy control | 6 |
cumulative infection | 6 |
remains unchanged | 6 |
clinical trials | 6 |
epidemic states | 6 |
regular error | 6 |
neural network | 6 |
relatively small | 6 |
critical care | 6 |
node receives | 6 |
disease death | 6 |
elderly patients | 6 |
geometric mean | 6 |
watery diarrhea | 6 |
infected state | 6 |
virus outbreaks | 6 |
food hygiene | 6 |
like illness | 6 |
i index | 6 |
health regulations | 6 |
younger people | 6 |
media data | 6 |
rate peaks | 6 |
solid curve | 6 |
strict confinement | 6 |
infection scheme | 6 |
individual nodes | 6 |
complex network | 6 |
posted september | 6 |
emilia romagna | 6 |
empirical analysis | 6 |
square lattice | 6 |
mediating effect | 6 |
virulent virus | 6 |
erg framework | 6 |
sc risk | 6 |
social behavior | 6 |
high fever | 6 |
random variable | 6 |
ith day | 6 |
common sense | 6 |
time probabilities | 6 |
product functional | 6 |
bilateral pitting | 6 |
infectiousness function | 6 |
crisis management | 6 |
duration upstream | 6 |
social connections | 6 |
exit strategy | 6 |
epidemic diseases | 6 |
affected countries | 6 |
empirical results | 6 |
immune system | 6 |
near real | 6 |
high number | 6 |
heterogeneous populations | 6 |
transmission chain | 6 |
administrative units | 6 |
latency period | 6 |
also important | 6 |
infect humans | 6 |
varying reproduction | 6 |
given population | 6 |
use cases | 6 |
correlated transmission | 6 |
first case | 6 |
individual becomes | 6 |
across epidemic | 6 |
pairwise analysis | 6 |
isolation factor | 6 |
concurrent cases | 6 |
infected level | 6 |
information technology | 6 |
disinfectant wipes | 6 |
potential inaccuracy | 6 |
often used | 6 |
european centre | 6 |
intermediate value | 6 |
lockdown measures | 6 |
first phase | 6 |
disease severity | 6 |
third phase | 6 |
cov epidemic | 6 |
ili proportions | 6 |
endemic ped | 6 |
phase transition | 6 |
global scs | 6 |
ghsi score | 6 |
heterogeneous contact | 6 |
africa ebola | 6 |
will continue | 6 |
first two | 6 |
case importation | 6 |
least two | 6 |
differential diagnosis | 6 |
epidemiological modeling | 6 |
current study | 6 |
potential impact | 6 |
negative feedback | 6 |
epidemic ped | 6 |
vice versa | 6 |
disease epidemics | 6 |
precautionary measures | 6 |
may result | 6 |
two parameters | 6 |
using data | 6 |
epidemics occur | 6 |
current situation | 6 |
recovered state | 6 |
sc echelons | 6 |
publicly reported | 6 |
infections will | 5 |
atrophic enteritis | 5 |
i means | 5 |
may reduce | 5 |
epidemic state | 5 |
stock exchange | 5 |
second outbreak | 5 |
whole study | 5 |
epidemic research | 5 |
people may | 5 |
distancing interventions | 5 |
competitive diffusions | 5 |
several weeks | 5 |
adivasi populations | 5 |
homeland security | 5 |
inventory control | 5 |
deterministic data | 5 |
chain risk | 5 |
significantly different | 5 |
vaf value | 5 |
san francisco | 5 |
perceived feasibility | 5 |
detected factor | 5 |
influenza viruses | 5 |
disease emergence | 5 |
asymptomatic infection | 5 |
reduce covid | 5 |
human contact | 5 |
simulation model | 5 |
allow us | 5 |
will provide | 5 |
networks epidemics | 5 |
mitigation measures | 5 |
time markov | 5 |
community transmission | 5 |
severe ped | 5 |
must also | 5 |
high infected | 5 |
bubonic plague | 5 |
based studies | 5 |
disease burden | 5 |
much smaller | 5 |
different echelons | 5 |
epidemic weeks | 5 |
cumulative data | 5 |
vaccination strategy | 5 |
path analysis | 5 |
time intervals | 5 |
incidence rate | 5 |
fri rules | 5 |
brethren mission | 5 |
food safety | 5 |
million tweets | 5 |
endemic state | 5 |
pandemic outbreak | 5 |
gene source | 5 |
one day | 5 |
epidemic final | 5 |
medical devices | 5 |
home quarantine | 5 |
southern gujarat | 5 |
zoonotic origin | 5 |
simple random | 5 |
model fit | 5 |
last decade | 5 |
sis epidemics | 5 |
mild disease | 5 |
epidemic evolution | 5 |
possible outcomes | 5 |
cumulative curve | 5 |
international law | 5 |
case isolation | 5 |
one year | 5 |
infection may | 5 |
data available | 5 |
growing interest | 5 |
gender groups | 5 |
natural delay | 5 |
patients will | 5 |
considerably affects | 5 |
family coronaviridae | 5 |
correlation coefficient | 5 |
semantic variables | 5 |
also shown | 5 |
threshold increases | 5 |
network dynamics | 5 |
new pathogen | 5 |
rapidly emerging | 5 |
bfgs pnn | 5 |
research agenda | 5 |
time scale | 5 |
peak demand | 5 |
attitudes towards | 5 |
safe disposal | 5 |
epidemic trends | 5 |
incubation periods | 5 |
economic influence | 5 |
saharan africa | 5 |
may need | 5 |
geneva conventions | 5 |
tweets posted | 5 |
generalized logistic | 5 |
prediction interval | 5 |
epidemic weekly | 5 |
pedv vaccine | 5 |
epidemic must | 5 |
results also | 5 |
catastrophic events | 5 |
healthcare demand | 5 |
temporal dynamics | 5 |
transmission pattern | 5 |
based path | 5 |
perceived risk | 5 |
configuration model | 5 |
also show | 5 |
escherichia coli | 5 |
specimen collection | 5 |
vaccinated individuals | 5 |
products set | 5 |
recent research | 5 |
traditional healers | 5 |
model using | 5 |
influenza transmission | 5 |
predictive relevance | 5 |
mobility data | 5 |
surgeons reported | 5 |
structured population | 5 |
odds ratio | 5 |
first period | 5 |
naturally infected | 5 |
remain unchanged | 5 |
infection density | 5 |
individuals within | 5 |
stepwise exit | 5 |
linear dynamics | 5 |
will need | 5 |
epidemic infectivity | 5 |
although many | 5 |
unique sequence | 5 |
different groups | 5 |
porcine deltacoronavirus | 5 |
type i | 5 |
daily cost | 5 |
fuzzy rules | 5 |
affected patients | 5 |
epidemic starts | 5 |
words related | 5 |
causing great | 5 |
local people | 5 |
transmit disease | 5 |
whole time | 5 |
random sample | 5 |
health commission | 5 |
si model | 5 |
emerging diseases | 5 |
care facilities | 5 |
different social | 5 |
capacity building | 5 |
will cause | 5 |
impact assessment | 5 |
function configuration | 5 |
least square | 5 |
also include | 5 |
porcine plasma | 5 |
extremely difficult | 5 |
go beyond | 5 |
care practitioners | 5 |
new variants | 5 |
short term | 5 |
fewer cases | 5 |
epidemic normalization | 5 |
signal processing | 5 |
predict epidemics | 5 |
pedv rna | 5 |
known infected | 5 |
pedv specific | 5 |
control policies | 5 |
health agencies | 5 |
homogeneous networks | 5 |
school closure | 5 |
biennial cycle | 5 |
cb framework | 5 |
virus spreads | 5 |
consecutive weeks | 5 |
recovery processes | 5 |
subsidy policy | 5 |
rsv cases | 5 |
conjugate vaccine | 5 |
regular lattice | 5 |
unvaccinated individuals | 5 |
dynamic system | 5 |
different ages | 5 |
polymerase chain | 5 |
two factors | 5 |
critical unknowns | 5 |
observed values | 5 |
lake county | 5 |
limited resources | 5 |
tweet ids | 5 |
cent mortality | 5 |
mutual feedback | 5 |
infection searching | 5 |
diarrhoea caused | 5 |
age structure | 5 |
robert koch | 5 |
gathers momentum | 5 |
early intervention | 5 |
two years | 5 |
superspreading events | 5 |
simulation runs | 5 |
also cause | 5 |
effective way | 5 |
imperial college | 5 |
broad distribution | 5 |
infected sensitive | 5 |
fuzzy set | 5 |
survey results | 5 |
plague pandemic | 5 |
significant economic | 5 |
health doi | 5 |
cases occurred | 5 |
using different | 5 |
neutralizing antibodies | 5 |
animal disease | 5 |
infect dis | 5 |
new disease | 5 |
average peak | 5 |
cities gof | 5 |
cell cultures | 5 |
seventeenth century | 5 |
disease incidence | 5 |
optimal control | 5 |
short time | 5 |
detection analysis | 5 |
high case | 5 |
representative diseases | 5 |
additional information | 5 |
stochastic epidemic | 5 |
one may | 5 |
emerging outbreak | 5 |
biomedical model | 5 |
every time | 5 |
spread around | 5 |
pandemic plan | 5 |
medical doctors | 5 |
susceptible neighbors | 5 |
direct effect | 5 |
global epidemics | 5 |
first symptoms | 5 |
national pandemic | 5 |
special cases | 5 |
propagation dynamics | 5 |
medical science | 5 |
highly contagious | 5 |
virus detection | 5 |
por cine | 5 |
antibody responses | 5 |
new epidemic | 5 |
respiratory virus | 5 |
quantitative analysis | 5 |
epidemiological factors | 5 |
positive correlation | 5 |
good health | 5 |
mucosal immunity | 5 |
stochastic sir | 5 |
anylogistix simulation | 5 |
susceptible nodes | 5 |
human immunodeficiency | 5 |
percent error | 5 |
physical contacts | 5 |
care providers | 5 |
also provides | 5 |
test results | 5 |
cases like | 5 |
transmission process | 5 |
modified seir | 5 |
interview conducted | 5 |
prompt isolation | 5 |
supportive care | 5 |
higher risk | 5 |
epidemiological surveillance | 5 |
measures adopted | 5 |
daily case | 5 |
risk assessment | 5 |
three groups | 5 |
increasing number | 5 |
outbreak investigation | 5 |
modern epidemiology | 5 |
acute diarrhea | 5 |
western india | 5 |
potential solutions | 5 |
twitter corpora | 5 |
three geometric | 5 |
forecasting covid | 5 |
sufficiently small | 5 |
urgent surgery | 5 |
per capita | 5 |
transmission probability | 5 |
infection among | 5 |
mean field | 5 |
individuals will | 5 |
modified behavioral | 5 |
two key | 5 |
mental status | 5 |
family cluster | 5 |
believed neighbor | 5 |
free network | 5 |
completely different | 5 |
seasonal peak | 5 |
may spread | 5 |
new diseases | 5 |
awareness diffusion | 5 |
strict lockdown | 5 |
care units | 5 |
avoid unnecessary | 5 |
also called | 5 |
much greater | 5 |
riding behavior | 5 |
severe dehydration | 5 |
good agreement | 5 |
crisis situations | 5 |
wasting cases | 5 |
germ theory | 5 |
contact searching | 5 |
health conditions | 5 |
strains isolated | 5 |
scenario iii | 5 |
physical distancing | 5 |
disease ecology | 5 |
township health | 5 |
collateral effects | 5 |
experimental results | 5 |
elective surgeries | 5 |
design effect | 5 |
small perturbations | 5 |
vital role | 5 |
special case | 5 |
informed consent | 5 |
strain dr | 5 |
path coefficient | 5 |
impact size | 5 |
medical history | 5 |
social distance | 5 |
epidemic pedv | 5 |
epidemic analysis | 5 |
disease status | 5 |
human influenza | 5 |
also found | 5 |
rumor spreading | 5 |
believed density | 5 |
epidemiological reasoning | 5 |
per contact | 5 |
seven years | 5 |
networks temporal | 5 |
every node | 5 |
phenomenological models | 5 |
epidemic spreads | 5 |
infected arrivals | 5 |
expected values | 5 |
next generation | 5 |
determine whether | 5 |
also known | 5 |
also depends | 5 |
subscript i | 5 |
final phase | 5 |
will take | 5 |
shortest path | 5 |
coronavirus epidemic | 5 |
fuzzy probability | 5 |
spatially structured | 5 |
predictive power | 5 |
possible states | 5 |
health community | 5 |
infected animals | 5 |
national level | 5 |
maximum prevalence | 5 |
traded animals | 5 |
measures taken | 5 |
large epidemics | 5 |
infected without | 5 |
exploratory spatial | 5 |
relationships among | 5 |
third plague | 5 |
unconstrained epidemic | 5 |
three consecutive | 5 |
borne disease | 5 |
become available | 5 |
reported cases | 5 |
lifecourse epidemiology | 5 |
different models | 5 |
corona virus | 5 |
natural death | 5 |
i represents | 5 |
confirmed influenza | 5 |
italian regions | 5 |
outbreak propagation | 5 |
thematic analysis | 5 |
rsv epidemics | 5 |
interconnected networks | 5 |
research topics | 5 |
epidemic transmission | 5 |
access article | 5 |
observation time | 5 |
confidence intervals | 5 |
modified sis | 5 |
extreme cases | 5 |
period began | 5 |
error component | 5 |
pls path | 5 |
asset pricing | 5 |
infection rates | 5 |
higher value | 5 |
case finding | 5 |
simultaneous disruptions | 5 |
may provide | 5 |
epidemic area | 5 |
older people | 5 |
evd epidemic | 5 |
design development | 5 |
bushmeat ban | 5 |
prior infection | 5 |
adopt covid | 5 |
john snow | 5 |
indirect effect | 5 |
low risk | 5 |
special attention | 5 |
ili values | 5 |
specific case | 5 |
model captures | 5 |
saudi arabia | 5 |
attack rate | 5 |
regression models | 5 |
maximum value | 5 |
dynamic behavior | 5 |
dried porcine | 5 |
will see | 5 |
game dynamics | 5 |
person transmission | 5 |
epidemic time | 5 |
respiratory tract | 5 |
civil society | 5 |
pricing model | 5 |
structure networks | 5 |
unnatural epidemics | 5 |
based removal | 5 |
us pig | 5 |
state university | 5 |
readily available | 5 |
contact pattern | 5 |
protein genes | 5 |
direct cost | 5 |
virus epidemic | 5 |
pig population | 5 |
transmission routes | 5 |
total delay | 5 |
factors associated | 5 |
right column | 5 |
two representative | 5 |
rapid response | 5 |
epidemic information | 5 |
table shows | 5 |
elective surgery | 5 |
biomedical research | 5 |
will transmit | 5 |
spatial lifecourse | 5 |
every year | 5 |
table provides | 5 |
knowledge may | 5 |
sufficient condition | 5 |
among patients | 5 |
healthcare personnel | 5 |
cavity node | 5 |
diffusion processes | 5 |
care workers | 5 |
open reading | 5 |
also find | 5 |
sharp increase | 5 |
people living | 5 |
secondary infections | 5 |
epidemiological studies | 5 |
transmission will | 5 |
start date | 5 |
trapezoidal membership | 5 |
may vary | 5 |
parameter uncertainty | 5 |
global spatial | 5 |
research showed | 5 |
may call | 5 |
fatality ratio | 5 |
disease epidemic | 5 |
medical cost | 5 |
mass vaccination | 5 |
yemen cholera | 5 |
vast majority | 5 |
widely spread | 5 |
government officials | 5 |
poisson distribution | 5 |
adaptability factor | 5 |
structured populations | 5 |
numerical simulation | 5 |
real statistics | 5 |
i tot | 5 |
physical health | 5 |
fuzzy theory | 5 |
peak values | 5 |
community caregivers | 5 |
growth phase | 5 |
secretory syndrome | 5 |
viral hemorrhagic | 5 |
highly infectious | 5 |
current research | 5 |
community level | 4 |
age slice | 4 |
related information | 4 |
females aged | 4 |
homogeneously mixed | 4 |
travelers per | 4 |
severe villous | 4 |
data linkage | 4 |
new world | 4 |
precision public | 4 |
recovery probabilities | 4 |
human lives | 4 |
rapid review | 4 |
viral genomic | 4 |
peak delay | 4 |
result shows | 4 |
flu epidemic | 4 |
fuzzy number | 4 |
west nile | 4 |
recovery policies | 4 |
virus outbreak | 4 |
epidemic prediction | 4 |
air pollution | 4 |
new ways | 4 |
will depend | 4 |
vaccination strategies | 4 |
infected nursing | 4 |
mortality associated | 4 |
health professionals | 4 |
limited data | 4 |
consequently resulting | 4 |
made early | 4 |
rights reserved | 4 |
deterministic input | 4 |
host vulnerability | 4 |
sanitary commissioner | 4 |
daily income | 4 |
diffusion dynamics | 4 |
genomic rna | 4 |
seird model | 4 |
complex layered | 4 |
constant across | 4 |
epidemic modelling | 4 |
past years | 4 |
may play | 4 |
risk factor | 4 |
spatial contact | 4 |
travel ban | 4 |
social life | 4 |
breaking process | 4 |
virus variant | 4 |
without permission | 4 |
mainly focus | 4 |
uniform immunization | 4 |
health education | 4 |
optimization software | 4 |
also includes | 4 |
individuals infected | 4 |
information exchange | 4 |
epidemiological investigation | 4 |
downstream facilities | 4 |
epidemic development | 4 |
six weeks | 4 |
arriving passengers | 4 |
epidemiometric system | 4 |
either susceptible | 4 |
common cold | 4 |
water supply | 4 |
philip strong | 4 |
current status | 4 |
continuous dynamic | 4 |
i steps | 4 |
three authors | 4 |
pf usage | 4 |
first pandemic | 4 |
well described | 4 |
ongoing pandemic | 4 |
normal distribution | 4 |
bond percolation | 4 |
infectious pedv | 4 |
media posts | 4 |
dotted curve | 4 |
analytic estimations | 4 |
community education | 4 |
rapid detection | 4 |
novel influenza | 4 |
may well | 4 |
epidemic parameters | 4 |
solid line | 4 |
lower bound | 4 |
series analysis | 4 |
two vertices | 4 |
several months | 4 |
discharge criteria | 4 |
following formula | 4 |
one person | 4 |
healthy individual | 4 |
population may | 4 |
model epidemic | 4 |
mathematical tools | 4 |
disease reproduction | 4 |
two approaches | 4 |
wave events | 4 |
temporal differentiation | 4 |
diarrhea viruses | 4 |
emergency preparedness | 4 |
marginal probability | 4 |
management program | 4 |
single value | 4 |
zoonotic disease | 4 |
avian coronaviruses | 4 |
erg formalism | 4 |
global level | 4 |
absorbing state | 4 |
online shopping | 4 |
particular disease | 4 |
linearized dynamics | 4 |
severe hypercalcemia | 4 |
dynamics model | 4 |
statistics gathered | 4 |
layered networks | 4 |
dat max | 4 |
three general | 4 |
less sensitive | 4 |
shanghai stock | 4 |
dynamic processes | 4 |
local communities | 4 |
peter daszak | 4 |
development aid | 4 |
summed average | 4 |
epidemic modellers | 4 |
iata packing | 4 |
previously unknown | 4 |
pandemic planning | 4 |
bird flu | 4 |
get infected | 4 |
first study | 4 |
south china | 4 |
biotype caused | 4 |
pair approximations | 4 |
secret society | 4 |
goblet cells | 4 |
economic modeling | 4 |
much faster | 4 |
allowed us | 4 |
contacts per | 4 |
approach based | 4 |
potential future | 4 |
will almost | 4 |
temporal evolution | 4 |
will increase | 4 |
several interesting | 4 |
host immunity | 4 |
related diseases | 4 |
virus isolate | 4 |
shadowing lemma | 4 |
aggregated network | 4 |
among healthcare | 4 |
virus propagation | 4 |
meningococcal meningitis | 4 |
many studies | 4 |
intestinal crypt | 4 |
adequate ppe | 4 |
modified susceptible | 4 |
feedback loop | 4 |
cine epidemic | 4 |
major factor | 4 |
reproduction rate | 4 |
took place | 4 |
use iata | 4 |
national surveillance | 4 |
shanghai composite | 4 |
positive predictive | 4 |
surat district | 4 |
energy physics | 4 |
epidemic waves | 4 |
observed rsv | 4 |
psychological support | 4 |
different tools | 4 |
without crediting | 4 |
quarantine strategies | 4 |
clinical disease | 4 |
added delay | 4 |
outbreak period | 4 |
community networks | 4 |
control options | 4 |
complex system | 4 |
epidemic ends | 4 |
commonly identified | 4 |
networks effects | 4 |
great deal | 4 |
growth dynamics | 4 |
prevention strategies | 4 |
news content | 4 |
large value | 4 |
major epidemics | 4 |
trade network | 4 |
haggle network | 4 |
nipah virus | 4 |
affected communities | 4 |
psychological responses | 4 |
emerging pathogens | 4 |
deterministic variables | 4 |
birth rates | 4 |
genetic characterization | 4 |
integrated approach | 4 |
without considering | 4 |
italian cumulative | 4 |
expressed per | 4 |
medical staff | 4 |
highly sensitive | 4 |
grid search | 4 |
interacting spreading | 4 |
randomly chosen | 4 |
inflexion point | 4 |
interaction term | 4 |
epidemic progression | 4 |
us porcine | 4 |
identify key | 4 |
dynamic social | 4 |
layer system | 4 |
root mean | 4 |
second epidemic | 4 |
following analysis | 4 |
case definitions | 4 |
laboratory tests | 4 |
closing price | 4 |
beta functions | 4 |
human behavioral | 4 |
obtain information | 4 |
primary hyperparathyroidism | 4 |
spreading rate | 4 |
epidemic diar | 4 |
mckendrick model | 4 |
authors found | 4 |
left column | 4 |
pedv transmission | 4 |
packing instruction | 4 |
go outside | 4 |
knowledge diffusion | 4 |
coupled differential | 4 |
school age | 4 |
public domain | 4 |
epidemic crisis | 4 |
square root | 4 |
biohazard label | 4 |
many times | 4 |
differential detection | 4 |
new data | 4 |
behavioral sciences | 4 |
infected enterocytes | 4 |
national epidemic | 4 |
single infected | 4 |
purpose without | 4 |
infectious individuals | 4 |
participant stated | 4 |
live borne | 4 |
first deaths | 4 |
underlying time | 4 |
dengue virus | 4 |
postpandemic period | 4 |
aids epidemic | 4 |
new perspective | 4 |
bacterial meningitis | 4 |
driven epidemic | 4 |
rapid dissemination | 4 |
louis pasteur | 4 |
main factors | 4 |
viral epidemic | 4 |
epidemic length | 4 |
homogeneous network | 4 |
pathogenic avian | 4 |
original authors | 4 |
like strain | 4 |
case reporting | 4 |
biorisk management | 4 |
factor detection | 4 |
confirmed ili | 4 |
behavioral dynamics | 4 |
existing models | 4 |
new epidemics | 4 |
human host | 4 |
disruption time | 4 |
health facilities | 4 |
remote areas | 4 |
subjective norms | 4 |
experimentally infected | 4 |
present paper | 4 |
bayesian inference | 4 |
data used | 4 |
kivu ebola | 4 |
within households | 4 |
daily infection | 4 |
global public | 4 |
international armed | 4 |
watery diarrhoea | 4 |
geometric parametrization | 4 |
health reforms | 4 |
multiple sources | 4 |
lower mortality | 4 |
per individual | 4 |
whole villages | 4 |
molecular characterization | 4 |
epidemic game | 4 |
incident cases | 4 |
will focus | 4 |
event simulation | 4 |
international public | 4 |
understanding social | 4 |
er network | 4 |
urgently needed | 4 |
small values | 4 |
among respondents | 4 |
networks dynamical | 4 |
one imported | 4 |
convulsive epilepsy | 4 |
larger number | 4 |
takes place | 4 |
cultural models | 4 |
various scenarios | 4 |
multiple waves | 4 |
local information | 4 |
also provide | 4 |
admission rate | 4 |
set theory | 4 |
coronavirus infections | 4 |
useful tool | 4 |
common practice | 4 |
two examples | 4 |
prediction models | 4 |
aa substitutions | 4 |
article distributed | 4 |
epidemic renormalisation | 4 |
empirical study | 4 |
exposed people | 4 |
research group | 4 |
people worldwide | 4 |
general epidemic | 4 |
relatively mild | 4 |
work may | 4 |
multiple regions | 4 |
smartphone applications | 4 |
symptomatic infection | 4 |
ped vaccines | 4 |
geometric method | 4 |
large deletion | 4 |
will go | 4 |
outbreak sizes | 4 |
safety measures | 4 |
transmission risk | 4 |
psychological effects | 4 |
eradicating rumor | 4 |
theoretical models | 4 |
global network | 4 |
normalized epidemic | 4 |
asymptomatic transmission | 4 |
become endemic | 4 |
one hour | 4 |
class i | 4 |
new information | 4 |
patient isolation | 4 |
potentially conflicting | 4 |
legally share | 4 |
control strategy | 4 |
different locations | 4 |
size increases | 4 |
ivanov transportation | 4 |
medical symptoms | 4 |
table ii | 4 |
dynamic networks | 4 |
different sc | 4 |
confidentiality protection | 4 |
international distribution | 4 |
composite index | 4 |
analysis tools | 4 |
behavior models | 4 |
first three | 4 |
recovery period | 4 |
william farr | 4 |
demand disruptions | 4 |
experimental infection | 4 |
independent runs | 4 |
differential system | 4 |
antibiotic therapy | 4 |
using three | 4 |
preventive behaviors | 4 |
john graunt | 4 |
crispr cas | 4 |
high rates | 4 |
severe watery | 4 |
meningitis belt | 4 |
new eids | 4 |
recently infected | 4 |
interventions will | 4 |
asymmetrically interacting | 4 |
ebola en | 4 |
old people | 4 |
early estimation | 4 |
different parts | 4 |
digital medical | 4 |
chain reaction | 4 |
health epidemic | 4 |
undocumented infection | 4 |
global security | 4 |
based information | 4 |
pedv may | 4 |
spanish influenza | 4 |
general diseases | 4 |
logic programming | 4 |
haemorrhagic fever | 4 |
hemorrhagic fevers | 4 |
four weeks | 4 |
renormalization group | 4 |
phase space | 4 |
simulation experiments | 4 |
endocrine surgery | 4 |
informed susceptible | 4 |
will discuss | 4 |
infection curve | 4 |
spine surgery | 4 |
underlying network | 4 |
february th | 4 |
isaac newton | 4 |
dynamical interplay | 4 |
varying degrees | 4 |
new framework | 4 |
restrictive measures | 4 |
population growth | 4 |
two cases | 4 |
clinical trial | 4 |
sampling techniques | 4 |
outbreak phase | 4 |
modeling framework | 4 |
spike gene | 4 |
distancing strategy | 4 |
population movement | 4 |
invisible enemy | 4 |
annual epidemic | 4 |
matrix technique | 4 |
two parts | 4 |
best available | 4 |
will make | 4 |
lighter confinement | 4 |
fit statistics | 4 |
vaccination game | 4 |
noticeable increase | 4 |
deaths worldwide | 4 |
demographic data | 4 |
given time | 4 |
stock market | 4 |
models may | 4 |
upper bounds | 4 |
previous infectious | 4 |
scenario ii | 4 |
mediation model | 4 |
square test | 4 |
different ways | 4 |
open questions | 4 |
edge weights | 4 |
long term | 4 |
two days | 4 |
infected deceased | 4 |
regular component | 4 |
dotted line | 4 |
two layers | 4 |
secondary prevention | 4 |
epidemic strength | 4 |
effective interventions | 4 |
respiratory viral | 4 |
probability judgments | 4 |
boundary condition | 4 |
large group | 4 |
disease presents | 4 |
indel isolates | 4 |
vaccine coverage | 4 |
term interventions | 4 |
size distribution | 4 |
previous results | 4 |
monoclonal antibodies | 4 |
parenteral antibiotic | 4 |
valley fever | 4 |
different network | 4 |
mortality among | 4 |
first initiated | 4 |
activity rate | 4 |
uncontrolled epidemic | 4 |
human environment | 4 |
cities sequence | 4 |
adivasi tracts | 4 |
highest mortality | 4 |
probability distributions | 4 |
human extinction | 4 |
many researchers | 4 |
intermittent social | 4 |
first isolated | 4 |
new research | 4 |
negative binomial | 4 |
borne child | 4 |
parameters describing | 4 |
specific social | 4 |
contacts tracing | 4 |
first seizure | 4 |
particle associated | 4 |
urban centres | 4 |
best fit | 4 |
medical support | 4 |
anthropological perspective | 4 |
outcomes among | 4 |
esp weeks | 4 |
causes acute | 4 |
imported case | 4 |
network may | 4 |
armed conflict | 4 |
tourism industries | 4 |
infrastructure rehabilitation | 4 |
hostile party | 4 |
also consider | 4 |
using twitter | 4 |
high school | 4 |
new methods | 4 |
managerial insights | 4 |