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 |
---|---|
social media | 187 |
public health | 180 |
infectious diseases | 175 |
infected individuals | 163 |
treatment locations | 139 |
preventive behavior | 114 |
social networks | 112 |
social distancing | 104 |
candidate treatment | 94 |
contact tracing | 87 |
treatment location | 86 |
infectious disease | 85 |
human rights | 85 |
susceptible individuals | 85 |
grocery store | 80 |
antibiotic resistance | 80 |
social network | 78 |
natural kind | 73 |
treatment effects | 68 |
epidemic prevention | 63 |
control strategies | 62 |
entrepreneurial entry | 62 |
outer ring | 60 |
realized treatment | 57 |
infected individual | 57 |
treatment effect | 55 |
contact network | 53 |
grocery stores | 52 |
potential outcomes | 52 |
incubation period | 52 |
trust propensity | 52 |
media use | 51 |
disease dynamics | 51 |
candidate locations | 45 |
contact patterns | 44 |
average treatment | 44 |
wage workers | 43 |
epidemic spreading | 43 |
natural kinds | 43 |
time step | 42 |
spatial treatments | 41 |
disease transmission | 41 |
offline social | 41 |
health care | 41 |
complex networks | 41 |
doc id | 40 |
respiratory syndrome | 40 |
epidemic models | 40 |
cord uid | 40 |
individuals may | 39 |
severe acute | 39 |
spillover transmission | 39 |
total number | 39 |
machine learning | 39 |
acute respiratory | 39 |
social norms | 39 |
novel coronavirus | 38 |
contact networks | 38 |
disease spreading | 37 |
may also | 37 |
united states | 37 |
sir model | 35 |
incidental host | 34 |
branching process | 34 |
mental health | 33 |
symptomatic individuals | 32 |
large number | 32 |
mobile phone | 31 |
propensity score | 31 |
personal data | 30 |
data protection | 30 |
treatment assignment | 30 |
spatial treatment | 29 |
average number | 29 |
mean number | 29 |
global health | 28 |
transmission rate | 28 |
microbial forensics | 28 |
household isolation | 28 |
diffusion process | 28 |
attack rate | 28 |
direct transmission | 28 |
human behavior | 28 |
world health | 28 |
pandemic influenza | 28 |
home isolation | 28 |
fixed effects | 27 |
social capital | 27 |
hong kong | 27 |
study area | 26 |
network structure | 26 |
disease control | 26 |
free networks | 25 |
one health | 25 |
one another | 25 |
basic reproductive | 25 |
epidemic threshold | 25 |
health status | 25 |
average effect | 25 |
population density | 25 |
reproduction number | 25 |
vl cases | 25 |
risk perception | 24 |
network size | 24 |
vaccinated individuals | 24 |
health organization | 24 |
diffusion processes | 24 |
disease outbreaks | 24 |
individuals near | 24 |
critical care | 23 |
employed individuals | 23 |
vaccination behavior | 23 |
causal effects | 23 |
failure rate | 23 |
adopt epidemic | 23 |
time period | 23 |
vaccine failure | 23 |
ideal experiment | 23 |
store locations | 23 |
game theory | 22 |
control measures | 22 |
large outbreak | 22 |
epidemiological threshold | 22 |
antimicrobial resistance | 22 |
care homes | 22 |
many individuals | 22 |
living natural | 22 |
one individual | 22 |
asymptomatic infection | 22 |
control group | 22 |
online social | 21 |
disease spread | 21 |
i propose | 21 |
sis model | 21 |
mass media | 20 |
network dynamics | 20 |
contact interactions | 20 |
standard errors | 20 |
among individuals | 20 |
care home | 20 |
risk factors | 20 |
nash equilibrium | 20 |
parallel trends | 20 |
human health | 20 |
transmission probability | 20 |
individual contact | 20 |
inner ring | 20 |
marking posts | 20 |
dynamic contact | 20 |
individuals within | 19 |
infectious individuals | 19 |
emerging infectious | 19 |
group contact | 19 |
average degree | 19 |
coronavirus disease | 19 |
weak ties | 19 |
instrumental variables | 19 |
reporting rate | 19 |
herd immunity | 19 |
conceptual framework | 19 |
causal inference | 18 |
vaccination coverage | 18 |
human natural | 18 |
human social | 18 |
mathematical models | 18 |
grid cell | 18 |
epidemiological dynamics | 18 |
see appendix | 18 |
adopt preventive | 18 |
social security | 18 |
access control | 18 |
data collection | 18 |
population size | 18 |
second term | 18 |
location data | 18 |
small number | 17 |
epidemic outbreaks | 17 |
household quarantine | 17 |
medical ethics | 17 |
recovery rate | 17 |
resistance genes | 17 |
host population | 17 |
markov chain | 17 |
control potential | 17 |
symptom onset | 17 |
become infected | 17 |
completely randomized | 17 |
male cheetahs | 17 |
group size | 17 |
individuals will | 17 |
recovered individuals | 17 |
transmission dynamics | 17 |
family members | 17 |
personalized medicine | 17 |
table i | 16 |
age group | 16 |
growth rate | 16 |
reproductive number | 16 |
potential outcome | 16 |
natural ecosystems | 16 |
behavior dynamics | 16 |
close proximity | 16 |
contact tracking | 16 |
across regions | 16 |
individual self | 16 |
exposed individuals | 16 |
environmental health | 16 |
reproductive ratio | 16 |
individual level | 16 |
human beings | 16 |
moral hazard | 16 |
antiviral drugs | 16 |
infection risk | 16 |
basic reproduction | 16 |
individual preventive | 16 |
made available | 16 |
internal biological | 16 |
differential equations | 16 |
new infections | 16 |
epidemic model | 16 |
less likely | 16 |
seasonal influenza | 15 |
recent years | 15 |
disease outbreak | 15 |
human mobility | 15 |
susceptible population | 15 |
temporal networks | 15 |
adaptive networks | 15 |
information flow | 15 |
infected cases | 15 |
proposed model | 15 |
intervention strategies | 15 |
case study | 15 |
mixed population | 15 |
natural history | 15 |
finite sample | 15 |
degree distribution | 15 |
relative cost | 15 |
functional form | 15 |
time scale | 15 |
human contact | 15 |
excreting cvlp | 15 |
time periods | 15 |
individual fixed | 15 |
privacy protection | 15 |
biological relations | 15 |
social interaction | 14 |
seir model | 14 |
virtual identity | 14 |
may help | 14 |
voluntary vaccination | 14 |
close contact | 14 |
disease progression | 14 |
cellular automata | 14 |
virtual identities | 14 |
global public | 14 |
panel data | 14 |
higher social | 14 |
neural networks | 14 |
possible encounters | 14 |
social contact | 14 |
human society | 14 |
susceptible individual | 14 |
vaccine efficacy | 14 |
infectious period | 14 |
will also | 14 |
computer science | 14 |
informed consent | 14 |
asymptomatic individuals | 14 |
visceral leishmaniasis | 14 |
i will | 14 |
individual i | 14 |
individual isolation | 14 |
candidate location | 14 |
influence factors | 13 |
important role | 13 |
clinical characteristics | 13 |
influenza pandemic | 13 |
individual variation | 13 |
contact information | 13 |
climate change | 13 |
allows us | 13 |
counterfactual grocery | 13 |
human preventive | 13 |
network associates | 13 |
two different | 13 |
age groups | 13 |
health services | 13 |
generative adversarial | 13 |
hiv aids | 13 |
world networks | 13 |
sample size | 13 |
least one | 13 |
wage work | 13 |
household transmission | 13 |
psychological distress | 13 |
personal influence | 13 |
long time | 13 |
individual may | 13 |
convolutional neural | 13 |
infection network | 13 |
network structures | 13 |
sexual contact | 12 |
health policy | 12 |
tracing process | 12 |
moral norms | 12 |
infection pressure | 12 |
sexual contacts | 12 |
health outcomes | 12 |
even though | 12 |
entrepreneurial process | 12 |
anonymous credentials | 12 |
zoonotic pathogens | 12 |
health insurance | 12 |
overseas travellers | 12 |
interactions among | 12 |
monte carlo | 12 |
control policies | 12 |
may lead | 12 |
job control | 12 |
influenza epidemic | 12 |
national self | 12 |
spatial structure | 12 |
future research | 12 |
employment indicator | 12 |
first term | 12 |
crucial role | 12 |
homogeneous mixing | 12 |
two diffusion | 12 |
real grocery | 12 |
fixed number | 12 |
shifting individuals | 12 |
social distance | 12 |
social contacts | 12 |
behavioral responses | 12 |
multiplex networks | 12 |
become susceptible | 11 |
solidarity rights | 11 |
individual behavior | 11 |
infection status | 11 |
heavy metals | 11 |
ring businesses | 11 |
level treatment | 11 |
mutual marking | 11 |
census data | 11 |
missing data | 11 |
contact rates | 11 |
wireless sensors | 11 |
typical history | 11 |
health ethics | 11 |
adversarial networks | 11 |
noncooperative game | 11 |
influenza epidemics | 11 |
two types | 11 |
coupled disease | 11 |
protective behavior | 11 |
data processing | 11 |
every individual | 11 |
group model | 11 |
medical care | 11 |
long distance | 11 |
author funder | 11 |
one may | 11 |
one must | 11 |
total population | 11 |
level treatments | 11 |
player i | 11 |
diffusion model | 11 |
individuals become | 11 |
take place | 11 |
early stages | 11 |
observational data | 11 |
influenza cases | 11 |
species transmission | 11 |
surveillance data | 11 |
treatment times | 11 |
three diffusion | 11 |
mixed isolation | 11 |
unrealized candidate | 11 |
mobile network | 11 |
population level | 11 |
direct contact | 11 |
estimators proposed | 11 |
granted medrxiv | 11 |
sf networks | 11 |
mixed populations | 11 |
mixing patterns | 11 |
least squares | 11 |
possible encounter | 11 |
lockdown policy | 11 |
epidemic control | 11 |
critically infected | 11 |
i recommend | 11 |
epidemic spread | 11 |
disease models | 11 |
healthy individuals | 11 |
international license | 11 |
treated regions | 11 |
individual birth | 11 |
mathematical model | 11 |
like principles | 11 |
characteristic development | 11 |
infection spread | 11 |
go extinct | 11 |
update rule | 11 |
closely related | 11 |
barely contagious | 10 |
different types | 10 |
time scales | 10 |
model based | 10 |
individual autonomy | 10 |
hospital admissions | 10 |
public key | 10 |
high trust | 10 |
marking post | 10 |
sexually transmitted | 10 |
covid crisis | 10 |
new york | 10 |
two individuals | 10 |
sir epidemic | 10 |
control regions | 10 |
death rate | 10 |
vaccination strategy | 10 |
social support | 10 |
epidemic dynamics | 10 |
individual will | 10 |
population structure | 10 |
per region | 10 |
antibiotic use | 10 |
purpose limitation | 10 |
rna viruses | 10 |
real time | 10 |
data collected | 10 |
within region | 10 |
communication network | 10 |
network analysis | 10 |
highly contagious | 10 |
neurocognitive disorders | 10 |
negative impact | 10 |
influenza vaccination | 10 |
previous studies | 10 |
nearest realized | 10 |
myopic update | 10 |
large scale | 10 |
also known | 10 |
epidemic modeling | 10 |
human genome | 10 |
control individuals | 10 |
final size | 10 |
trends assumption | 10 |
peer pressure | 10 |
hyperbolic sine | 10 |
differential equation | 10 |
data analysis | 10 |
overall attack | 10 |
di erent | 10 |
data mining | 10 |
mobility behaviors | 10 |
absorbing state | 10 |
systematic review | 10 |
nearby restaurants | 10 |
birth rate | 10 |
instrumental variable | 10 |
recent work | 10 |
wastewater treatment | 10 |
inverse hyperbolic | 10 |
job demand | 10 |
air pollutants | 10 |
real treatment | 10 |
health risks | 9 |
copyright holder | 9 |
human population | 9 |
large fraction | 9 |
related stress | 9 |
communication networks | 9 |
control variable | 9 |
pseudomonas aeruginosa | 9 |
previous section | 9 |
i i | 9 |
account workers | 9 |
behavioral response | 9 |
power law | 9 |
physical contact | 9 |
background epidemic | 9 |
becomes infected | 9 |
natural language | 9 |
global positioning | 9 |
million dollar | 9 |
treatment setting | 9 |
one hand | 9 |
infection rate | 9 |
many people | 9 |
latent period | 9 |
wide range | 9 |
data augmentation | 9 |
tracing methods | 9 |
health impact | 9 |
nan doi | 9 |
determine whether | 9 |
allow us | 9 |
lockdown period | 9 |
epidemic disease | 9 |
higher risk | 9 |
statistical physics | 9 |
time spent | 9 |
medical science | 9 |
combined effectiveness | 9 |
temporal network | 9 |
individual death | 9 |
privacy concerns | 9 |
human transmission | 9 |
mobility patterns | 9 |
fundamental rights | 9 |
numerical simulations | 9 |
infected neighbors | 9 |
network model | 9 |
parameter values | 9 |
multilayer networks | 9 |
relatively small | 9 |
doubling time | 9 |
infection time | 9 |
disease prevalence | 9 |
average time | 9 |
epidemic knowledge | 9 |
weighted average | 9 |
different individuals | 9 |
maasai mara | 9 |
travel restriction | 9 |
social interactions | 9 |
i also | 9 |
empirical work | 9 |
treatment time | 9 |
physical activity | 9 |
index case | 9 |
contact behaviors | 9 |
return date | 9 |
different levels | 9 |
peak time | 9 |
employed workers | 9 |
community structure | 9 |
critical value | 9 |
among others | 9 |
strong ties | 9 |
antibiotic resistome | 9 |
preventive behaviors | 9 |
model assumes | 9 |
adrd may | 9 |
infected population | 9 |
see section | 9 |
simulation results | 9 |
randomized experiment | 9 |
th excursion | 8 |
epidemiological models | 8 |
next section | 8 |
network theory | 8 |
health measures | 8 |
air pollution | 8 |
two approaches | 8 |
higher job | 8 |
mile away | 8 |
indian subcontinent | 8 |
survey data | 8 |
control variables | 8 |
information diffusion | 8 |
randomly selected | 8 |
vaccination cost | 8 |
behavioral changes | 8 |
epidemic processes | 8 |
horizontal transfer | 8 |
previous works | 8 |
infection events | 8 |
distance bins | 8 |
expected number | 8 |
human populations | 8 |
neural network | 8 |
behavioral framework | 8 |
bat model | 8 |
will need | 8 |
ebola virus | 8 |
information regarding | 8 |
high level | 8 |
control strategy | 8 |
may need | 8 |
public good | 8 |
household size | 8 |
vaccine cost | 8 |
randomized design | 8 |
indigenous peoples | 8 |
case studies | 8 |
vaccination decision | 8 |
population dynamics | 8 |
individual health | 8 |
exponential growth | 8 |
social groups | 8 |
individuals also | 8 |
influenza virus | 8 |
environmental factors | 8 |
human behaviour | 8 |
based influence | 8 |
general population | 8 |
information transmission | 8 |
infectious individual | 8 |
compartmental model | 8 |
short distance | 8 |
health behavior | 8 |
syndrome coronavirus | 8 |
face masks | 8 |
pharmaceutical interventions | 8 |
many countries | 8 |
treated individuals | 8 |
degree distributions | 8 |
symptomatic cases | 8 |
contact structure | 8 |
policy makers | 8 |
superspreading events | 8 |
social brain | 8 |
first step | 8 |
treatment settings | 8 |
household members | 8 |
genetic elements | 8 |
deep learning | 8 |
aggregate effects | 8 |
empirical data | 8 |
composite group | 8 |
clustering coefficient | 8 |
average individual | 8 |
care workers | 8 |
vice versa | 8 |
disease propagation | 8 |
differentially private | 8 |
posterior distribution | 8 |
control theory | 8 |
euclidean distance | 8 |
help individuals | 8 |
immunization strategies | 8 |
vaccination policy | 8 |
genetic information | 8 |
baseline scenario | 8 |
new cases | 8 |
economic development | 8 |
positive probability | 8 |
different distances | 8 |
model parameters | 8 |
wearable wireless | 8 |
high virulence | 8 |
see table | 8 |
gps devices | 8 |
launch stage | 8 |
mathematical theory | 8 |
every year | 8 |
computer simulations | 8 |
identity authority | 8 |
risk assessment | 8 |
varying networks | 8 |
ar spread | 8 |
infection spreading | 8 |
health interventions | 8 |
decision making | 8 |
avian influenza | 8 |
common heritage | 8 |
private actors | 8 |
drug resistance | 8 |
contact behavior | 8 |
google flu | 8 |
ethical framework | 8 |
outcome units | 8 |
human disease | 8 |
differences approach | 8 |
newly infected | 8 |
community size | 8 |
developing communities | 8 |
se se | 8 |
i show | 8 |
i discuss | 8 |
single individual | 8 |
based approach | 8 |
propensity scores | 7 |
one might | 7 |
observational studies | 7 |
empirical studies | 7 |
us consider | 7 |
group sizes | 7 |
inverse probability | 7 |
constant across | 7 |
also consider | 7 |
differential privacy | 7 |
positioning system | 7 |
previous sections | 7 |
big data | 7 |
path coefficients | 7 |
i th | 7 |
immune response | 7 |
layered structure | 7 |
inner vs | 7 |
intellectually handicapped | 7 |
different ecosystems | 7 |
relative effectiveness | 7 |
spatial resolution | 7 |
average outcome | 7 |
public goods | 7 |
based inference | 7 |
nan sha | 7 |
political rights | 7 |
treated potential | 7 |
injury sense | 7 |
without considering | 7 |
law enforcement | 7 |
sickness benefits | 7 |
artificial intelligence | 7 |
secondary infections | 7 |
real world | 7 |
individual contacts | 7 |
mutation rate | 7 |
will take | 7 |
random networks | 7 |
selective pressure | 7 |
towards epidemic | 7 |
realized location | 7 |
realistic urban | 7 |
contacts among | 7 |
developing countries | 7 |
transition probability | 7 |
infection rates | 7 |
infection event | 7 |
remote party | 7 |
connection domains | 7 |
health system | 7 |
branching processes | 7 |
transmission potential | 7 |
microbial communities | 7 |
south africa | 7 |
asymptomatic cases | 7 |
using gps | 7 |
social self | 7 |
lower bound | 7 |
may affect | 7 |
low trust | 7 |
geographical proximity | 7 |
media platforms | 7 |
two models | 7 |
human behavioral | 7 |
riding behavior | 7 |
epidemic diseases | 7 |
disease agents | 7 |
network topology | 7 |
language processing | 7 |
give rise | 7 |
distancing regulations | 7 |
will change | 7 |
personal information | 7 |
without loss | 7 |
must also | 7 |
infectious agent | 7 |
will likely | 7 |
social change | 7 |
adaptive network | 7 |
biological factors | 7 |
larger social | 7 |
european countries | 7 |
disease will | 7 |
long term | 7 |
transmission models | 7 |
epidemiological model | 7 |
proposed methods | 7 |
human infectious | 7 |
vertical lines | 7 |
across different | 7 |
different time | 7 |
many different | 7 |
theoretical models | 7 |
main text | 7 |
heterogeneous contact | 7 |
enclave memory | 7 |
older people | 7 |
zika virus | 7 |
whole population | 7 |
azar dermal | 7 |
mobile phones | 7 |
disease emergence | 7 |
structural model | 7 |
contact matrix | 7 |
treatment plants | 7 |
stimulus control | 7 |
two terms | 7 |
escherichia coli | 7 |
infection intensity | 7 |
based modeling | 7 |
south korea | 7 |
taking precautions | 7 |
labor market | 7 |
distance bin | 7 |
becoming infected | 7 |
spatially structured | 7 |
dominant strategy | 7 |
distance function | 7 |
high cost | 7 |
much faster | 7 |
transmission process | 7 |
two cases | 7 |
encounter location | 7 |
dermal leishmaniasis | 7 |
will increase | 7 |
genomic medicine | 7 |
range infections | 7 |
operational architecture | 7 |
form assumptions | 7 |
standard deviation | 7 |
resistant bacteria | 7 |
infection risks | 7 |
income countries | 7 |
counterfactual locations | 7 |
based models | 7 |
particular time | 7 |
united nations | 7 |
clinical settings | 7 |
disease diffusion | 7 |
two groups | 7 |
isolation individuals | 7 |
based model | 7 |
large variance | 7 |
dynamical processes | 7 |
regular lattice | 7 |
delay distributions | 7 |
much larger | 7 |
sars coronavirus | 7 |
among different | 7 |
entire population | 7 |
two main | 7 |
offline networks | 7 |
hand hygiene | 7 |
data minimisation | 7 |
path length | 7 |
cognitive impairment | 7 |
takes place | 7 |
near realized | 7 |
local information | 7 |
homogeneously mixed | 7 |
older adults | 7 |
may occur | 7 |
human networks | 7 |
data set | 7 |
infected people | 7 |
worth mentioning | 7 |
also provide | 7 |
transmitted diseases | 7 |
animal reservoir | 7 |
twitter users | 7 |
transition function | 7 |
treated region | 7 |
social networking | 7 |
social group | 7 |
behavioral diffusion | 7 |
airborne transmission | 7 |
confirmed cases | 7 |
initial number | 7 |
learning framework | 7 |
pkdl cases | 7 |
acinonyx jubatus | 7 |
crime scene | 6 |
three different | 6 |
vaccination decisions | 6 |
daily activities | 6 |
stuttering chains | 6 |
full sample | 6 |
infected nodes | 6 |
stochastic sir | 6 |
stochastic processes | 6 |
large impact | 6 |
average effects | 6 |
fully susceptible | 6 |
metapopulation models | 6 |
increased risk | 6 |
continuous time | 6 |
disease burden | 6 |
physical activities | 6 |
statistical analysis | 6 |
toward adoption | 6 |
multiple sexual | 6 |
collective self | 6 |
took place | 6 |
contacts per | 6 |
physical distancing | 6 |
last decade | 6 |
online questionnaires | 6 |
aggregate weight | 6 |
worth noting | 6 |
gene transfer | 6 |
bacterial dna | 6 |
infection occurs | 6 |
two networks | 6 |
distance travelled | 6 |
spectrum beta | 6 |
data controllers | 6 |
male coalition | 6 |
aggregate treatment | 6 |
linking dynamics | 6 |
entrepreneurial ideas | 6 |
privacy risks | 6 |
individual becomes | 6 |
may result | 6 |
control effectiveness | 6 |
personal social | 6 |
spatial kernel | 6 |
network models | 6 |
mild symptoms | 6 |
per individual | 6 |
human immunodeficiency | 6 |
instrument one | 6 |
nearby businesses | 6 |
publicly available | 6 |
results show | 6 |
randomly chosen | 6 |
methods i | 6 |
epidemic curve | 6 |
data types | 6 |
large excursion | 6 |
floating population | 6 |
different control | 6 |
antimicrobial peptides | 6 |
supreme court | 6 |
global epidemic | 6 |
sars epidemic | 6 |
rights framework | 6 |
stationary distribution | 6 |
regulated access | 6 |
contact data | 6 |
insider attacks | 6 |
every day | 6 |
sis epidemic | 6 |
lte network | 6 |
affected communities | 6 |
dollar plant | 6 |
particularly relevant | 6 |
million people | 6 |
relative infectiousness | 6 |
every time | 6 |
medrxiv preprint | 6 |
different values | 6 |
simple average | 6 |
short distances | 6 |
attitude towards | 6 |
treatment outcomes | 6 |
relative importance | 6 |
encryption scheme | 6 |
media allows | 6 |
asymptomatic infections | 6 |
baseline setting | 6 |
businesses near | 6 |
much smaller | 6 |
maximum possible | 6 |
partial least | 6 |
demographic group | 6 |
conditional probability | 6 |
green curves | 6 |
sars outbreak | 6 |
significant impact | 6 |
washing hands | 6 |
may influence | 6 |
larger number | 6 |
i argue | 6 |
spreading process | 6 |
get infected | 6 |
health agencies | 6 |
many years | 6 |
symptomatic infection | 6 |
approximate finite | 6 |
planned behavior | 6 |
like particles | 6 |
mean risk | 6 |
intraspecific interactions | 6 |
highly infectious | 6 |
additively separable | 6 |
several countries | 6 |
political polarization | 6 |
technological forecasting | 6 |
antibiotic consumption | 6 |
international law | 6 |
biological materials | 6 |
primate social | 6 |
mitigation strategies | 6 |
infecting others | 6 |
close contacts | 6 |
first place | 6 |
social systems | 6 |
learning algorithms | 6 |
epidemic peak | 6 |
social connection | 6 |
disease surveillance | 6 |
first case | 6 |
present paper | 6 |
networked populations | 6 |
objective function | 6 |
various types | 6 |
forecasting social | 6 |
ring around | 6 |
may require | 6 |
mean field | 6 |
free network | 6 |
perceived risk | 6 |
epidemic outbreak | 6 |
spatial experiment | 6 |
farther away | 6 |
human societies | 6 |
may contribute | 6 |
learning methods | 6 |
structured networks | 6 |
large population | 6 |
care beds | 6 |
may offer | 6 |
network cell | 6 |
commonly used | 6 |
offline network | 6 |
recent overseas | 6 |
assignment mechanism | 6 |
learning model | 6 |
nearest neighbors | 6 |
epidemiological characteristics | 6 |
largest outbreak | 6 |
vast majority | 6 |
hub nodes | 6 |
two categories | 6 |
widely used | 6 |
core areas | 6 |
necessary reference | 6 |
extremely high | 6 |
venereal disease | 6 |
mutation rates | 6 |
random variation | 6 |
small businesses | 6 |
process approximation | 6 |
prophylactic behavior | 6 |
online resource | 6 |
per contact | 6 |
individuals per | 6 |
contact rate | 6 |
likelihood function | 6 |
proximity threshold | 6 |
locations within | 6 |
microbial forensic | 6 |
fixed duration | 6 |
extinction probability | 6 |
european union | 6 |
health problem | 6 |
also shown | 6 |
transmission paths | 6 |
single candidate | 6 |
large numbers | 6 |
immunodeficiency virus | 6 |
wales hospital | 6 |
density function | 6 |
health practice | 6 |
decryption keys | 6 |
random graph | 6 |
technical systems | 6 |
infected i | 6 |
steady state | 6 |
initial state | 6 |
adoption status | 6 |
one week | 6 |
much higher | 6 |
single treatment | 6 |
high degree | 6 |
digital epidemiology | 6 |
may cause | 6 |
randomized experiments | 6 |
healthcare workers | 6 |
time steps | 6 |
temporal dimension | 6 |
donald trump | 6 |
communicable diseases | 6 |
threshold model | 6 |
period outcomes | 6 |
health problems | 6 |
pneumonia mortality | 6 |
platform economy | 6 |
individual members | 6 |
urban social | 6 |
death rates | 6 |
little attention | 6 |
shortest paths | 6 |
well known | 6 |
consultation service | 6 |
workplace closure | 6 |
spatial locations | 6 |
spatial distribution | 6 |
high school | 6 |
health resources | 6 |
household dynamics | 6 |
health genomics | 6 |
ar transmission | 6 |
nw nw | 6 |
adoption pressure | 6 |
preventive measures | 6 |
individual freedom | 5 |
suitable return | 5 |
first one | 5 |
wide variety | 5 |
pathogen evolution | 5 |
equally likely | 5 |
viral load | 5 |
randomly chooses | 5 |
one time | 5 |
transmission trees | 5 |
takes value | 5 |
based contact | 5 |
costs associated | 5 |
available data | 5 |
genetic databases | 5 |
borne diseases | 5 |
relatively large | 5 |
equal probability | 5 |
media users | 5 |
human behaviors | 5 |
developed symptoms | 5 |
modelling disease | 5 |
network update | 5 |
estimated propensity | 5 |
behavioral sciences | 5 |
two classes | 5 |
threshold level | 5 |
authors found | 5 |
personal network | 5 |
travel restrictions | 5 |
time population | 5 |
path analysis | 5 |
time stochastic | 5 |
may explain | 5 |
highly connected | 5 |
hubei province | 5 |
contact structures | 5 |
risky behaviors | 5 |
important aspect | 5 |
offline questionnaire | 5 |
bubonic plague | 5 |
stochastic model | 5 |
centralised server | 5 |
collective rights | 5 |
much less | 5 |
compare individuals | 5 |
cohort study | 5 |
weighting estimators | 5 |
personal networks | 5 |
three months | 5 |
individuals get | 5 |
food animals | 5 |
topic embedding | 5 |
infection process | 5 |
individual member | 5 |
per dyad | 5 |
substance abuse | 5 |
hospitalization days | 5 |
results obtained | 5 |
spatial transmission | 5 |
care providers | 5 |
individuals choose | 5 |
event occurs | 5 |
individual vaccination | 5 |
theoretical work | 5 |
social behavior | 5 |
well established | 5 |
score estimation | 5 |
nearby individuals | 5 |
online questionnaire | 5 |
lte networks | 5 |
sars cases | 5 |
receives treatment | 5 |
two separate | 5 |
influenza control | 5 |
two distinct | 5 |
adrd research | 5 |
one important | 5 |
pathogen virulence | 5 |
distant individuals | 5 |
social sciences | 5 |
one person | 5 |
social structure | 5 |
questionnaire survey | 5 |
node degree | 5 |
infection control | 5 |
respiratory infections | 5 |
relative agreement | 5 |
collective behavior | 5 |
retweet network | 5 |
human enteric | 5 |
encrypted data | 5 |
level scenario | 5 |
multiple distances | 5 |
spatial information | 5 |
metropolitan area | 5 |
may increase | 5 |
vaccination game | 5 |
vary significantly | 5 |
data controller | 5 |
vl incidence | 5 |
spatial statistics | 5 |
experimental design | 5 |
biggest communities | 5 |
small fraction | 5 |
human biological | 5 |
banjul charter | 5 |
perceived feasibility | 5 |
data sources | 5 |
closure strategy | 5 |
posterior distributions | 5 |
return rush | 5 |
cardiovascular disease | 5 |
stochastic simulations | 5 |
animal markets | 5 |
longitudinal data | 5 |
may therefore | 5 |
stage iii | 5 |
spatially explicit | 5 |
waking day | 5 |
second stage | 5 |
health state | 5 |
sample properties | 5 |
rosemary bush | 5 |
ar dissemination | 5 |
spatial settings | 5 |
subsidy policy | 5 |
heterogeneous networks | 5 |
within care | 5 |
southern china | 5 |
spatial overlap | 5 |
spread within | 5 |
individual decision | 5 |
causal effect | 5 |
based encryption | 5 |
adopt covid | 5 |
one treatment | 5 |
linkable anonymity | 5 |
sized town | 5 |
family relationships | 5 |
disease prevention | 5 |
indicator function | 5 |
expected value | 5 |
susceptible hosts | 5 |
based survey | 5 |
influenza outbreaks | 5 |
external threats | 5 |
drinking water | 5 |
also play | 5 |
peak infectivity | 5 |
finite populations | 5 |
tap pb | 5 |
different countries | 5 |
finite number | 5 |
urbanized area | 5 |
human contacts | 5 |
probability weighting | 5 |
bacterial pathogens | 5 |
may provide | 5 |
hiv transmission | 5 |
small business | 5 |
impact size | 5 |
campus contact | 5 |
research suggests | 5 |
social science | 5 |
dna fingerprinting | 5 |
parameters used | 5 |
un framework | 5 |
health issues | 5 |
recurrent emergence | 5 |
privacy policies | 5 |
social settings | 5 |
influenza containment | 5 |
shortest path | 5 |
high risk | 5 |
disease incidence | 5 |
random graphs | 5 |
degree nodes | 5 |
infection searching | 5 |
infectious periods | 5 |
individuals infected | 5 |
connected individuals | 5 |
host populations | 5 |
second case | 5 |
based simulation | 5 |
aadhaar ids | 5 |
near treatment | 5 |
data analytics | 5 |
static networks | 5 |
homomorphic encryption | 5 |
mortality rate | 5 |
treatment assignments | 5 |
traced back | 5 |
small world | 5 |
city center | 5 |
seasonal flu | 5 |
inferential privacy | 5 |
value one | 5 |
true number | 5 |
rewiring probability | 5 |
three candidate | 5 |
sick leave | 5 |
firm entry | 5 |
new treatment | 5 |
urban areas | 5 |
different geographical | 5 |
resource search | 5 |
attack rates | 5 |
large sample | 5 |
also needs | 5 |
store location | 5 |
long run | 5 |
health research | 5 |
dead individuals | 5 |
based methods | 5 |
based path | 5 |
epidemiological data | 5 |
update events | 5 |
extensive numerical | 5 |
per cell | 5 |
cognitive reappraisal | 5 |
final approval | 5 |
also discussed | 5 |
cognitive impairments | 5 |
goes extinct | 5 |
recent empirical | 5 |
section discusses | 5 |
public service | 5 |
heterogeneous models | 5 |
care system | 5 |
carlo simulation | 5 |
approved tes | 5 |
disease status | 5 |
based data | 5 |
next generation | 5 |
black death | 5 |
human gut | 5 |
compartmental models | 5 |
sexual behavior | 5 |
contact searching | 5 |
individuals due | 5 |
one needs | 5 |
automatic methods | 5 |
differenced outcomes | 5 |
may even | 5 |
collected data | 5 |
finite population | 5 |
regular lattices | 5 |
information available | 5 |
endemic equilibrium | 5 |
bacterial communities | 5 |
disease name | 5 |
high mutation | 5 |
dynamic exposure | 5 |
local awareness | 5 |
individualized thresholds | 5 |
initially infected | 5 |
new infection | 5 |
effects across | 5 |
complex network | 5 |
server te | 5 |
dashed line | 5 |
one candidate | 5 |
effective tool | 5 |
phase transition | 5 |
ethical issues | 5 |
individuals seek | 5 |
existing estimators | 5 |
pb scenario | 5 |
period distribution | 5 |
background transmission | 5 |
individual risk | 5 |
general data | 5 |
modeling approach | 5 |
spatial econometrics | 5 |
making process | 5 |
public policy | 5 |
exposure window | 5 |
disease must | 5 |
public services | 5 |
nipah virus | 5 |
infection will | 5 |
asymptomatically infected | 5 |
local transition | 5 |
informational privacy | 5 |
infected host | 5 |
first two | 5 |
infectious state | 5 |
collective dynamics | 5 |
ct image | 5 |
transmission model | 5 |
population will | 5 |
taking place | 5 |
tracing capacity | 5 |
human pathogens | 5 |
higher levels | 5 |
wearing facemasks | 5 |
bacterial species | 5 |
poor hygiene | 5 |
per unit | 5 |
pandemic outbreak | 5 |
two nodes | 5 |
world bank | 5 |
demographic processes | 5 |
tracing model | 5 |
penelope smith | 5 |
making use | 5 |
dynamic interactions | 5 |
symptomatic individual | 5 |
global scale | 5 |
network providers | 5 |
sufficiently large | 5 |
less effective | 5 |
data provider | 5 |
per day | 5 |
negative information | 5 |
contact events | 5 |
close friends | 5 |
weekly number | 5 |
critical role | 5 |
palm civets | 5 |
household structure | 5 |
will allow | 5 |
mobility information | 5 |
avoid contact | 5 |
years old | 5 |
pathogen may | 5 |
infection spreads | 5 |
individuals need | 5 |
older individuals | 5 |
avoiding contact | 5 |
average aggregate | 5 |
neighbor nodes | 5 |
primary school | 5 |
international biobanking | 5 |
fourth term | 5 |
economic impact | 5 |
swine flu | 5 |
social rules | 5 |
just one | 5 |
social isolation | 5 |
carlo method | 5 |
city centers | 5 |
particular disease | 5 |
virus transmission | 5 |
available information | 5 |
another individual | 5 |
scale population | 5 |
fifth term | 5 |
gastroenteritis group | 5 |
substantial contributions | 5 |
common good | 5 |
modified behavioral | 5 |
i infected | 5 |
collateral sensitivity | 5 |
good approximation | 5 |
risk individuals | 5 |
increasing number | 5 |
time delayed | 5 |
location matters | 5 |
following sections | 5 |
web version | 5 |
network modeling | 5 |
thresholds toward | 5 |
temporal dynamics | 5 |
score weighting | 5 |
recent advances | 5 |
experimental methods | 5 |
epidemic starts | 5 |
social contagion | 5 |
urban planning | 5 |
epidemics using | 5 |
distance max | 5 |
face contact | 5 |
direct physical | 5 |
ring vs | 5 |
better understanding | 5 |
transmission among | 5 |
first three | 5 |
based method | 4 |
random network | 4 |
cognitive behavioral | 4 |
information processing | 4 |
search engine | 4 |
will consider | 4 |
policy making | 4 |
spreading dynamics | 4 |
note springer | 4 |
human interactions | 4 |
overall epidemic | 4 |
allows individuals | 4 |
general principles | 4 |
temporal contact | 4 |
epidemic situation | 4 |
risk aversion | 4 |
us centers | 4 |
animal production | 4 |
statistical distributions | 4 |
seven pillars | 4 |
human well | 4 |
different scenarios | 4 |
environmental monitoring | 4 |
may fail | 4 |
individuals living | 4 |
perform well | 4 |
value judgments | 4 |
clearly shows | 4 |
two possible | 4 |
stochastic simulation | 4 |
least square | 4 |
private mechanisms | 4 |
social structures | 4 |
one large | 4 |
outbreaks will | 4 |
loved one | 4 |
death costs | 4 |
initial stage | 4 |
far away | 4 |
may choose | 4 |
time investment | 4 |
sex differences | 4 |
health approach | 4 |
gut microbiota | 4 |
expected values | 4 |
small excursions | 4 |
network technologies | 4 |
via ble | 4 |
models may | 4 |
international human | 4 |
given distance | 4 |
recent developments | 4 |
current situation | 4 |
objects used | 4 |
important component | 4 |
case scenario | 4 |
individual mobility | 4 |
feature importance | 4 |
institutional affiliations | 4 |
louvain method | 4 |
faecal specimens | 4 |
assignment distribution | 4 |
control policy | 4 |
observational settings | 4 |
two assumptions | 4 |
degree class | 4 |
gets infected | 4 |
resistance gene | 4 |
spatially correlated | 4 |
jupyter notebook | 4 |
van der | 4 |
treatment status | 4 |
spike protein | 4 |
urban area | 4 |
probability density | 4 |
exogenous shock | 4 |
secondary infection | 4 |
recent outbreaks | 4 |
average connectivity | 4 |
stationary state | 4 |
mathematical tools | 4 |
galkina chetty | 4 |
researchers need | 4 |
forensic science | 4 |
world structure | 4 |
protection framework | 4 |
may evolve | 4 |
output data | 4 |
attractive interpretation | 4 |
time allocation | 4 |
person may | 4 |
behavior may | 4 |
persuasive topics | 4 |
important factor | 4 |
intensive care | 4 |
risk among | 4 |
data will | 4 |
social bonds | 4 |
initial conditions | 4 |
following three | 4 |
ordinary differential | 4 |
authorisation rules | 4 |
marginal probability | 4 |
tackle ar | 4 |
infection also | 4 |
springer nature | 4 |
strong social | 4 |
transmission mechanisms | 4 |
private information | 4 |
will decrease | 4 |
day outside | 4 |
health studies | 4 |
cold war | 4 |
contact layer | 4 |
behavior models | 4 |
two parameters | 4 |
times anonymous | 4 |
may find | 4 |
effect estimates | 4 |
directly applicable | 4 |
parameter estimates | 4 |
infected person | 4 |
positive impact | 4 |
population census | 4 |
twitter networks | 4 |
nationally representative | 4 |
school closure | 4 |
briefly discuss | 4 |
local level | 4 |
spatial data | 4 |
human flourishing | 4 |
large one | 4 |
behavior among | 4 |
influenza transmission | 4 |
evolutionary games | 4 |
per se | 4 |
another one | 4 |
data obtained | 4 |
generation times | 4 |
influenza surveillance | 4 |
proposed framework | 4 |
dbt recipients | 4 |
different intervention | 4 |
third equality | 4 |
grid cells | 4 |
may prevent | 4 |
normally distributed | 4 |
imperfect vaccination | 4 |
individuals increase | 4 |
case data | 4 |
individual exposure | 4 |
single strategies | 4 |
sf network | 4 |
structured populations | 4 |
ring individuals | 4 |
wc scenario | 4 |
black box | 4 |
regression models | 4 |
disturbed internal | 4 |
following section | 4 |
future outbreaks | 4 |
influenza activity | 4 |
results indicate | 4 |
particular day | 4 |
relative distances | 4 |
death toll | 4 |
medical treatment | 4 |
existing literature | 4 |
disease symptoms | 4 |
maintaining social | 4 |
based simulations | 4 |
first time | 4 |
affected workplaces | 4 |
home quarantine | 4 |
different ways | 4 |
social dynamics | 4 |
will cause | 4 |
epidemic process | 4 |
daily life | 4 |
workers may | 4 |
potential risks | 4 |
highly heterogeneous | 4 |
dynamical features | 4 |
dotted lines | 4 |
virtual social | 4 |
disease may | 4 |
infection dynamics | 4 |
infectious agents | 4 |
near real | 4 |
spring festival | 4 |
dunbar graphs | 4 |
selfemployed individuals | 4 |
capturing human | 4 |
lower levels | 4 |
global pandemics | 4 |
individual privacy | 4 |
pathogen barely | 4 |
may go | 4 |
crisis may | 4 |
louisville school | 4 |
sars pandemic | 4 |
one outbreak | 4 |
wild dogs | 4 |
develop symptoms | 4 |
theoretic approach | 4 |
i denotes | 4 |
much lower | 4 |
reasonable precautions | 4 |
much slower | 4 |
social influence | 4 |
average outcomes | 4 |
sensitivity analysis | 4 |
may allow | 4 |
network layers | 4 |
different age | 4 |
short term | 4 |
health conditions | 4 |
protection regulation | 4 |
location per | 4 |
predictive relevance | 4 |
digital signature | 4 |
anonymized positions | 4 |
vl case | 4 |
reliable information | 4 |
models using | 4 |
community may | 4 |
anonymous authentication | 4 |
direct benefit | 4 |
privacy preserving | 4 |
fitness costs | 4 |
one infected | 4 |
frequently visited | 4 |
one natural | 4 |
animal origin | 4 |
goes beyond | 4 |
intensive farming | 4 |
first day | 4 |
reported cases | 4 |
observed cases | 4 |
consider two | 4 |
country level | 4 |
higher rates | 4 |
reality mining | 4 |
external floating | 4 |
chest ct | 4 |
space use | 4 |
health records | 4 |
settings may | 4 |
i use | 4 |
i steps | 4 |
treatment probabilities | 4 |
phlebotomus argentipes | 4 |
neighbor households | 4 |
existing empirical | 4 |
effective interventions | 4 |
infected patients | 4 |
intermediate time | 4 |
variable analysis | 4 |
social world | 4 |
phone data | 4 |
certain individuals | 4 |
urban resilience | 4 |
cpfs survey | 4 |
occurred within | 4 |
potential risk | 4 |
numerical analysis | 4 |
based studies | 4 |
content analysis | 4 |
equilibrium point | 4 |
seijr model | 4 |
social time | 4 |
tr pb | 4 |
social relationships | 4 |
higher level | 4 |
may perceive | 4 |
entrepreneurial idea | 4 |
useful information | 4 |
exponentially distributed | 4 |
spillover effects | 4 |
may change | 4 |
deterministic model | 4 |
world wide | 4 |
far apart | 4 |
outbreak data | 4 |
host specificity | 4 |
unvaccinated individuals | 4 |
betweenness centrality | 4 |
possible world | 4 |
mean value | 4 |
graph machine | 4 |
wave packets | 4 |
two years | 4 |
reproduction numbers | 4 |
section presents | 4 |
shifting users | 4 |
coordination problem | 4 |
two key | 4 |
results suggest | 4 |
individual spends | 4 |
time evolution | 4 |
hiv infection | 4 |
mathematical modelling | 4 |
sirv model | 4 |
restriction strategy | 4 |
additional within | 4 |
literature review | 4 |
health benefits | 4 |
resistance determinants | 4 |
mildly symptomatic | 4 |
rapidly evolving | 4 |
section concludes | 4 |
heterogeneous effects | 4 |
unidad ciudadana | 4 |
endogeneity issues | 4 |
private key | 4 |
individual incentive | 4 |
gut microbiome | 4 |
regulatory oversight | 4 |
large enough | 4 |
potential impact | 4 |
compartment i | 4 |
regulatory access | 4 |
agreement model | 4 |
blind signatures | 4 |
diseases like | 4 |
perceived behavioral | 4 |
remote attestation | 4 |
voluntarily adopt | 4 |
healthcare costs | 4 |
one node | 4 |
expected outcome | 4 |
powdery mildew | 4 |
activation scores | 4 |
literature suggests | 4 |
african wild | 4 |
scale contact | 4 |
empirical evidence | 4 |
hospital admission | 4 |
two processes | 4 |
voronoi tessellations | 4 |
detection system | 4 |
bernoulli trials | 4 |
recent research | 4 |
onset date | 4 |
static group | 4 |
equation modeling | 4 |
mutational resistome | 4 |
level combination | 4 |
daily contacts | 4 |
scaling ratio | 4 |
clinical trials | 4 |
processing techniques | 4 |
month period | 4 |
subjective norms | 4 |
working group | 4 |
hospital staff | 4 |
authentication information | 4 |
uniform immunization | 4 |
also relevant | 4 |
layer network | 4 |
immune individuals | 4 |
observed outcome | 4 |
privacy risk | 4 |
two instruments | 4 |
will depend | 4 |
value sd | 4 |
data sets | 4 |
bank account | 4 |
emotional distress | 4 |
university students | 4 |
output channels | 4 |
endorphin system | 4 |
increased demand | 4 |
communication technologies | 4 |
phase transitions | 4 |
optimal control | 4 |
will never | 4 |
layer sizes | 4 |
district fixed | 4 |
economic growth | 4 |
density maps | 4 |
symptomatic period | 4 |
threshold value | 4 |
model results | 4 |
average path | 4 |
streptococcus pneumoniae | 4 |
international conference | 4 |
model realizations | 4 |
elderly population | 4 |
global contacts | 4 |
human development | 4 |
structural equation | 4 |
containing pandemic | 4 |
cheng shiu | 4 |
inner layers | 4 |
prove useful | 4 |
particular relevance | 4 |
behaviour change | 4 |
new host | 4 |
potential infection | 4 |
direct contacts | 4 |
study human | 4 |
information propagation | 4 |
three single | 4 |
two vertices | 4 |
suppress epidemic | 4 |
everyone else | 4 |
transmission rates | 4 |
initial value | 4 |
substance use | 4 |
ethical principles | 4 |
represent individuals | 4 |
population perspective | 4 |
human ace | 4 |
cvlp excretors | 4 |
predictive models | 4 |
certain probability | 4 |
case numbers | 4 |
across individuals | 4 |
cell identity | 4 |
constant force | 4 |
central business | 4 |
knowledge gaps | 4 |
three dyads | 4 |
first model | 4 |
early days | 4 |
also possible | 4 |
established using | 4 |
effect will | 4 |
hospitalization rates | 4 |
kernel function | 4 |
also found | 4 |
molecular biology | 4 |
carlo simulations | 4 |
role model | 4 |
stage ii | 4 |
growing force | 4 |
zoonotic pathogen | 4 |
conditional probabilities | 4 |
legal principles | 4 |
cell radii | 4 |
layered structures | 4 |
proposed system | 4 |
searchable encryption | 4 |
three control | 4 |
communication layer | 4 |
historical city | 4 |
endogenous variable | 4 |
gives rise | 4 |
risk posed | 4 |
controlled encryption | 4 |
diffusion framework | 4 |
bird flu | 4 |
proposed approach | 4 |
risk factor | 4 |
study variables | 4 |
largest number | 4 |
puttaswamy i | 4 |
wage employment | 4 |
negative binomial | 4 |
infected will | 4 |
given time | 4 |
job loss | 4 |
model described | 4 |
level effects | 4 |
also analyzed | 4 |
recent past | 4 |
contact process | 4 |
human infection | 4 |
use may | 4 |
mobile genetic | 4 |
behavioral control | 4 |
well beyond | 4 |
pandemic times | 4 |
low virulence | 4 |
acceptance rate | 4 |
effective infectiousness | 4 |
stores may | 4 |
unemployment insurance | 4 |
wc pb | 4 |
will help | 4 |
physical exercise | 4 |
personal protective | 4 |
effective strategy | 4 |
novel pathogens | 4 |
path lengths | 4 |
different treatment | 4 |
west africa | 4 |
i focus | 4 |
momentary assessment | 4 |
two conditions | 4 |
european parliament | 4 |
aggregate effect | 4 |
different contexts | 4 |
imitation behavior | 4 |
persuasive arguments | 4 |
nature remains | 4 |
surrounding individuals | 4 |
contiguous region | 4 |
remains neutral | 4 |
also find | 4 |
health professionals | 4 |
frequently used | 4 |
methods proposed | 4 |
state may | 4 |
research interests | 4 |
vaccination campaign | 4 |
model introduced | 4 |
social norm | 4 |
protective equipment | 4 |
characteristics space | 4 |
susceptible state | 4 |
infected case | 4 |
ecological momentary | 4 |
used social | 4 |
may seem | 4 |
receiving treatment | 4 |
stage least | 4 |
business district | 4 |
awareness diffusion | 4 |
individual scale | 4 |
gastroenteritis control | 4 |
infectious rate | 4 |
percentage points | 4 |
large data | 4 |
region receives | 4 |
electronic health | 4 |
matrix factorization | 4 |
also influence | 4 |
individuals interact | 4 |
simulation models | 4 |
percolation theory | 4 |
theoretical studies | 4 |
adaptive behavior | 4 |
traditional offline | 4 |
individual characteristics | 4 |
degree hki | 4 |
analytical prediction | 4 |
move forward | 4 |
observed data | 4 |
transmission tree | 4 |
disease system | 4 |
particular percentage | 4 |
resource scarcity | 4 |
health consequences | 4 |
different infections | 4 |
actual value | 4 |
key factors | 4 |
clinical practice | 4 |
guangdong province | 4 |
flu trends | 4 |
pkdl case | 4 |
i represents | 4 |
health systems | 4 |
population spends | 4 |
mapping system | 4 |
article proposes | 4 |
countries around | 4 |
vaccine uptake | 4 |
recent studies | 4 |
different models | 4 |
classification scheme | 4 |
topological properties | 4 |
classification task | 4 |
urban life | 4 |
receive treatment | 4 |
wild animals | 4 |
fully understand | 4 |
provide valuable | 4 |
negative matrix | 4 |
network adaptation | 4 |
first generation | 4 |
modeling infectious | 4 |
future work | 4 |
york city | 4 |
cellular automaton | 4 |
complex social | 4 |
hand washing | 4 |
across space | 4 |
service places | 4 |
may receive | 4 |
last two | 4 |
human activities | 4 |
universal declaration | 4 |
exposure windows | 4 |
objective basis | 4 |
interaction among | 4 |
tracing strategies | 4 |
i define | 4 |
additional grocery | 4 |
various kinds | 4 |
uninfected individuals | 4 |
hotel metropole | 4 |
questionnaire data | 4 |
see text | 4 |
dunbar graph | 4 |
scale separation | 4 |
gps coordinates | 4 |
moderating effect | 4 |
household models | 4 |
flu prophylaxis | 4 |
adherence levels | 4 |
giving rise | 4 |
evolutionary game | 4 |
data must | 4 |
behavioral strategies | 4 |
longer distances | 4 |
healthy environment | 4 |
jurisdictional claims | 4 |
genetic research | 4 |
work formats | 4 |
design principles | 4 |
female baboons | 4 |
immunity certificates | 4 |
predominantly infected | 4 |
went extinct | 4 |
published maps | 4 |
will see | 4 |
health authorities | 4 |
significant role | 4 |
many cases | 4 |
tensor deconvolution | 4 |
protective measures | 4 |
relative locations | 4 |
law degree | 4 |
bernoulli trial | 4 |
will occur | 4 |
arg pr | 4 |
younger persons | 4 |
altruistic behavior | 4 |
intel sgx | 4 |
best return | 4 |
experiment estimator | 4 |
working population | 4 |
may arise | 4 |
single infected | 4 |
old world | 4 |
transmission parameters | 4 |
small sample | 4 |
rules laws | 4 |
may involve | 4 |
home will | 4 |
spatial location | 4 |
decision makers | 4 |
weighting estimator | 4 |
also need | 4 |
years ago | 4 |
individuals remain | 4 |
infectious lines | 4 |
disease expansion | 4 |
one month | 4 |
secondary cases | 4 |
behavioral dynamics | 4 |
whose value | 4 |
spatial scale | 4 |
early detection | 4 |
within households | 4 |
population health | 4 |
first stage | 4 |
high levels | 4 |
complete data | 4 |
common cold | 4 |
realized locations | 4 |
probability per | 4 |
factors influencing | 4 |
exposure estimate | 4 |
modeling approaches | 4 |
models based | 4 |
random variable | 4 |
social dilemmas | 4 |
entrepreneurship scholars | 3 |
may signal | 3 |
business owners | 3 |
networks effects | 3 |
disease within | 3 |
pathogen transmission | 3 |
neighborhood characteristics | 3 |
influenza viruses | 3 |
vaccination dynamics | 3 |
condition holds | 3 |
default mode | 3 |
risks associated | 3 |
remote assessment | 3 |
administrative data | 3 |
group level | 3 |
prior research | 3 |
miles away | 3 |
real data | 3 |
third parties | 3 |
relative spatial | 3 |
san francisco | 3 |
agent i | 3 |
computational modeling | 3 |
entrepreneurial phenomena | 3 |
blue curve | 3 |
economic costs | 3 |
male mortality | 3 |
treated states | 3 |
worldwide spread | 3 |
may significantly | 3 |
human ecology | 3 |
networks epidemic | 3 |
also consistent | 3 |
weekly basis | 3 |
acquiring information | 3 |
like illness | 3 |
address endogeneity | 3 |
surveillance systems | 3 |
encryption schemes | 3 |
intellectually retarded | 3 |
encryption allows | 3 |
health agency | 3 |
edged sword | 3 |
distancing interventions | 3 |
binding domain | 3 |
specific values | 3 |
often associated | 3 |
based access | 3 |
manage stress | 3 |
consistent across | 3 |
events occurred | 3 |
antiviral use | 3 |
explicit promise | 3 |
epidemic peaks | 3 |
main analyses | 3 |
policy decisions | 3 |
human animal | 3 |
may begin | 3 |
diffusion dynamics | 3 |
transition probabilities | 3 |
close ties | 3 |
spatiotemporal data | 3 |
throughput sequencing | 3 |
cross sectional | 3 |
net dis | 3 |
anthropoid primates | 3 |
often assumed | 3 |
first equality | 3 |
treated location | 3 |
vaccine coverage | 3 |
major cause | 3 |
single infection | 3 |
contact switching | 3 |
also present | 3 |
obtained via | 3 |
immune system | 3 |
risk compensation | 3 |
land use | 3 |
actual situation | 3 |
network cells | 3 |
directed towards | 3 |
evolutionary biology | 3 |
introduced pathogen | 3 |
people know | 3 |
launch phase | 3 |
negatively correlated | 3 |
epidemic may | 3 |
hosts may | 3 |
level effect | 3 |
social bonding | 3 |
check method | 3 |
example illustrates | 3 |
adverse events | 3 |
entire database | 3 |
one knows | 3 |
particular locations | 3 |
dynamic social | 3 |
distributed across | 3 |
one case | 3 |
fields wherein | 3 |
economic shocks | 3 |
may differ | 3 |
stochastic epidemic | 3 |
highly clustered | 3 |
uncorrelated networks | 3 |
fake information | 3 |
rate depends | 3 |
tested positive | 3 |
statistical mechanics | 3 |
smaller attack | 3 |
remains constant | 3 |
healthcare demand | 3 |
rights council | 3 |
current literature | 3 |
local population | 3 |
regions without | 3 |
aggregate outcome | 3 |
research directions | 3 |
distance apart | 3 |
optimal spillover | 3 |
competing interests | 3 |
outbreak probability | 3 |
legal framework | 3 |
therefore recommend | 3 |
asynchronous updating | 3 |
misbehaving users | 3 |
three stages | 3 |
santiago maldonado | 3 |
limited number | 3 |
th day | 3 |
might also | 3 |
national laws | 3 |
static individual | 3 |
distancing will | 3 |
indirect human | 3 |
assessing vaccination | 3 |
spread among | 3 |
geographic dispersion | 3 |
gastroenteritis excreting | 3 |
individuals i | 3 |
symptomatic infectious | 3 |
extensive spatial | 3 |
overall effect | 3 |
observed facts | 3 |
virulent pathogens | 3 |
store nearby | 3 |
antimicrobial agents | 3 |
within china | 3 |
disease spreads | 3 |
density distribution | 3 |
contracting covid | 3 |
individual within | 3 |
remain valid | 3 |
health behaviors | 3 |
ss links | 3 |
health belief | 3 |
will focus | 3 |
incentive increases | 3 |
idea behind | 3 |
may impact | 3 |
health pandemic | 3 |
realistic social | 3 |
developed countries | 3 |
mif model | 3 |
new epidemic | 3 |
will decide | 3 |
zoonotic infections | 3 |
expressed symptoms | 3 |
antibiotics use | 3 |
secondary vl | 3 |
average age | 3 |
convex combination | 3 |
outside world | 3 |
detecting influenza | 3 |
borderline cases | 3 |
risk distributions | 3 |
large epidemic | 3 |
occupational hazards | 3 |
disease free | 3 |
bacterial populations | 3 |
small firms | 3 |
epidemic curves | 3 |
generation rights | 3 |
mathematical modeling | 3 |
disease classification | 3 |
search queries | 3 |
facilitate reading | 3 |
taking antiviral | 3 |
bank accounts | 3 |
new pathogens | 3 |
different data | 3 |
weight placed | 3 |
straight line | 3 |
disease containment | 3 |
usually assumed | 3 |
moved away | 3 |
political parties | 3 |
therapeutic strategies | 3 |
still absent | 3 |
obtain information | 3 |
may enhance | 3 |
also used | 3 |
particular country | 3 |
health indicators | 3 |
bacterial infection | 3 |
benefits will | 3 |
prospective cohort | 3 |
contact via | 3 |
individuals whose | 3 |
make decisions | 3 |
certain rights | 3 |
predicted results | 3 |
also increases | 3 |
different groups | 3 |
reflective natural | 3 |
unique identifier | 3 |
closest realized | 3 |
without dementia | 3 |
dynamic interaction | 3 |
named covid | 3 |
digital traces | 3 |
make control | 3 |
assumption may | 3 |
partly incongruent | 3 |
perceived payoffs | 3 |
faster diffusion | 3 |
world social | 3 |
human constructions | 3 |
data type | 3 |
emerging infections | 3 |
estimated treatment | 3 |
physical health | 3 |
highest accuracy | 3 |
dispositional predicates | 3 |
network parameters | 3 |
variable strategy | 3 |
fitting model | 3 |
outbreaks occur | 3 |
employment law | 3 |
data access | 3 |
small proportion | 3 |
transmitted disease | 3 |
conceptual model | 3 |
online platform | 3 |
disease information | 3 |
small enough | 3 |
without interference | 3 |
capital theory | 3 |
drug development | 3 |
natural logarithm | 3 |
discrete individuals | 3 |
physical location | 3 |
control study | 3 |
generation size | 3 |
incomplete without | 3 |
symptomatic periods | 3 |
virtual world | 3 |
infectious spread | 3 |
family ties | 3 |
high infectivity | 3 |
daily time | 3 |
knowledge gap | 3 |
global intervention | 3 |
identify individuals | 3 |
causal explanation | 3 |
individual proximity | 3 |
amino acid | 3 |
possible reason | 3 |
random walk | 3 |
nothing adherence | 3 |
three groups | 3 |
strategies include | 3 |
dependent average | 3 |
four dyads | 3 |
network sizes | 3 |
thereby allowing | 3 |
single individuals | 3 |
simple sir | 3 |
control groups | 3 |
upper limit | 3 |
behavior interactions | 3 |
absolute locations | 3 |
individuals around | 3 |
finite set | 3 |
current empirical | 3 |
negative correlation | 3 |
basic modeling | 3 |
times per | 3 |
mean size | 3 |
largely depends | 3 |
shall employ | 3 |
infectious process | 3 |
trusted authority | 3 |
topic decomposition | 3 |
markov process | 3 |
independence movement | 3 |
older persons | 3 |
computing infrastructure | 3 |
entrepreneurial ventures | 3 |
younger people | 3 |
joint posterior | 3 |
authors declare | 3 |
emotional states | 3 |
inflection point | 3 |
spreading rate | 3 |
approximate estimator | 3 |
distance away | 3 |
us now | 3 |
underlying health | 3 |
main topics | 3 |
connected nodes | 3 |
stress disorder | 3 |
control region | 3 |
also typically | 3 |
nonpharmaceutical interventions | 3 |
higher threshold | 3 |
sanitary systems | 3 |
two locations | 3 |
people density | 3 |
global networks | 3 |
animal populations | 3 |
may become | 3 |
also reduces | 3 |
without revealing | 3 |
without individual | 3 |
district level | 3 |
one third | 3 |
mobile devices | 3 |
approach may | 3 |
analytic hierarchy | 3 |
estimated using | 3 |
forensic analysis | 3 |
patterns relevant | 3 |
protection bill | 3 |
many diseases | 3 |
various control | 3 |
effects similar | 3 |
spreading processes | 3 |
distancing strategy | 3 |
new ventures | 3 |
personal health | 3 |
structures within | 3 |
three concepts | 3 |
biological explanation | 3 |
er random | 3 |
spatial effectiveness | 3 |
also contribute | 3 |
will become | 3 |
substantial increase | 3 |
financial networks | 3 |
across candidate | 3 |
newly connected | 3 |
twentieth century | 3 |
framework framework | 3 |
behavioral epidemiology | 3 |
supercritical branching | 3 |
communication technology | 3 |
found construct | 3 |