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 |
---|---|
reproduction number | 269 |
total number | 245 |
social distancing | 154 |
infected individuals | 131 |
author funder | 128 |
granted medrxiv | 128 |
copyright holder | 123 |
novel coronavirus | 115 |
cumulative number | 111 |
version posted | 106 |
made available | 106 |
confirmed cases | 103 |
public health | 100 |
international license | 97 |
coronavirus disease | 91 |
incubation period | 90 |
left behind | 89 |
infectious diseases | 82 |
age groups | 76 |
exponential growth | 76 |
new cases | 74 |
age group | 73 |
fatality rate | 72 |
infected cases | 70 |
control measures | 70 |
infection rate | 70 |
posted may | 67 |
cord uid | 66 |
doc id | 66 |
per day | 65 |
medrxiv preprint | 65 |
total population | 64 |
basic reproduction | 63 |
contact rate | 62 |
average number | 61 |
sir model | 61 |
large number | 57 |
peer review | 56 |
behind passengers | 56 |
serial interval | 54 |
susceptible individuals | 53 |
detection rates | 52 |
reproduction numbers | 52 |
intensive care | 52 |
reproductive number | 50 |
population density | 50 |
new infections | 49 |
infectious disease | 49 |
south korea | 48 |
maximum number | 48 |
critical care | 48 |
transmission rate | 47 |
health care | 47 |
neural network | 45 |
infected people | 45 |
mean number | 45 |
united states | 44 |
expected number | 44 |
transmission dynamics | 43 |
time series | 42 |
final size | 42 |
daily number | 42 |
generation time | 42 |
standard deviation | 41 |
infectious period | 40 |
growth rate | 40 |
spillover transmission | 39 |
true number | 39 |
acute respiratory | 39 |
tourism revenue | 38 |
news sentiment | 37 |
respiratory syndrome | 37 |
reported cases | 37 |
world health | 37 |
digit plates | 36 |
influenza pandemic | 36 |
branching process | 35 |
sentiment analysis | 35 |
social isolation | 35 |
epidemic model | 35 |
infectious individuals | 34 |
incidental host | 34 |
infected persons | 34 |
secondary metabolites | 33 |
health organization | 33 |
fatality ratio | 33 |
net reproduction | 33 |
actual number | 33 |
herd immunity | 32 |
march th | 32 |
infection fatality | 32 |
reported infections | 32 |
pharmaceutical interventions | 31 |
severe acute | 31 |
effective reproduction | 30 |
infected individual | 30 |
positive samples | 30 |
system dynamics | 30 |
positive rate | 30 |
seir model | 30 |
second wave | 29 |
positive tests | 29 |
isolation measures | 29 |
co emissions | 29 |
case study | 29 |
global news | 29 |
detection rate | 29 |
energy consumption | 29 |
confirmed covid | 28 |
tests performed | 28 |
population mobility | 28 |
inbound tourists | 28 |
nan doi | 28 |
may th | 28 |
co emission | 28 |
direct transmission | 28 |
latent period | 28 |
rights reserved | 27 |
different countries | 27 |
new york | 27 |
case fatality | 27 |
contact tracing | 27 |
i i | 27 |
asymptomatic cases | 26 |
death rate | 26 |
clinical trials | 26 |
motor vehicles | 26 |
parameter values | 26 |
infected population | 26 |
small number | 25 |
epidemic curves | 25 |
available data | 25 |
hong kong | 25 |
video counts | 25 |
human mobility | 25 |
different types | 25 |
epidemic models | 25 |
urban transportation | 25 |
real time | 25 |
cases per | 24 |
household reproduction | 24 |
population size | 24 |
external factors | 24 |
susceptible population | 24 |
pandemic influenza | 24 |
time period | 24 |
case isolation | 24 |
representative testing | 23 |
previous section | 23 |
total fatalities | 23 |
sales data | 23 |
isolation beds | 23 |
positive cases | 23 |
deaths due | 23 |
health systems | 23 |
see table | 23 |
expected final | 23 |
initial number | 22 |
national lockdown | 22 |
daily new | 22 |
sd model | 22 |
table iv | 22 |
million citizens | 22 |
infection rates | 22 |
data collection | 22 |
epidemiological dynamics | 22 |
household quarantine | 22 |
time interval | 22 |
object detection | 22 |
genetic programming | 22 |
european countries | 22 |
much higher | 22 |
epidemiological threshold | 22 |
contact patterns | 22 |
large outbreak | 22 |
disease transmission | 21 |
personalized plates | 21 |
decision making | 21 |
normal distribution | 21 |
passengers left | 21 |
random testing | 21 |
data analytics | 21 |
public transportation | 21 |
ds theory | 21 |
years old | 21 |
recovery rate | 21 |
without permission | 20 |
time distribution | 20 |
mock communities | 20 |
allowed without | 20 |
linear growth | 20 |
economic activities | 20 |
reuse allowed | 20 |
defense compounds | 20 |
least one | 20 |
infected herds | 20 |
italian regions | 20 |
epidemic curve | 20 |
waiting times | 19 |
news articles | 19 |
fuel consumption | 19 |
private cars | 19 |
recovered individuals | 19 |
group testing | 19 |
tested positive | 19 |
gravity model | 19 |
instantaneous reproduction | 19 |
two different | 19 |
fuel tax | 19 |
modelling study | 19 |
response time | 19 |
infection model | 18 |
random variable | 18 |
upper bound | 18 |
systematic review | 18 |
linear regression | 18 |
mild symptoms | 18 |
i assume | 18 |
contact matrix | 18 |
icu beds | 18 |
linguistic term | 18 |
scientific community | 18 |
reported number | 18 |
natural products | 18 |
historical data | 18 |
climate change | 18 |
differential equations | 18 |
parking management | 18 |
allows us | 18 |
infected person | 18 |
transportation research | 18 |
may also | 18 |
posterior distribution | 18 |
results show | 18 |
monte carlo | 18 |
social interactions | 17 |
standard kaiju | 17 |
widely used | 17 |
every day | 17 |
asymptomatic infected | 17 |
disposable income | 17 |
individual disposable | 17 |
proposed model | 17 |
symptom onset | 17 |
statistically significant | 17 |
disease spread | 17 |
active cases | 17 |
private vehicles | 17 |
digit plate | 17 |
care beds | 17 |
simple square | 17 |
north station | 17 |
growth phase | 17 |
emerging infectious | 17 |
early stage | 17 |
proposed algorithm | 17 |
hesitant fuzzy | 17 |
branching processes | 17 |
healthcare system | 17 |
will increase | 17 |
partial infection | 17 |
detected cases | 17 |
research part | 17 |
severe symptoms | 17 |
infectious cases | 16 |
mean value | 16 |
table i | 16 |
visiting taiwan | 16 |
average time | 16 |
pandemic outbreak | 16 |
posted july | 16 |
mathematical models | 16 |
data available | 16 |
vehicular fuel | 16 |
diamond princess | 16 |
publicly available | 16 |
health interventions | 16 |
free bus | 16 |
health system | 16 |
model predictions | 16 |
round robin | 16 |
epidemiological parameters | 16 |
mobile phone | 16 |
distancing measures | 16 |
significant differences | 16 |
policy makers | 16 |
one week | 16 |
narx neural | 16 |
contact rates | 16 |
affected countries | 16 |
fuzzy linguistic | 16 |
take place | 16 |
care unit | 15 |
infected animals | 15 |
two months | 15 |
disease outbreaks | 15 |
will decrease | 15 |
reported confirmed | 15 |
corresponding date | 15 |
four days | 15 |
flow diversity | 15 |
health measures | 15 |
mobility rate | 15 |
transmission within | 15 |
york state | 15 |
reduce covid | 15 |
table shows | 15 |
sars coronavirus | 15 |
future covid | 15 |
hospital response | 15 |
two groups | 15 |
results suggest | 15 |
see appendix | 15 |
secondary infections | 15 |
model parameters | 15 |
school closures | 15 |
per age | 15 |
health authorities | 15 |
mortality rate | 15 |
susceptible people | 15 |
healthcare demand | 15 |
generalized gamma | 15 |
taxonomic classification | 15 |
posted october | 15 |
early stages | 15 |
hc model | 15 |
estimated coefficients | 15 |
cases will | 14 |
united kingdom | 14 |
results indicate | 14 |
arriving flights | 14 |
testing data | 14 |
general social | 14 |
will also | 14 |
probability distribution | 14 |
hospitalization rate | 14 |
dynamics model | 14 |
syndrome coronavirus | 14 |
place closures | 14 |
motorcycle parking | 14 |
gamma function | 14 |
synthetic control | 14 |
may lead | 14 |
door policy | 14 |
days since | 14 |
per capita | 14 |
avian influenza | 13 |
many cases | 13 |
regression analysis | 13 |
per animal | 13 |
oil price | 13 |
median age | 13 |
around days | 13 |
per million | 13 |
many countries | 13 |
mixing patterns | 13 |
strict measures | 13 |
symptomatic cases | 13 |
first case | 13 |
dwell time | 13 |
data collected | 13 |
asymptomatic individuals | 13 |
allow us | 13 |
even though | 13 |
contagious people | 13 |
explanatory variables | 13 |
data used | 13 |
mechanical thrombectomies | 13 |
different regions | 13 |
using data | 13 |
daily cases | 13 |
apparent prevalence | 13 |
parameter uncertainty | 13 |
kaohsiung city | 13 |
suspected cases | 13 |
times higher | 13 |
estimated number | 13 |
epidemic dynamics | 13 |
hand side | 13 |
species composition | 13 |
human microbiome | 13 |
care system | 13 |
exponential model | 13 |
pandemic period | 13 |
first detection | 13 |
deviation days | 13 |
wide range | 13 |
numerical simulations | 13 |
coronavirus infections | 13 |
confirmed sars | 13 |
publicly reported | 13 |
distribution function | 13 |
weak control | 13 |
exposed individuals | 13 |
countries territories | 13 |
effective reproductive | 13 |
isolation capacity | 13 |
interquartile range | 13 |
fuel prices | 13 |
final number | 13 |
prevalence rate | 12 |
first one | 12 |
relative abundances | 12 |
daily social | 12 |
compartment i | 12 |
competitiveness report | 12 |
epidemiological models | 12 |
many alkaloids | 12 |
simulation results | 12 |
first wave | 12 |
next section | 12 |
passenger counts | 12 |
care units | 12 |
zoonotic pathogens | 12 |
tourists visiting | 12 |
infective herds | 12 |
control strategies | 12 |
human population | 12 |
travel restrictions | 12 |
initial value | 12 |
infected pneumonia | 12 |
household transmission | 12 |
total cases | 12 |
antimicrobial consumption | 12 |
world tourism | 12 |
epidemiological characteristics | 12 |
peak number | 12 |
healing time | 12 |
reported deaths | 12 |
dark red | 12 |
control signal | 12 |
hospitalized people | 12 |
santa clara | 12 |
clinical characteristics | 12 |
individuals infected | 12 |
i th | 12 |
infections per | 12 |
million people | 12 |
linguistic terms | 12 |
household size | 12 |
microbial communities | 12 |
attack rate | 12 |
black dots | 12 |
year age | 12 |
basic reproductive | 12 |
birth rate | 12 |
winning bids | 12 |
first day | 12 |
passengers waiting | 12 |
short period | 12 |
shapley value | 12 |
early phase | 12 |
electron microscopy | 12 |
disease dynamics | 12 |
chemical defense | 12 |
mathematical modelling | 12 |
given time | 12 |
susceptible persons | 12 |
cruise ship | 11 |
wuhan city | 11 |
different levels | 11 |
natural logarithm | 11 |
individual birth | 11 |
using equation | 11 |
mean daily | 11 |
regression model | 11 |
gompertz growth | 11 |
well known | 11 |
markov chain | 11 |
generating function | 11 |
economic development | 11 |
early april | 11 |
urban population | 11 |
time since | 11 |
covid outbreak | 11 |
video feeds | 11 |
best fit | 11 |
differences among | 11 |
varying contact | 11 |
initially infected | 11 |
care workers | 11 |
ordinary plates | 11 |
one day | 11 |
first days | 11 |
simulated data | 11 |
blue curve | 11 |
contacts per | 11 |
special plates | 11 |
output variables | 11 |
will occur | 11 |
time days | 11 |
shotgun sequencing | 11 |
southeast asia | 11 |
time varying | 11 |
spatial distribution | 11 |
acds per | 11 |
evidential reasoning | 11 |
herd infected | 11 |
swine flu | 11 |
many people | 11 |
much larger | 11 |
initial state | 11 |
simple exponential | 11 |
public transport | 11 |
daily growth | 11 |
ebola virus | 11 |
social media | 11 |
stringency measures | 11 |
linear regime | 11 |
personal protection | 11 |
based algorithm | 11 |
global competitiveness | 11 |
license plates | 11 |
optimal confinement | 11 |
much lower | 11 |
index case | 11 |
infective herd | 11 |
national level | 11 |
clinical studies | 11 |
machine learning | 11 |
statistical analysis | 11 |
nonpharmaceutical interventions | 11 |
transportation system | 11 |
relative abundance | 11 |
authors declare | 11 |
initial conditions | 11 |
bad times | 11 |
host plant | 11 |
variables indicated | 11 |
group i | 11 |
supporting information | 11 |
contact matrices | 11 |
mortality rates | 10 |
transmission rates | 10 |
data sources | 10 |
customers allowed | 10 |
google trends | 10 |
antimicrobial use | 10 |
classical swine | 10 |
early transmission | 10 |
home order | 10 |
mechanical thrombectomy | 10 |
evaluation grades | 10 |
princess cruise | 10 |
term sets | 10 |
simple model | 10 |
epidemic spread | 10 |
deterministic continuous | 10 |
risk factors | 10 |
nan sha | 10 |
pool size | 10 |
significantly different | 10 |
elderly people | 10 |
one individual | 10 |
mobility reduction | 10 |
incubation cases | 10 |
surveillance video | 10 |
evidence theory | 10 |
marginal effect | 10 |
performed using | 10 |
newly confirmed | 10 |
saudi arabia | 10 |
ifr value | 10 |
future prediction | 10 |
obtained using | 10 |
bus service | 10 |
quinolizidine alkaloids | 10 |
middle east | 10 |
logistic regression | 10 |
will tend | 10 |
power law | 10 |
intervention period | 10 |
latent dirichlet | 10 |
maximum value | 10 |
five days | 10 |
based model | 10 |
real numbers | 10 |
threshold value | 10 |
dirichlet allocation | 10 |
community mobility | 10 |
swine fever | 10 |
growth models | 10 |
ongoing pandemic | 10 |
donor group | 10 |
host plants | 10 |
barely contagious | 10 |
waiting time | 10 |
across different | 10 |
differential equation | 10 |
mean values | 10 |
main text | 10 |
control action | 10 |
fatality ratios | 10 |
model predicts | 10 |
different methods | 10 |
infection within | 10 |
contact network | 10 |
attack rates | 10 |
camera views | 10 |
peak demand | 10 |
weibull distribution | 10 |
predicted number | 10 |
competing interests | 10 |
three days | 10 |
asymptomatic patients | 10 |
real world | 10 |
individual reproduction | 9 |
approximate counting | 9 |
transportation systems | 9 |
become infected | 9 |
several countries | 9 |
large population | 9 |
input data | 9 |
random sampling | 9 |
optimal intensity | 9 |
research areas | 9 |
manual counts | 9 |
long term | 9 |
one herd | 9 |
economic growth | 9 |
quadratic model | 9 |
light trucks | 9 |
testing travellers | 9 |
initial exponential | 9 |
online news | 9 |
correlation coefficient | 9 |
daily expenditures | 9 |
mobility data | 9 |
staff members | 9 |
steroidal alkaloids | 9 |
real data | 9 |
time spent | 9 |
grey curve | 9 |
air quality | 9 |
large numbers | 9 |
viral transmissibility | 9 |
transit stations | 9 |
time slots | 9 |
constant environment | 9 |
negativity index | 9 |
lag time | 9 |
two weeks | 9 |
two main | 9 |
stroke alerts | 9 |
gamma distribution | 9 |
us population | 9 |
individual death | 9 |
healthcare expenditure | 9 |
two variables | 9 |
relatively small | 9 |
leaving behind | 9 |
spatial localisation | 9 |
large fraction | 9 |
exponentially distributed | 9 |
case reproduction | 9 |
tests per | 9 |
amplicon sequencing | 9 |
seven nations | 9 |
patch model | 9 |
hubei province | 9 |
class parameters | 9 |
artificial intelligence | 9 |
positive test | 9 |
infected herd | 9 |
optimization problem | 9 |
hard measures | 9 |
daily deaths | 9 |
city buses | 9 |
unreported cases | 9 |
important role | 9 |
test result | 9 |
variable length | 9 |
asymptomatic infections | 9 |
data using | 9 |
stability condition | 9 |
data obtained | 9 |
positive correlation | 9 |
rrna gene | 9 |
measures taken | 9 |
fixed effect | 9 |
medical resources | 9 |
disease control | 9 |
tourism organization | 9 |
people infected | 9 |
infective animals | 9 |
will continue | 9 |
months ahead | 9 |
stochastic model | 9 |
entire population | 9 |
generation times | 9 |
image processing | 9 |
feedforward neural | 9 |
epidemic spreading | 9 |
microbial community | 9 |
stage approach | 9 |
heavy trucks | 9 |
standard errors | 9 |
transport contacts | 9 |
natural selection | 9 |
start date | 9 |
time periods | 9 |
secondary cases | 8 |
region movements | 8 |
ion channels | 8 |
homo sapiens | 8 |
daily basis | 8 |
mass testing | 8 |
high proportion | 8 |
based seroepidemiological | 8 |
great britain | 8 |
asymptomatic proportion | 8 |
social mixing | 8 |
stock variable | 8 |
log likelihood | 8 |
reviewed drafts | 8 |
rapid dissemination | 8 |
severity index | 8 |
illness onset | 8 |
digit ordinary | 8 |
orange curve | 8 |
prevention measures | 8 |
exponential phase | 8 |
sensitivity analysis | 8 |
mentioned earlier | 8 |
lombardy region | 8 |
removed individuals | 8 |
within households | 8 |
smooth muscle | 8 |
raw data | 8 |
first stage | 8 |
time intervals | 8 |
neural networks | 8 |
top left | 8 |
confirmed infected | 8 |
rhymes similarly | 8 |
payment phase | 8 |
stock market | 8 |
two cases | 8 |
i jk | 8 |
control policies | 8 |
will reduce | 8 |
reproductive ratio | 8 |
state vector | 8 |
large scale | 8 |
pest county | 8 |
infected patients | 8 |
cumulated number | 8 |
new method | 8 |
posted june | 8 |
co trend | 8 |
million population | 8 |
exactly equal | 8 |
days later | 8 |
dashed lines | 8 |
mathematical theory | 8 |
molecular targets | 8 |
will experience | 8 |
one infected | 8 |
high level | 8 |
entire country | 8 |
threshold values | 8 |
drug discovery | 8 |
save lives | 8 |
will lead | 8 |
social contact | 8 |
peak icu | 8 |
final draft | 8 |
specific interventions | 8 |
time course | 8 |
defense compound | 8 |
data analysis | 8 |
will show | 8 |
time dependent | 8 |
next generation | 8 |
shotgun metagenomics | 8 |
growth rates | 8 |
different strategies | 8 |
interval i | 8 |
healthy susceptible | 8 |
united nations | 8 |
infection facilitates | 8 |
imperial college | 8 |
higher compared | 8 |
lower bound | 8 |
get infected | 8 |
negative correlation | 8 |
bed demand | 8 |
length vector | 8 |
creative commons | 8 |
point process | 8 |
substantial undocumented | 8 |
person comes | 8 |
epidemic growth | 8 |
largest number | 8 |
capital cities | 8 |
th generation | 8 |
highly sensitive | 8 |
trends data | 8 |
undocumented infection | 8 |
icu demand | 8 |
different research | 8 |
education index | 8 |
absence data | 8 |
will die | 8 |
performed daily | 8 |
poisson point | 8 |
vehicular energy | 8 |
estimated based | 8 |
particular herd | 8 |
asymptomatic people | 8 |
positive sample | 8 |
treatment intensity | 8 |
total numbers | 8 |
varying reproduction | 8 |
disease outbreak | 8 |
growth model | 8 |
second peak | 8 |
stage i | 8 |
reference model | 8 |
discount coefficient | 8 |
estimated using | 8 |
th excursion | 8 |
death toll | 8 |
treatment unit | 8 |
new daily | 8 |
critical cases | 8 |
mathematical model | 8 |
incubation time | 8 |
small mock | 8 |
orange line | 8 |
per unit | 8 |
april th | 8 |
country i | 8 |
reasonable number | 8 |
across countries | 8 |
east respiratory | 8 |
commons attribution | 8 |
reported data | 8 |
phone data | 8 |
infected fraction | 8 |
data sets | 8 |
false positive | 8 |
wearable sensors | 8 |
presence absence | 8 |
also consider | 8 |
linear fit | 8 |
breeding bird | 8 |
numerical experiments | 8 |
since infection | 8 |
healthcare workers | 8 |
care facilities | 8 |
logistic growth | 8 |
international spread | 8 |
mitigation efforts | 8 |
reducing total | 8 |
ds evidence | 8 |
posterior median | 8 |
paraffin embedded | 8 |
epidemical control | 8 |
proposed approach | 7 |
high risk | 7 |
different age | 7 |
waste products | 7 |
surgical cases | 7 |
credibility interval | 7 |
fully susceptible | 7 |
high probability | 7 |
deaths million | 7 |
square model | 7 |
calculated using | 7 |
daily fatality | 7 |
contact data | 7 |
total tourism | 7 |
present study | 7 |
mitigation measures | 7 |
white indicates | 7 |
school closure | 7 |
hospital beds | 7 |
commonly used | 7 |
north america | 7 |
human transmission | 7 |
one another | 7 |
obtained results | 7 |
administrative staff | 7 |
poisson distribution | 7 |
another important | 7 |
hospital bed | 7 |
proportional controller | 7 |
different groups | 7 |
mobility flows | 7 |
better understand | 7 |
si table | 7 |
southern regions | 7 |
standard error | 7 |
esvac project | 7 |
contact reduction | 7 |
relatively low | 7 |
cohort study | 7 |
tourism competitiveness | 7 |
adhesion molecule | 7 |
individual based | 7 |
continuous time | 7 |
need hospitalization | 7 |
different population | 7 |
parameter estimation | 7 |
generation matrix | 7 |
highest number | 7 |
also use | 7 |
predicted values | 7 |
turning point | 7 |
hospital admission | 7 |
optimal values | 7 |
age distribution | 7 |
global health | 7 |
ending social | 7 |
cumulative detection | 7 |
ordinary differential | 7 |
varying environment | 7 |
north american | 7 |
case scenario | 7 |
infections generated | 7 |
larger number | 7 |
discrete time | 7 |
antiviral drugs | 7 |
develop symptoms | 7 |
gp trees | 7 |
modelling approach | 7 |
economic damage | 7 |
two regions | 7 |
antimicrobial usage | 7 |
statistical significance | 7 |
metric tons | 7 |
respiratory disease | 7 |
preliminary results | 7 |
total duration | 7 |
submitted work | 7 |
dairy cows | 7 |
tourism demand | 7 |
ncov outbreak | 7 |
infectious agent | 7 |
one hand | 7 |
situation report | 7 |
containment measures | 7 |
mass screening | 7 |
hospitalized patients | 7 |
active infections | 7 |
statistical computing | 7 |
different epidemic | 7 |
covidsim code | 7 |
results presented | 7 |
decreasing trend | 7 |
agenda setting | 7 |
emerging epidemic | 7 |
two curves | 7 |
proposed method | 7 |
daily infections | 7 |
high number | 7 |
higher detection | 7 |
demographic data | 7 |
solar radiation | 7 |
may occur | 7 |
magic trick | 7 |
population groups | 7 |
secondary products | 7 |
optimal number | 7 |
donor units | 7 |
developed countries | 7 |
day lag | 7 |
first time | 7 |
simple models | 7 |
three times | 7 |
amino acids | 7 |
tracing day | 7 |
largest outbreak | 7 |
classification methods | 7 |
making method | 7 |
measures adopted | 7 |
viral load | 7 |
true value | 7 |
unit time | 7 |
number estimated | 7 |
fuel price | 7 |
shopping area | 7 |
previous studies | 7 |
three small | 7 |
plant species | 7 |
mitigation scenarios | 7 |
negative sentiment | 7 |
stochastic discrete | 7 |
virus disease | 7 |
recovered cases | 7 |
two types | 7 |
provide estimates | 7 |
also shown | 7 |
allowed inside | 7 |
mobility reports | 7 |
will need | 7 |
allowed us | 7 |
logistic models | 7 |
first week | 7 |
analysis showed | 7 |
infected household | 7 |
ascertainment rate | 7 |
worth noting | 7 |
course doses | 7 |
isolation facilities | 7 |
potential domestic | 7 |
highly dependent | 7 |
poisson process | 7 |
one primary | 7 |
likelihood function | 7 |
destination nation | 7 |
small cell | 7 |
economic intuition | 7 |
research works | 7 |
contact events | 7 |
information stream | 7 |
seasonal influenza | 7 |
index values | 7 |
chaotic maps | 7 |
also observed | 7 |
early days | 7 |
exposed people | 7 |
two decades | 7 |
core families | 7 |
one month | 7 |
pandemic processes | 7 |
outbreak originating | 7 |
measures will | 7 |
higher risk | 7 |
clinical severity | 7 |
epidemic threshold | 7 |
automated passenger | 7 |
communicable diseases | 7 |
authors wish | 7 |
whole genome | 7 |
control interventions | 7 |
emergency department | 7 |
reported numbers | 7 |
require hospitalization | 7 |
maximum likelihood | 7 |
saving lives | 7 |
evaluation levels | 7 |
access article | 7 |
first two | 7 |
dna sudoku | 7 |
virus spread | 7 |
partial restarting | 7 |
train doors | 7 |
go extinct | 7 |
limited number | 7 |
medical care | 7 |
testing efforts | 7 |
face masks | 7 |
statistical methods | 7 |
viral disease | 7 |
reasoning approach | 7 |
transportation policies | 7 |
slow reduction | 7 |
mobility diversity | 7 |
older age | 7 |
set point | 7 |
bird flu | 7 |
herd spread | 7 |
naive bayes | 7 |
doors close | 7 |
see section | 7 |
recent years | 7 |
epidemiological measures | 7 |
mathematical modeling | 7 |
data include | 7 |
past two | 7 |
three years | 7 |
continuous solver | 7 |
one case | 7 |
article distributed | 7 |
bessel functions | 7 |
infected multipliers | 7 |
epidemiological model | 6 |
behavioural adaptations | 6 |
average rate | 6 |
hospitalized cases | 6 |
alerts codes | 6 |
risk space | 6 |
virus outbreak | 6 |
infect others | 6 |
multiple comparisons | 6 |
daily temperature | 6 |
past years | 6 |
different threshold | 6 |
population will | 6 |
track covid | 6 |
weibull pdf | 6 |
sample pooling | 6 |
infection dynamics | 6 |
must also | 6 |
protease inhibitors | 6 |
sequencing data | 6 |
model based | 6 |
epidemic will | 6 |
microscopic examination | 6 |
peak hospital | 6 |
predictive power | 6 |
emission reduction | 6 |
score function | 6 |
clustering methods | 6 |
immune system | 6 |
model using | 6 |
mass function | 6 |
may help | 6 |
yet detected | 6 |
time delay | 6 |
health status | 6 |
infected i | 6 |
also used | 6 |
many studies | 6 |
significant decrease | 6 |
medical system | 6 |
median time | 6 |
already infected | 6 |
beds per | 6 |
last day | 6 |
strong control | 6 |
automated video | 6 |
time stochastic | 6 |
based dashboard | 6 |
lives lost | 6 |
listed prices | 6 |
french population | 6 |
rfid tag | 6 |
gradual decrease | 6 |
corresponding tourism | 6 |
elective cases | 6 |
afinn lexicon | 6 |
departing train | 6 |
cumulative distribution | 6 |
makes sense | 6 |
search interest | 6 |
ground truth | 6 |
good fit | 6 |
traffic density | 6 |
truncated stick | 6 |
transmission model | 6 |
macroeconomic environments | 6 |
fit performed | 6 |
one year | 6 |
bird groups | 6 |
defense chemicals | 6 |
essential economic | 6 |
cases generated | 6 |
second stage | 6 |
dead time | 6 |
optimal initial | 6 |
average daily | 6 |
predicted relative | 6 |
dramatically reduce | 6 |
influenza virus | 6 |
social distance | 6 |
tracking records | 6 |
market index | 6 |
synthetic policy | 6 |
developed models | 6 |
food producing | 6 |
data will | 6 |
vehicles will | 6 |
death process | 6 |
epidemic indicators | 6 |
contagious individuals | 6 |
many instances | 6 |
cell carcinomas | 6 |
also provide | 6 |
convalescent plasma | 6 |
sir models | 6 |
observed data | 6 |
paramedical staff | 6 |
management policies | 6 |
fuel efficiency | 6 |
health point | 6 |
new covid | 6 |
rfid tags | 6 |
manual observations | 6 |
parameter estimates | 6 |
total cost | 6 |
determine whether | 6 |
red dots | 6 |
th day | 6 |
abundance data | 6 |
eight days | 6 |
icu bed | 6 |
breaking prior | 6 |
will change | 6 |
directly related | 6 |
pooling methods | 6 |
flow diversities | 6 |
primary infector | 6 |
exponentially growing | 6 |
national health | 6 |
different parameters | 6 |
symptomatic infected | 6 |
becomes infected | 6 |
may provide | 6 |
vaccine development | 6 |
news negativity | 6 |
determined using | 6 |
infection level | 6 |
genus level | 6 |
urban transport | 6 |
passengers entering | 6 |
forecasting tool | 6 |
probability density | 6 |
detected infections | 6 |
lockdown period | 6 |
human behavioural | 6 |
tables i | 6 |
stroke care | 6 |
factors like | 6 |
algorithm predicts | 6 |
train tracking | 6 |
previous study | 6 |
tested individuals | 6 |
large populations | 6 |
recovery period | 6 |
process approximation | 6 |
within cities | 6 |
recovered people | 6 |
compartmental model | 6 |
different macroeconomic | 6 |
incubation periods | 6 |
veneto region | 6 |
january th | 6 |
movie reviews | 6 |
decision matrix | 6 |
correct number | 6 |
sentiment category | 6 |
stuttering chains | 6 |
vice versa | 6 |
will use | 6 |
function defined | 6 |
critically ill | 6 |
population carrying | 6 |
approximately equal | 6 |
financial relationships | 6 |
fixed point | 6 |
big data | 6 |
infection probability | 6 |
daily tests | 6 |
will become | 6 |
peak hours | 6 |
randomly chosen | 6 |
healthy human | 6 |
batch size | 6 |
incidence rate | 6 |
case numbers | 6 |
new zealand | 6 |
frost model | 6 |
daily death | 6 |
chain monte | 6 |
cell type | 6 |
mild cases | 6 |
pandemic compared | 6 |
scenario without | 6 |
low number | 6 |
stochastic processes | 6 |
global scale | 6 |
tests required | 6 |
reproductive numbers | 6 |
symptomatic individuals | 6 |
seroepidemiological studies | 6 |
real number | 6 |
confirmed case | 6 |
potential impact | 6 |
good approximation | 6 |
large excursion | 6 |
good estimate | 6 |
different days | 6 |
mle method | 6 |
model also | 6 |
rush period | 6 |
will see | 6 |
italian provinces | 6 |
interactive web | 6 |
early dynamics | 6 |
one infective | 6 |
private healthcare | 6 |
time estimation | 6 |
gaussian process | 6 |
countries like | 6 |
less toxic | 6 |
null flow | 6 |
carrying capacity | 6 |
log link | 6 |
true epidemic | 6 |
one hour | 6 |
based analysis | 6 |
epidemic data | 6 |
first step | 6 |
substantial number | 6 |
transmission process | 6 |
infections caused | 6 |
fuzzy numbers | 6 |
simulation model | 6 |
arrival rate | 6 |
prior distribution | 6 |
herd immunization | 6 |
different nations | 6 |
many factors | 6 |
protective equipment | 6 |
method based | 6 |
age index | 6 |
group decision | 6 |
certain number | 6 |
following parameters | 6 |
given day | 6 |
bottom panels | 6 |
fatality rates | 6 |
adenylate cyclase | 6 |
higher number | 6 |
test positive | 6 |
social contacts | 6 |
people tested | 6 |
transportation sector | 6 |
million cases | 6 |
sis epidemic | 6 |
traditional plates | 6 |
primary case | 6 |
will recover | 6 |
global spread | 6 |
initial guesses | 6 |
identification framework | 6 |
model describes | 6 |
significantly higher | 6 |
three different | 6 |
million individuals | 6 |
much less | 6 |
three months | 6 |
adhesion molecules | 6 |
economic cost | 6 |
control theory | 6 |
evaluation information | 6 |
fully infected | 6 |
antimicrobial resistance | 6 |
species groups | 6 |
linear relationship | 6 |
medical doctor | 6 |
scanning electron | 6 |
clinical data | 6 |
leave behind | 6 |
time point | 6 |
biogeographical regions | 6 |
initial condition | 6 |
available intensive | 6 |
breast cancer | 6 |
significant premium | 6 |
reported fraction | 5 |
discussed earlier | 5 |
two quantities | 5 |
will allow | 5 |
logistic model | 5 |
dynamics approach | 5 |
people may | 5 |
present model | 5 |
method described | 5 |
darwinian fitness | 5 |
cumulative duration | 5 |
bone formation | 5 |
one group | 5 |
stage due | 5 |
sustainable transport | 5 |
criteria decision | 5 |
case data | 5 |
health expenditure | 5 |
antiviral medications | 5 |
times better | 5 |
infection process | 5 |
left panel | 5 |
station platforms | 5 |
protein biosynthesis | 5 |
first cases | 5 |
nonlinear autoregressive | 5 |
pfam database | 5 |
restrictive measures | 5 |
analyzed using | 5 |
infections among | 5 |
face proximity | 5 |
epidemic outbreaks | 5 |
new infection | 5 |
stage iii | 5 |
model provides | 5 |
multiple regions | 5 |
traditional method | 5 |
pyrrolizidine alkaloids | 5 |
five affected | 5 |
governments across | 5 |
general public | 5 |
gp tree | 5 |
takes place | 5 |
useful information | 5 |
different numbers | 5 |
grows exponentially | 5 |
different areas | 5 |
daily travels | 5 |
highly contagious | 5 |
viral shedding | 5 |
predicted covid | 5 |
bcg vaccination | 5 |
news media | 5 |
one type | 5 |
mode choice | 5 |
factors include | 5 |
waking day | 5 |
asymptomatic infection | 5 |
new coronavirus | 5 |
reviewed journals | 5 |
mitigation effort | 5 |
multiple layers | 5 |
given country | 5 |
data suggest | 5 |
null hypothesis | 5 |
medicine selection | 5 |
benefit analysis | 5 |
core pfams | 5 |
number represents | 5 |
particularly relevant | 5 |
authorized species | 5 |
put forward | 5 |
per person | 5 |
final total | 5 |
symptomatic infectious | 5 |
veterinary medicine | 5 |
area indicates | 5 |
identification algorithms | 5 |
road transportation | 5 |
world bank | 5 |
plate number | 5 |
world countries | 5 |
toxic effects | 5 |
future pandemics | 5 |
destination nations | 5 |
methods using | 5 |
table xi | 5 |
cases using | 5 |
scaling factor | 5 |
core protein | 5 |
air pollution | 5 |
strict quarantine | 5 |
abundance distribution | 5 |
corona virus | 5 |
several studies | 5 |
urgent cases | 5 |
actual total | 5 |
april st | 5 |
single linguistic | 5 |
logistic function | 5 |
ifr values | 5 |
certain time | 5 |
video count | 5 |
critical resource | 5 |
fitting parameters | 5 |
daily time | 5 |
reduction factor | 5 |
initial stage | 5 |
lower population | 5 |
two individuals | 5 |
maryland communities | 5 |
recorded deaths | 5 |
several factors | 5 |
mechanical ventilation | 5 |
ulcerative colitis | 5 |
two parameters | 5 |
compare different | 5 |
economic losses | 5 |
right censoring | 5 |
mean waiting | 5 |
daily cumulative | 5 |
collecting data | 5 |
time evolution | 5 |
microbiome project | 5 |
also provides | 5 |
based method | 5 |
middle stages | 5 |
city government | 5 |
around day | 5 |
cell walls | 5 |
varying environments | 5 |
long time | 5 |
corresponding rfid | 5 |
bottom left | 5 |
experimental groups | 5 |
ten days | 5 |
equal among | 5 |
domestic violence | 5 |
early data | 5 |
bgc group | 5 |
without symptoms | 5 |
necessarily coincide | 5 |
predictions made | 5 |
opinion mining | 5 |
reference database | 5 |
antimicrobial agents | 5 |
fatalities positives | 5 |
urban development | 5 |
top five | 5 |
easter holidays | 5 |
hpv dna | 5 |
data availability | 5 |
rate within | 5 |
exponential rate | 5 |
i denote | 5 |
copy number | 5 |
policy factor | 5 |
seihrd model | 5 |
toxic plants | 5 |
predicted data | 5 |
epithelial cells | 5 |
intensive growth | 5 |
standard sir | 5 |
one country | 5 |
adjusted case | 5 |
recorded infections | 5 |
superiore di | 5 |
higher population | 5 |
present work | 5 |
model consists | 5 |
private car | 5 |
global pandemic | 5 |
vectors per | 5 |
lamina propria | 5 |
i will | 5 |
observation period | 5 |
social behavior | 5 |
surgical procedures | 5 |
real statistics | 5 |
higher levels | 5 |
economically active | 5 |
average infectiousness | 5 |
person transmission | 5 |
million persons | 5 |
cumulative counts | 5 |
health commission | 5 |
afternoon peak | 5 |
geometric random | 5 |
epidemic infection | 5 |
distance measure | 5 |
data become | 5 |
starting point | 5 |
clinical course | 5 |
stochastic sir | 5 |
per se | 5 |
acid sequences | 5 |
type i | 5 |
statistical method | 5 |
lines indicate | 5 |
bus policy | 5 |
human health | 5 |
third week | 5 |
tested half | 5 |
tests conducted | 5 |
high throughput | 5 |
see supporting | 5 |
control methodology | 5 |
will consider | 5 |
valid data | 5 |
third level | 5 |
day effective | 5 |
results obtained | 5 |
unit i | 5 |
zika virus | 5 |
without loss | 5 |
another study | 5 |
payment area | 5 |
northern regions | 5 |
transmission coefficient | 5 |
environmental conditions | 5 |
oral application | 5 |
table show | 5 |
socioeconomic factors | 5 |
regions will | 5 |
scaled video | 5 |
measures like | 5 |
study shows | 5 |
treated animals | 5 |
best parameter | 5 |
using social | 5 |
significant predictors | 5 |
dynamic behavior | 5 |
parameters used | 5 |
fixed effects | 5 |
disease transmissibility | 5 |
especially important | 5 |
gets infected | 5 |
also shows | 5 |
nervous system | 5 |
general population | 5 |
min time | 5 |
within one | 5 |
modeling infectious | 5 |
independent variables | 5 |
squared error | 5 |
higher winning | 5 |
i cr | 5 |
estimated coefficient | 5 |
passenger flow | 5 |
renal failure | 5 |
similar results | 5 |
significant increase | 5 |
college london | 5 |
retirement homes | 5 |
per year | 5 |
cell carcinoma | 5 |
i obtain | 5 |
probability distributions | 5 |
random factor | 5 |
stricter measures | 5 |
slaughtered animals | 5 |
cases every | 5 |
classification method | 5 |
average size | 5 |
capital city | 5 |
regression models | 5 |
key parameters | 5 |
also considered | 5 |
node corresponds | 5 |
tells us | 5 |
transmission parameters | 5 |
average length | 5 |
model used | 5 |
right panel | 5 |
three types | 5 |
passenger count | 5 |
crowding analysis | 5 |
information appendix | 5 |
demographic processes | 5 |
di dt | 5 |
population dynamics | 5 |
numerical optimization | 5 |
supply chain | 5 |
sir epidemic | 5 |
macrolides used | 5 |
high degree | 5 |
classic sir | 5 |
disease caused | 5 |
daily practice | 5 |
million us | 5 |
acute ischemic | 5 |
mostly related | 5 |
news data | 5 |
interactions among | 5 |
parameter setting | 5 |
moving average | 5 |
current data | 5 |
also include | 5 |
make predictions | 5 |
model fit | 5 |
paper presents | 5 |
uniform model | 5 |
west africa | 5 |
dry weight | 5 |
transport processes | 5 |
among different | 5 |
course dose | 5 |
different times | 5 |
high enough | 5 |
partially infected | 5 |
developing symptoms | 5 |
transmission models | 5 |
myocardial infarction | 5 |
monoclonal antibody | 5 |
partial lockdown | 5 |
province region | 5 |
virus transmission | 5 |
controlling covid | 5 |
one rfid | 5 |
may differ | 5 |
top right | 5 |
epidemiological situation | 5 |
monoclonal antibodies | 5 |
viral infection | 5 |
vertical dashed | 5 |
will remain | 5 |
wait time | 5 |
random variables | 5 |
ribosomal protein | 5 |
two distinct | 5 |
two service | 5 |
someone infected | 5 |
using simulated | 5 |
table reports | 5 |
deterministic model | 5 |
high rate | 5 |
population infected | 5 |
early portion | 5 |
autoregressive exogenous | 5 |
early march | 5 |
available beds | 5 |
per stroke | 5 |
second term | 5 |
strong positive | 5 |
gexf format | 5 |
seir models | 5 |
global population | 5 |
temporal resolution | 5 |
regional health | 5 |
statistical data | 5 |
protection measures | 5 |
explicit list | 5 |
day i | 5 |
herds infected | 5 |
population balance | 5 |
epidemic control | 5 |
relatively high | 5 |
donald trump | 5 |
italy page | 5 |
worst case | 5 |
particularly important | 5 |
coronavirus covid | 5 |
people tend | 5 |
recurrent emergence | 5 |
mitigation strategies | 5 |
deaths per | 5 |
different species | 5 |
interval distribution | 5 |
main difference | 5 |
toxic properties | 5 |
place closure | 5 |
last two | 5 |
related deaths | 5 |
will provide | 5 |
binary splitting | 5 |
infection numbers | 5 |
collected data | 5 |
stochastic simulations | 5 |
generalized logistic | 5 |
light microscopy | 5 |
first weeks | 5 |
shopping phase | 5 |
highly variable | 5 |
global burden | 5 |
slightly lower | 5 |
small enough | 5 |
rate will | 5 |
one species | 5 |
per kg | 5 |
may vary | 5 |
testing strategies | 5 |
shaded area | 5 |
coronavirus infection | 5 |
reliable estimate | 5 |
paediatric orthopaedic | 5 |
contact numbers | 5 |
high school | 5 |
cashier area | 5 |
hospital staff | 5 |
effect model | 5 |
first index | 5 |
strong correlation | 5 |
million metric | 5 |
virus will | 5 |
active infection | 5 |
previous sections | 5 |
later stages | 5 |
producing plants | 5 |
cure time | 5 |
massive testing | 5 |
single variable | 5 |
using regression | 5 |
president donald | 5 |
active compounds | 5 |
throughput sequencing | 5 |
much greater | 5 |
expected value | 5 |
infectious periods | 5 |
active population | 5 |
bone resorption | 5 |
estimates obtained | 5 |
quarantine measures | 5 |
hard clustering | 5 |
discrete simulation | 5 |
paediatric hospital | 5 |
pf ribosomal | 5 |
optimal pool | 5 |
individual wearing | 5 |
week i | 5 |
different situations | 5 |
transition rates | 5 |
sentiment classification | 5 |
ergot alkaloids | 4 |
analysis will | 4 |
respiratory infections | 4 |
symptomatic sars | 4 |
direct contact | 4 |
data source | 4 |
ending isolation | 4 |
help us | 4 |
population count | 4 |
infectious persons | 4 |
positive effect | 4 |
possibly due | 4 |
table lists | 4 |
measures commenced | 4 |
discussed later | 4 |
potential therapies | 4 |
small intestine | 4 |
explanatory variable | 4 |
infections occur | 4 |
seven days | 4 |
italian region | 4 |
random samples | 4 |
small excursions | 4 |
making problems | 4 |
disease burden | 4 |
many places | 4 |
infections i | 4 |
higher plants | 4 |
analysis section | 4 |
first hospital | 4 |
coronavirus pandemic | 4 |
various countries | 4 |
managing epidemics | 4 |
patients requiring | 4 |
will reach | 4 |
around half | 4 |
previous epidemics | 4 |
stimulated dna | 4 |
check whether | 4 |
also seen | 4 |
error estimates | 4 |
daily reported | 4 |
epidemic transmission | 4 |
true infected | 4 |
motivates researchers | 4 |
confidence intervals | 4 |
time day | 4 |
constant rate | 4 |
significant effects | 4 |
poisson distributed | 4 |
nucleic acid | 4 |
taxa composition | 4 |
aircraft maintenance | 4 |
different social | 4 |
ask whether | 4 |
alkaloid levels | 4 |
memory property | 4 |
italian case | 4 |
ribosomal rna | 4 |
progressively harmonized | 4 |
first phase | 4 |
high levels | 4 |
distributed errors | 4 |
control variable | 4 |
also note | 4 |
likelihood based | 4 |
github repository | 4 |
effective transmission | 4 |
currently available | 4 |
us consider | 4 |
influenza antiviral | 4 |
field hospitals | 4 |
autocorrelated errors | 4 |
results also | 4 |
effective measures | 4 |
missing data | 4 |
estimated proportion | 4 |
protezione civile | 4 |
care resources | 4 |
passenger counting | 4 |
provide information | 4 |
policies aimed | 4 |
fuzzy sets | 4 |
cases produced | 4 |
best case | 4 |
peak incidence | 4 |
stochastic differential | 4 |
output layer | 4 |
data presented | 4 |
square models | 4 |
biomass repartition | 4 |
marginal priors | 4 |
queue sizes | 4 |
stage ii | 4 |
strong enough | 4 |
experimental data | 4 |
large fluctuations | 4 |
tunisian data | 4 |
data reported | 4 |
values used | 4 |
large proportion | 4 |
detailed information | 4 |
reaching different | 4 |
bounding box | 4 |
root mean | 4 |
disease may | 4 |
total biomass | 4 |
community structure | 4 |
till april | 4 |
one may | 4 |
i denotes | 4 |
veterinary antimicrobials | 4 |
contact persons | 4 |
analysis based | 4 |
istituto superiore | 4 |
average per | 4 |
top panels | 4 |
one approach | 4 |
departure times | 4 |
tourism policy | 4 |
home will | 4 |
ischemic stroke | 4 |
rate goes | 4 |
proposed methodological | 4 |
allocation model | 4 |
comparisons showed | 4 |
robust synthetic | 4 |
i consider | 4 |
choquet integral | 4 |
visiting nation | 4 |
i use | 4 |
confinement policy | 4 |
care bed | 4 |
infections begins | 4 |
default values | 4 |
influenza epidemic | 4 |
precisely wrong | 4 |
major nations | 4 |
proposed work | 4 |
injectable products | 4 |
vulnerable population | 4 |
automatically collected | 4 |
significant part | 4 |
identified cases | 4 |
food source | 4 |
successfully applied | 4 |
tourism revenues | 4 |
among travellers | 4 |
just one | 4 |
gaussian distribution | 4 |
cases coming | 4 |
products authorized | 4 |
like covid | 4 |
will require | 4 |
early testing | 4 |
bitter lupines | 4 |
population growth | 4 |
information available | 4 |
three weeks | 4 |
model assumes | 4 |
mechanical ventilators | 4 |
data suggests | 4 |
auxiliary variable | 4 |
total french | 4 |
utility value | 4 |
first part | 4 |
least squares | 4 |
different ways | 4 |
using afinn | 4 |
predicting covid | 4 |
world energy | 4 |
significant difference | 4 |
reveal similar | 4 |
new infected | 4 |
will country | 4 |
cases increased | 4 |
sweet lupines | 4 |
extracted ifr | 4 |
rate estimation | 4 |
video feed | 4 |
will denote | 4 |
extended gravity | 4 |
picture changes | 4 |
remains constant | 4 |
plate numbers | 4 |
industrial center | 4 |
methodological framework | 4 |
international trade | 4 |
iterative map | 4 |
adverse effects | 4 |
total contact | 4 |
uniform arrivals | 4 |
will probably | 4 |
selected based | 4 |
unsupervised approach | 4 |
epidemic two | 4 |
death costs | 4 |
attract tourists | 4 |
economic activity | 4 |
one outbreak | 4 |
must bear | 4 |
single test | 4 |
covid mortality | 4 |
lessons learned | 4 |
already formed | 4 |
well beyond | 4 |
carriage rate | 4 |
move around | 4 |
obtain estimates | 4 |
areas focusing | 4 |
bottom right | 4 |
response speed | 4 |
health organisation | 4 |
term set | 4 |
false positives | 4 |
hospital overcrowding | 4 |
four seasons | 4 |
patient outcomes | 4 |
income countries | 4 |
household models | 4 |
vehicle ownership | 4 |
passengers coming | 4 |
contacts involving | 4 |
median incubation | 4 |
daily increase | 4 |
model may | 4 |
electric vehicles | 4 |
full simulation | 4 |
taking measures | 4 |
international tourism | 4 |
large cell | 4 |
initial intensity | 4 |
driven analysis | 4 |
hospital care | 4 |
flow chart | 4 |
university hospital | 4 |
simulated people | 4 |
results reveal | 4 |
beds needed | 4 |
doubling time | 4 |
low level | 4 |
social circles | 4 |
many herbivores | 4 |
mobility rates | 4 |
random mixing | 4 |
marketing authorization | 4 |
first half | 4 |
car use | 4 |
product level | 4 |
stay home | 4 |
exogenous input | 4 |
based methods | 4 |
inpatient number | 4 |
relevant parameters | 4 |
auction price | 4 |
will result | 4 |
complex system | 4 |
several scenarios | 4 |
model proposed | 4 |
per time | 4 |
bowel disease | 4 |
public opinion | 4 |
cases showed | 4 |
early detection | 4 |
one percent | 4 |
one doi | 4 |
also suggest | 4 |
linear mixed | 4 |
age slice | 4 |
established statistical | 4 |
one needs | 4 |
citizens population | 4 |
closed population | 4 |
european union | 4 |
detailed differences | 4 |
day outside | 4 |
city identity | 4 |
natural history | 4 |
step model | 4 |
letter prefix | 4 |
community composition | 4 |
hiv aids | 4 |
long history | 4 |
even within | 4 |
since march | 4 |
disease surveillance | 4 |
wound strength | 4 |
positive pools | 4 |
trains leaving | 4 |
ill patients | 4 |
anova showed | 4 |
potential infection | 4 |
health resources | 4 |
functionless molecules | 4 |
many patients | 4 |
gave us | 4 |
prior distributions | 4 |
size distribution | 4 |
changes substantially | 4 |
similar pattern | 4 |
three scenarios | 4 |
rate may | 4 |
quite different | 4 |
consistent results | 4 |
first three | 4 |
initial values | 4 |
health workers | 4 |
people hospitalized | 4 |
paediatric orthopaedics | 4 |
following form | 4 |
medical resource | 4 |
based mitigation | 4 |
total fatality | 4 |
different transmission | 4 |
special case | 4 |
cumulative infections | 4 |
age structure | 4 |
well documented | 4 |
life expectancy | 4 |
natural population | 4 |
human behavior | 4 |
right truncation | 4 |
limited data | 4 |
even worse | 4 |
government measures | 4 |
value component | 4 |
visit taiwan | 4 |
health services | 4 |
surveillance data | 4 |
per sample | 4 |
infection spreads | 4 |
single infected | 4 |
intervention strategies | 4 |
latency period | 4 |
public transit | 4 |
preprint servers | 4 |
relatively large | 4 |
epidemic using | 4 |
step optimization | 4 |
evidence suggests | 4 |
similar impacts | 4 |
pathogen barely | 4 |
analysis shows | 4 |
nothing scenario | 4 |
geographic locations | 4 |
method presented | 4 |
empirical findings | 4 |
reference signal | 4 |
two levels | 4 |
hamiltonian monte | 4 |
measures influence | 4 |
tax policy | 4 |
people might | 4 |
experienced waiting | 4 |
cases divided | 4 |
package version | 4 |
different scenarios | 4 |
also thank | 4 |
lancet global | 4 |
sick people | 4 |
infection per | 4 |
gdp growth | 4 |
accurate results | 4 |
active ingredients | 4 |
movement restrictions | 4 |
results must | 4 |
given moment | 4 |
biological activities | 4 |
positively identified | 4 |
data described | 4 |
jupyter notebook | 4 |
case studies | 4 |
three levels | 4 |
include population | 4 |
analysis using | 4 |
customers waiting | 4 |
data corpus | 4 |
imposed social | 4 |
fewer groups | 4 |
antimicrobial properties | 4 |
level paediatric | 4 |
general observation | 4 |
taiwan tourism | 4 |
trick algorithm | 4 |
period days | 4 |
detection capacity | 4 |
asian countries | 4 |
fattening pigs | 4 |
third world | 4 |
become available | 4 |
royal society | 4 |
accession number | 4 |
nothing value | 4 |
health emergency | 4 |
biomass distribution | 4 |
veal calves | 4 |
patients receiving | 4 |
computational models | 4 |
potential enemies | 4 |
sequencing reads | 4 |
flow variable | 4 |
calendar time | 4 |
video conferences | 4 |
initial confinement | 4 |
resource occupation | 4 |
place order | 4 |
general health | 4 |
second largest | 4 |
station platform | 4 |
reserve prices | 4 |
open source | 4 |
label switching | 4 |
plos one | 4 |
new data | 4 |
energy conservation | 4 |
lancet infectious | 4 |
emergency departments | 4 |
higher accuracy | 4 |
red curve | 4 |
resource planning | 4 |
vaguely right | 4 |
will depend | 4 |
statistics gathered | 4 |
marker gene | 4 |
evaluation grade | 4 |
breast carcinoma | 4 |
period march | 4 |
statistical properties | 4 |
much easier | 4 |
seiird model | 4 |
shape parameters | 4 |
york city | 4 |
people affected | 4 |
supplementary material | 4 |
case counts | 4 |
information theory | 4 |
potential spread | 4 |
will grow | 4 |
us cities | 4 |
affected patients | 4 |
classification scheme | 4 |
panel data | 4 |
will help | 4 |
tourism bureau | 4 |
avoid contact | 4 |
previous years | 4 |
maximal number | 4 |
reduce social | 4 |
based models | 4 |
daily incidences | 4 |
rhyme similarly | 4 |
actual infection | 4 |
outcome variable | 4 |
preliminary estimation | 4 |
large initial | 4 |
wavy reduction | 4 |
coronavirus pneumonia | 4 |
marginal effects | 4 |
squares regression | 4 |
tunisian population | 4 |
infectious individual | 4 |
person per | 4 |
already detected | 4 |
medicinal products | 4 |
right time | 4 |
epidemiological data | 4 |
maintenance routing | 4 |
training dataset | 4 |
percentage drop | 4 |
free cashier | 4 |
research work | 4 |
mouth epidemic | 4 |
susceptible herds | 4 |
probability mass | 4 |
product characteristics | 4 |
tourists per | 4 |
one person | 4 |
per people | 4 |
defense chemistry | 4 |
time lag | 4 |
based surveillance | 4 |
literature review | 4 |
different states | 4 |
spatial heterogeneity | 4 |
first infection | 4 |
detailed analysis | 4 |
models using | 4 |
population numbers | 4 |
pandemic using | 4 |
open access | 4 |
random number | 4 |
peak period | 4 |
study duration | 4 |
current covid | 4 |
longer incubation | 4 |
covid study | 4 |
death rates | 4 |
mg kg | 4 |
hastings mcmc | 4 |
shotgun methods | 4 |
crowded trains | 4 |
indicators among | 4 |
reports released | 4 |
objective function | 4 |
mutually exclusive | 4 |
ttr data | 4 |
reserve price | 4 |
service phases | 4 |
small compared | 4 |
hedonic estimations | 4 |
sufficient number | 4 |
corresponding sentiment | 4 |
sufficient condition | 4 |
recovered immune | 4 |
universal social | 4 |
various reasons | 4 |
northern italy | 4 |
parameter file | 4 |
one million | 4 |
host population | 4 |
wide variety | 4 |
number infected | 4 |
daily disease | 4 |
twenty reads | 4 |
opening remarks | 4 |
hesitancy degree | 4 |
table gives | 4 |
online global | 4 |
clinical parameters | 4 |
mutation rate | 4 |
makes use | 4 |
produce alkaloids | 4 |
rather low | 4 |
available case | 4 |
woo kwok | 4 |
auction data | 4 |
active ingredient | 4 |
correlation test | 4 |
tourism industry | 4 |
next days | 4 |
care systems | 4 |
using contact | 4 |
available critical | 4 |
different cities | 4 |
mainland china | 4 |
infection transmission | 4 |
pathogen evolution | 4 |
main results | 4 |
practical means | 4 |
source software | 4 |
new positive | 4 |
people will | 4 |
medical attention | 4 |
pandemic date | 4 |
real estimates | 4 |
period february | 4 |
set equal | 4 |
baseline transmission | 4 |
unsupervised sentiment | 4 |
certain moment | 4 |
daily fluctuations | 4 |
two independent | 4 |
accurately predict | 4 |
containing plants | 4 |
rail station | 4 |
small fraction | 4 |
mortality following | 4 |
term care | 4 |
store management | 4 |
bird survey | 4 |
co mitigation | 4 |
parenthesis indicate | 4 |
low confidence | 4 |
prospective predictions | 4 |
auction date | 4 |
future epidemics | 4 |
significance level | 4 |
table presents | 4 |
novel cases | 4 |
vary widely | 4 |
membership function | 4 |
carbon dioxide | 4 |
novel influenza | 4 |
zero mean | 4 |
model makes | 4 |
generation sequencing | 4 |
plants often | 4 |
time points | 4 |
significantly decreased | 4 |
estimated cumulative | 4 |
many different | 4 |
next influenza | 4 |
trauma cases | 4 |
daily rate | 4 |
based approach | 4 |
i show | 4 |
containment policy | 4 |
simulation susceptible | 4 |
low grade | 4 |
second part | 4 |
pacific region | 4 |
response team | 4 |
lda model | 4 |
local outbreak | 4 |
among children | 4 |
contacts varied | 4 |
number srp | 4 |
robust standard | 4 |
therapeutic intensity | 4 |
dioxide emission | 4 |
certain amount | 4 |
samples per | 4 |
state variables | 4 |
bottom panel | 4 |
last ten | 4 |
disease will | 4 |
transmission potential | 4 |
three mock | 4 |
organization unwto | 4 |
loop control | 4 |
door closing | 4 |
negative tests | 4 |
genome shotgun | 4 |
across plates | 4 |
personalized plate | 4 |
study using | 4 |
table summarizes | 4 |
data mining | 4 |
vehicles driven | 4 |
standardized epidemiological | 4 |
various transportation | 4 |
individual infection | 4 |
stochastic simulation | 4 |
repeated observations | 4 |
target distribution | 4 |
board trains | 4 |
individuals will | 4 |
epidemic starts | 4 |
three cases | 4 |
asymptomatic state | 4 |
epidemic outbreak | 4 |
without place | 4 |
comparative study | 4 |
total knee | 4 |
empirical analysis | 4 |
american breeding | 4 |
google covid | 4 |
control measure | 4 |
cell adhesion | 4 |
lymph node | 4 |
positive taxa | 4 |
many groups | 4 |
large one | 4 |
million inhabitants | 4 |
tables iv | 4 |
second generation | 4 |
percent infected | 4 |
different algorithms | 4 |
appropriate model | 4 |
number preferences | 4 |
best guess | 4 |
sampling individuals | 4 |
online platform | 4 |
following covid | 4 |
equivalent scenario | 4 |
public policies | 4 |
disease study | 4 |
cumulative count | 4 |
mean rate | 4 |
infectious person | 4 |
sampling unit | 4 |
deceased positives | 4 |
integer optimization | 4 |
nursing homes | 4 |
disease progression | 4 |
infectious lines | 4 |
isolation ends | 4 |
different classes | 4 |
common sense | 4 |
breast cancers | 4 |
learning approach | 4 |
vary across | 4 |
bounding boxes | 4 |
common value | 4 |
selective isolation | 4 |
small risk | 4 |
probably due | 4 |
using equations | 4 |
mental health | 4 |
younger people | 4 |
overall mortality | 4 |
main reason | 4 |
personal protective | 4 |
cases among | 4 |
species level | 4 |
effective contact | 4 |
protein domains | 4 |
histological features | 4 |
size will | 4 |
ratio fatalities | 4 |
multiple criteria | 4 |
secure online | 4 |
also reduce | 4 |
issues related | 4 |
infectious case | 4 |
one patient | 4 |
reported new | 4 |
thrombectomy per | 4 |
probabilistic linguistic | 4 |
expected duration | 4 |
thereby providing | 4 |
death counts | 4 |
accurate estimate | 4 |
taxa abundance | 4 |
infected state | 4 |