International Journal of Production Research,
Год журнала:
2021,
Номер
61(24), С. 8367 - 8383
Опубликована: Июнь 14, 2021
Since
the
beginning
of
COVID-19,
more
than
13,036,550
people
have
been
infected,
and
571,574
died
because
disease
by
July
13,
2020.
Developing
new
methodologies
to
predict
COVID-19
pandemic
will
help
policymakers
plan
contain
spread
virus.
In
this
research,
we
develop
a
Stochastic
Fractal
Search
algorithm
combined
with
mathematical
model
forecast
pandemic.
To
enhance
algorithm,
employed
design
experiments
approach
for
tuning.
We
applied
our
public
datasets
in
Canada
upcoming
months.
Our
predicts
number
symptomatic,
asymptomatic,
life-threatening,
recovered,
death
cases.
The
outcomes
reveal
that
asymptomatic
cases
play
main
role
transmission
also
show
increasing
testing
capacity
would
detection
limit
community
transmission.
Moreover,
performed
sensitivity
analyses
discover
effects
changes
rates
on
growth.
provide
realistic
overview
future
if
change
due
emergence
variants
or
social
measures.
Considering
outcomes,
several
managerial
insights
minimize
Informatics in Medicine Unlocked,
Год журнала:
2020,
Номер
20, С. 100420 - 100420
Опубликована: Янв. 1, 2020
Epidemiological
models
have
been
used
extensively
to
predict
disease
spread
in
large
populations.
Among
these
models,
Susceptible
Infectious
Exposed
Recovered
(SEIR)
is
considered
be
a
suitable
model
for
COVID-19
predictions.
However,
SEIR
its
classical
form
unable
quantify
the
impact
of
lockdowns.
In
this
work,
we
introduce
variable
system
equations
study
various
degrees
social
distancing
on
disease.
As
case
study,
apply
our
modified
initial
data
available
(till
April
9,
2020)
Kingdom
Saudi
Arabia
(KSA).
Our
analysis
shows
that
with
no
lockdown
around
2.1
million
people
might
get
infected
during
peak
2
months
from
date
was
first
enforced
KSA
(March
25th).
On
other
hand,
Kingdom's
current
strategy
partial
lockdowns,
predicted
number
infections
can
lowered
0.4
by
September
2020.
We
further
demonstrate
stricter
level
curve
effectively
flattened
KSA.
IEEE Internet of Things Journal,
Год журнала:
2021,
Номер
8(21), С. 15906 - 15918
Опубликована: Март 17, 2021
The
rapid
geographic
spread
of
COVID-19,
to
which
various
factors
may
have
contributed,
has
caused
a
global
health
crisis.
Recently,
the
analysis
and
forecast
COVID-19
pandemic
attracted
worldwide
attention.
In
this
work,
large
data
set
consisting
pandemic,
testing
capacity,
economic
level,
demographic
information,
location
in
184
countries
1241
areas
from
December
18,
2019,
September
30,
2020,
were
developed
public
reports
released
by
national
authorities
bureau
statistics.
We
proposed
machine
learning
model
for
prediction
based
on
broad
system
(BLS).
Here,
we
leveraged
random
forest
(RF)
screen
out
key
features.
Then,
combine
bagging
strategy
BLS
develop
random-forest-bagging
(RF-Bagging-BLS)
approach
trend
pandemic.
addition,
compared
forecasting
results
with
linear
regression
(LR)
model,
[Formula:
see
text]-nearest
neighbors
(KNN),
decision
tree
(DT),
adaptive
boosting
(Ada),
RF,
gradient
DT
(GBDT),
support
vector
(SVR),
extra
trees
(ETs)
regressor,
CatBoost
(CAT),
LightGBM
(LGB),
XGBoost
(XGB),
BLS.The
RF-Bagging
showed
better
performance
terms
relative
mean-square
error
(RMSE),
coefficient
determination
([Formula:
text]),
adjusted
median
absolute
(MAD),
mean
percentage
(MAPE)
than
other
models.
Hence,
demonstrates
superior
predictive
power
over
benchmark
Heliyon,
Год журнала:
2021,
Номер
7(10), С. e08143 - e08143
Опубликована: Окт. 1, 2021
COVID-19
has
produced
a
global
pandemic
affecting
all
over
of
the
world.
Prediction
rate
spread
and
modeling
its
course
have
critical
impact
on
both
health
system
policy
makers.
Indeed,
making
depends
judgments
formed
by
prediction
models
to
propose
new
strategies
measure
efficiency
imposed
policies.
Based
nonlinear
complex
nature
this
disorder
difficulties
in
estimation
virus
transmission
features
using
traditional
epidemic
models,
artificial
intelligence
methods
been
applied
for
spread.
importance
machine
deep
learning
approaches
spreading
trend,
present
study,
we
review
studies
which
used
these
predict
number
cases
COVID-19.
Adaptive
neuro-fuzzy
inference
system,
long
short-term
memory,
recurrent
neural
network
multilayer
perceptron
are
among
mostly
regard.
We
compared
performance
several
Root
means
squared
error
(RMSE),
mean
absolute
(MAE),
R
IEEE Transactions on Artificial Intelligence,
Год журнала:
2022,
Номер
4(1), С. 44 - 59
Опубликована: Янв. 11, 2022
The
purpose
of
this
article
is
to
see
how
machine
learning
(ML)
algorithms
and
applications
are
used
in
the
COVID-19
inquiry
for
other
purposes.
available
traditional
methods
international
epidemic
prediction,
researchers
authorities
have
given
more
attention
simple
statistical
epidemiological
methodologies.
inadequacy
absence
medical
testing
diagnosing
identifying
a
solution
one
key
challenges
preventing
spread
COVID-19.
A
few
statistical-based
improvements
being
strengthened
answer
challenge,
resulting
partial
resolution
up
certain
level.
ML
advocated
wide
range
intelligence-based
approaches,
frameworks,
equipment
cope
with
issues
industry.
application
inventive
structure,
such
as
handling
relevant
outbreak
difficulties,
has
been
investigated
article.
major
goal
1)
Examining
impact
data
type
nature,
well
obstacles
processing
2)
Better
grasp
importance
intelligent
approaches
like
pandemic.
3)
development
improved
types
prognosis.
4)
effectiveness
influence
various
strategies
5)
To
target
on
potential
diagnosis
order
motivate
academics
innovate
expand
their
knowledge
research
into
additional
COVID-19-affected
industries.
Journal of Experimental & Theoretical Artificial Intelligence,
Год журнала:
2022,
Номер
35(4), С. 507 - 534
Опубликована: Фев. 9, 2022
The
proportion
of
COVID-19
patients
is
significantly
expanding
around
the
world.
Treatment
with
serious
consideration
has
become
a
significant
problem.
Identifying
clinical
indicators
succession
towards
severe
conditions
desperately
required
to
empower
hazard
stratification
and
optimise
resource
allocation
in
pandemic
COVID-19.
Consequently,
classification
severity
level
for
patient's
triaging.
It
categorise
as
mild,
moderate,
severe,
critical
based
on
patients'
symptoms.
Various
symptomatic
parameters
may
encourage
evaluation
infection
seriousness.
Likewise,
rapid
spread
transmissibility
patients,
it
crucial
utilise
telemonitoring
schemes
patients.
Telemonitoring
mediation
encourages
remote
data
information
exchange
among
medicinal
services,
suppliers,
furthermore,
risk
mitigation
provision
appropriate
medical
facilities.
This
paper
provides
explorative
analysis
symptoms,
comorbidities,
other
parameters,
comparing
different
machine
learning
algorithms
case
detection.
also
system
(based
degree
truthfulness)
detection
that
might
be
utilised
stratify
levels
anticipated
moderate
Finally,
we
provide
model
ensure
continuous
monitoring
progression
strategies.
Oeconomia Copernicana,
Год журнала:
2024,
Номер
15(1), С. 27 - 58
Опубликована: Март 30, 2024
Research
background:
Deep
and
machine
learning-based
algorithms
can
assist
in
COVID-19
image-based
medical
diagnosis
symptom
tracing,
optimize
intensive
care
unit
admission,
use
clinical
data
to
determine
patient
prioritization
mortality
risk,
being
pivotal
qualitative
provision,
reducing
errors,
increasing
survival
rates,
thus
diminishing
the
massive
healthcare
system
burden
relation
severe
inpatient
stay
duration,
while
operational
costs
throughout
organizational
management
of
hospitals.
Data-driven
financial
scenario-based
contingency
planning,
predictive
modelling
tools,
risk
pooling
mechanisms
should
be
deployed
for
additional
equipment
unforeseen
demand
expenses.
Purpose
article:
We
show
that
deep
decision
making
systems
likelihood
treatment
outcomes
with
regard
susceptible,
infected,
recovered
individuals,
performing
accurate
analyses
by
modeling
based
on
vital
signs,
surveillance
data,
infection-related
biomarkers,
furthering
hospital
facility
optimization
terms
bed
allocation.
Methods:
The
review
software
employed
article
screening
quality
evaluation
were:
AMSTAR,
AXIS,
DistillerSR,
Eppi-Reviewer,
MMAT,
PICO
Portal,
Rayyan,
ROBIS,
SRDR.
Findings
&
value
added:
support
tools
forecast
spread,
confirmed
cases,
infection
rates
data-driven
appropriate
resource
allocations
effective
therapeutic
protocol
development,
determining
suitable
measures
regulations
using
symptoms
comorbidities,
laboratory
records
across
units,
impacting
financing
infrastructure.
As
a
result
heightened
personal
protective
equipment,
pharmacy
medication,
outpatient
treatment,
supplies,
revenue
loss
vulnerability
occur,
also
due
expenses
related
hiring
staff
critical
expenditures.
Hospital
care,
screening,
capacity
expansion,
lead
further
losses
affecting
frontline
workers
patients.
Decision Analytics Journal,
Год журнала:
2021,
Номер
1, С. 100007 - 100007
Опубликована: Окт. 30, 2021
The
COVID-19
pandemic
spread
rapidly
around
the
world
and
is
currently
one
of
most
leading
causes
death
heath
disaster
in
world.
Turkey,
like
countries,
has
been
negatively
affected
by
COVID-19.
aim
this
study
to
design
a
predictive
model
based
on
artificial
neural
network
(ANN)
predict
future
number
daily
cases
deaths
caused
generalized
way
fit
different
countries'
spreads.
In
study,
we
used
dataset
between
11
March
2020
23
January
2021
for
countries.
This
provides
an
ANN
assist
government
take
preventive
action
hospitals
medical
facilities.
results
show
that
there
86%
overall
accuracy
predicting
mortality
rate
87%
cases.
IEEE Access,
Год журнала:
2021,
Номер
10, С. 35094 - 35105
Опубликована: Май 5, 2021
In
the
current
era,
data
is
growing
exponentially
due
to
advancements
in
smart
devices.
Data
scientists
apply
a
variety
of
learning-based
techniques
identify
underlying
patterns
medical
address
various
health-related
issues.
this
context,
automated
disease
detection
has
now
become
central
concern
science.
Such
approaches
can
reduce
mortality
rate
through
accurate
and
timely
diagnosis.
COVID-19
modern
virus
that
spread
all
over
world
affecting
millions
people.
Many
countries
are
facing
shortage
testing
kits,
vaccines,
other
resources
significant
rapid
growth
cases.
order
accelerate
process,
around
have
sought
create
novel
methods
for
virus.
paper,
we
propose
hybrid
deep
learning
model
based
on
convolutional
neural
network
(CNN)
gated
recurrent
unit
(GRU)
detect
viral
from
chest
X-rays
(CXRs).
proposed
model,
CNN
used
extract
features,
GRU
as
classifier.
The
been
trained
424
CXR
images
with
3
classes
(COVID-19,
Pneumonia,
Normal).
achieves
encouraging
results
0.96,
0.95
terms
precision,
recall,
f1-score,
respectively.
These
findings
indicate
how
significantly
contribute
early
patients
analysis
X-ray
scans.
indications
pave
way
mitigate
impact
disease.
We
believe
be
an
effective
tool
practitioners
Journal of Control Science and Engineering,
Год журнала:
2021,
Номер
2021, С. 1 - 23
Опубликована: Июль 30, 2021
COVID-19
has
sparked
a
worldwide
pandemic,
with
the
number
of
infected
cases
and
deaths
rising
on
regular
basis.
Along
recent
advances
in
soft
computing
technology,
researchers
are
now
actively
developing
enhancing
different
mathematical
machine-learning
algorithms
to
forecast
future
trend
this
pandemic.
Thus,
if
we
can
accurately
globally,
spread
pandemic
be
controlled.
In
study,
hybrid
CNN-LSTM
model
was
developed
time-series
dataset
confirmed
COVID-19.
The
proposed
evaluated
compared
17
baseline
models
test
data.
primary
finding
research
is
that
outperformed
them
all,
lowest
average
MAPE,
RMSE,
RRMSE
values
both
Conclusively,
our
experimental
results
show
that,
while
standalone
CNN
LSTM
provide
acceptable
efficient
forecasting
performance
for
time
series,
combining
encoder-decoder
structure
provides
significant
boost
performance.
Furthermore,
demonstrated
suggested
produced
satisfactory
predicting
even
small
amount