Frontiers in Public Health,
Journal Year:
2022,
Volume and Issue:
10
Published: July 26, 2022
Since
the
outbreak
of
Coronavirus
Disease
2019
(COVID-19),
Chinese
government
has
taken
a
number
measures
to
effectively
control
pandemic.
By
end
2021,
China
achieved
full
vaccination
rate
higher
than
85%.
The
Plan
provides
an
important
model
for
global
fight
against
COVID-19.
Internet
search
reflects
public's
attention
toward
and
potential
demand
particular
thing.
Research
on
spatiotemporal
characteristics
online
vaccines
can
determine
distribution
vaccine
in
basis
public
health
policy
making.
This
study
analyzes
their
influencing
factors
31
provinces/municipalities
mainland
with
Baidu
Index
as
data
source
by
using
geographic
concentration
index,
coefficient
variation,
GeoDetector,
other
methods.
following
findings
are
presented.
First,
showed
overall
upward
trend
since
2011,
especially
after
2016.
Significant
seasonal
differences
unbalanced
monthly
were
observed.
Second,
there
was
obvious
geographical
imbalance
among
provinces/municipalities,
generally
exhibiting
spatial
pattern
"high
east
low
west."
Low
aggregation
dispersion
also
hot
cold
spots
clear
boundaries.
mainly
distributed
central-eastern
provinces
western
provinces.
Third,
combined
result
socioeconomic
level,
socio-demographic
characteristics,
disease
level.
JMIR Public Health and Surveillance,
Journal Year:
2023,
Volume and Issue:
10, P. e49185 - e49185
Published: Dec. 7, 2023
Public
health
surveillance
plays
a
vital
role
in
informing
public
decision-making.
The
onset
of
the
COVID-19
pandemic
early
2020
caused
widespread
shift
priorities.
Global
efforts
focused
on
monitoring
and
contact
tracing.
Existing
programs
were
interrupted
due
to
physical
distancing
measures
reallocation
resources.
intersected
with
advancements
technologies
that
have
potential
support
efforts.
Journal of Medical Internet Research,
Journal Year:
2022,
Volume and Issue:
25, P. e42519 - e42519
Published: Nov. 30, 2022
Background
The
potential
to
harness
the
plurality
of
available
data
in
real
time
along
with
advanced
analytics
for
accurate
prediction
influenza-like
illness
(ILI)
outbreaks
has
gained
significant
scientific
interest.
Different
methodologies
based
on
use
machine
learning
techniques
and
traditional
alternative
sources,
such
as
ILI
surveillance
reports,
weather
search
engine
queries,
social
media,
have
been
explored
ultimate
goal
being
used
development
electronic
systems
that
could
complement
existing
monitoring
resources.
Objective
scope
this
study
was
investigate
first
combined
data,
Twitter
deep
toward
models
able
nowcast
forecast
weekly
cases.
By
assessing
predictive
power
both
sources
case
ILI,
aimed
provide
a
novel
approach
corroborating
evidence
enhancing
accuracy
reliability
infectious
diseases.
Methods
model’s
input
space
consisted
information
related
surveillance,
web-based
(eg,
Twitter)
behavior,
conditions.
For
design
model,
relevant
corresponding
period
2010
2019
focusing
Greek
population
were
collected.
Long
short-term
memory
(LSTM)
neural
networks
leveraged
efficiently
handle
sequential
nonlinear
nature
multitude
collected
data.
3
categories
separately
training
LSTM-based
primary
models.
Subsequently,
different
transfer
(TL)
approaches
aim
creating
various
feature
spaces
combining
features
extracted
from
models’
LSTM
layers
latter
feed
dense
layer.
Results
model
learned
yielded
better
(root
mean
square
error
[RMSE]=0.144;
Pearson
correlation
coefficient
[PCC]=0.801)
than
trained
historical
(RMSE=0.159;
PCC=0.794).
best
performance
achieved
by
TL-based
leveraging
combination
(RMSE=0.128;
PCC=0.822).
Conclusions
superiority
which
considers
reflects
public
enhance
reliable
spread.
Despite
its
focus
Greece,
proposed
can
be
generalized
other
locations,
populations,
media
platforms
support
diseases
reinforcing
preparedness
future
epidemics.
Cureus,
Journal Year:
2022,
Volume and Issue:
unknown
Published: Sept. 23, 2022
Background
In
light
of
the
ongoing
coronavirus
disease
2019
(COVID-19)
pandemic,
vaccination
is
one
most
important
defensive
strategies
in
combating
severe
acute
respiratory
syndrome
2
(SARS-CoV-2).
Vaccine
hesitancy
or
anti-vaccination
attitude
has
become
a
barrier
to
nationwide
program,
potentially
sabotaging
effectiveness
vaccination.
Thus
far,
Google
Trends
(GT)
been
used
extensively
for
monitoring
information-seeking
behavior
during
pandemic.
We
aimed
investigate
association
between
search,
rate,
and
number
vaccinated
infected
cases
among
Malaysian
population.
Material
method
GT's
customizable
geographic
temporal
filters
were
applied
include
results
predetermined
keywords
from
January
1,
2021,
December
31,
2021.
Both
Malay
English
languages
reflect
multi-racial
multi-lingual
community
Malaysia.
The
search
volume
index
(SVI)
derived
was
compared
with
numbers
which
extracted
open-access
database
(COVIDNOW
Malaysia)
within
same
period.
analyses
performed
independently
by
two
authors
ensure
accuracy
data
extraction
process.
A
descriptive
analysis
compare
GT
daily
vaccinations
positive
COVID-19
cases.
Results
public
fluctuated
time
time.
interest
surged
initiation
program
upon
outbreak
surge
prior
peak
rate
also
indicated
that
tended
get
information
online
getting
vaccines.
Conclusion
This
observational
study
illustrates
ability
monitor
population
By
dynamic
changes
Trends,
healthcare
authorities
can
glimpse
perceptions
such
as
towards
vaccine,
hence
identify
stymie
any
dangerous
rhetoric
swiftly.
Children,
Journal Year:
2022,
Volume and Issue:
9(12), P. 1984 - 1984
Published: Dec. 16, 2022
The
aim
of
this
study
was
to
evaluate
whether
or
not
online
queries
for
Respiratory
Syncytial
Virus
(RSV)
retrieved
by
means
Google
Trends™
and
the
Italian
Wikipedia
analysis
program
mirror
occurrence
influenza-like
illnesses
(ILI),
as
reported
Influenza
Surveillance
network
(InfluNet).
Estimated
rates
ILI
in
general
population
age
groups
0–4
years
5–14
were
obtained
influenza
seasons
2017–2018
2020–2021.
Similarly,
a
weekly
fraction
searches
series
terms
associated
with
Virus.
Next,
trends
daily
visualization
Pages
Human
Virus,
Pneumonia,
Bronchiolitis,
Influenza,
Failure
similarly
retrieved.
correlation
all
search
analyzed
Spearman’s
rank
analysis.
Among
clinical
diagnosis
infections,
highly
correlated
only
Bronchiolitis
group
(β
0.210,
p
=
0.028),
while
more
generic
terms,
such
Bronchitis,
fever,
influenza,
identified
effective
predictors
ILI,
groups.
In
regression
modeled
ILIs
outcome
variable,
visualizations
pages
on
negative
−0.152,
0.032),
−0.264,
0.001)
−0.202,
0.006),
characterized
positive
effector
0.245,
0.001).
Interestingly,
extensively
one
another,
but
them
also
autocorrelation
through
Durbin-Watson
test
(all
estimates
DW
<
2.0)
summary,
our
complicated
pattern
data
no
clear
association
between
pediatric
5
14
actually
found.
Finally,
stress
that
infodemiology
option
may
be
quite
problematic
assessing
time
trend
RSV
infections
Italy
until
appropriate
reporting
will
made
available,
sharing
Lower
Tract
Infections,
accurate
characterization
younger
Journal of Contingencies and Crisis Management,
Journal Year:
2025,
Volume and Issue:
33(2)
Published: April 6, 2025
ABSTRACT
Since
the
start
of
COVID‐19
pandemic,
infodemiological
studies
have
utilized
Google
Trends
(GT)
data
to
monitor
and
predict
changes
in
public
interest
social
behavior.
However,
question
posed
by
researchers
regarding
relation
between
online
search
media
coverage
has
remained
mostly
unanswered.
Moreover,
many
focus
their
research
mainly
on
disease
labels
symptoms.
Thus,
this
article
aims
contribute
crisis
research,
providing
a
long‐term
analysis
queries
Germany
January
2020
December
2022,
incorporating
broad
range
different
keywords
categories.
The
study
identified
strong
correlations
GT
for
categories
,
dynamics
severity
followed
moderate
characteristics
.
these
may
be
suitable
awareness,
validate
impact
assess
efficacy
health
communication
strategies.
results
symptoms
showed
no
significant
relation,
serve
as
valuable
surveilling
or
forecasting
spread
infectious
diseases.
emphasizes
significance
examining
relationship
information‐seeking
behavior
during
pandemics
other
crises.
DARU Journal of Pharmaceutical Sciences,
Journal Year:
2024,
Volume and Issue:
32(2), P. 537 - 547
Published: June 18, 2024
The
analysis
of
how
people
search
and
"navigate"
the
internet
to
obtain
health-related
information
they
communicate
share
this
can
provide
valuable
knowledge
about
disease
patterns
behaviour
health
habits
populations.
Health Data Science,
Journal Year:
2024,
Volume and Issue:
4
Published: Jan. 1, 2024
Background:
The
COVID-19
pandemic
has
posed
various
difficulties
for
policymakers,
such
as
the
identification
of
health
issues,
establishment
policy
priorities,
formulation
regulations,
and
promotion
economic
competitiveness.
Evidence-based
practices
data-driven
decision-making
have
been
recognized
valuable
tools
improving
policymaking
process.
Nevertheless,
due
to
abundance
data,
there
is
a
need
develop
sophisticated
analytical
techniques
efficiently
extract
analyze
data.
Methods:
Using
Oxford
Government
Response
Tracker,
we
categorize
responses
into
6
different
categories:
(a)
containment
closure,
(b)
systems,
(c)
vaccines,
(d)
economic,
(e)
country,
(f)
others.
We
proposed
novel
research
framework
compare
response
times
scholars
general
public.
To
achieve
this,
analyzed
more
than
400,000
abstracts
published
over
past
2.5
years,
along
with
text
information
from
Google
Trends
proxy
topics
public
concern.
introduced
an
innovative
text-mining
method:
coherent
topic
clustering
huge
number
abstracts.
Results:
Our
results
show
that
not
only
discussed
almost
all
issues
earlier
did,
but
they
also
provided
in-depth
coverage.
This
should
help
policymakers
identify
core
act
earlier.
Besides,
our
method
can
better
reflect
main
messages
recent
advanced
deep
learning-based
modeling
tool.
Conclusion:
Scholars
generally
faster
in
discussing
Trends.
