International Journal of Environmental Research and Public Health,
Год журнала:
2022,
Номер
19(3), С. 1504 - 1504
Опубликована: Янв. 28, 2022
With
many
countries
experiencing
a
resurgence
in
COVID-19
cases,
it
is
important
to
forecast
disease
trends
enable
effective
planning
and
implementation
of
control
measures.
This
study
aims
develop
Seasonal
Autoregressive
Integrated
Moving
Average
(SARIMA)
models
using
593
data
points
smoothened
case
covariate
time-series
generate
28-day
during
the
third
wave
Malaysia.
SARIMA
were
developed
sourced
from
Ministry
Health
Malaysia’s
official
website.
Model
training
validation
was
conducted
22
January
2020
5
September
2021
daily
data.
The
model
with
lowest
root
mean
square
error
(RMSE),
absolute
percentage
(MAE)
Bayesian
information
criterion
(BIC)
selected
forecasts
6
3
October
2021.
best
RMSE
=
73.374,
MAE
39.716
BIC
8.656
showed
downward
trend
cases
period,
wherein
observed
within
range.
majority
(89%)
difference
between
forecasted
values
well
deviation
range
25%.
Based
on
this
work,
we
conclude
that
paper
sensitive
covariates
can
accurate
trends.
Alexandria Engineering Journal,
Год журнала:
2022,
Номер
61(10), С. 7585 - 7603
Опубликована: Янв. 6, 2022
Several
machine
learning
and
deep
models
were
reported
in
the
literature
to
forecast
COVID-19
but
there
is
no
comprehensive
report
on
comparison
between
statistical
models.
The
present
work
reports
a
comparative
time-series
analysis
of
techniques
(Recurrent
Neural
Networks
with
GRU
LSTM
cells)
(ARIMA
SARIMA)
country-wise
cumulative
confirmed,
recovered,
deaths.
Gated
Recurrent
Units
(GRU),
Long
Short-Term
Memory
(LSTM)
cells
based
(RNN),
ARIMA
SARIMA
trained,
tested,
optimized
trends
COVID-19.
We
deployed
python
optimize
parameters
which
include
(p,
d,
q)
representing
autoregressive
moving
average
terms
model
additional
seasonal
are
denoted
by
(P,
D,
Q).
Similarly,
for
RNN
models'
(number
layers,
hidden
size,
rate
number
epochs)
deploying
PyTorch
framework.
best
was
chosen
lowest
Mean
Square
Error
(MSE)
Root
Squared
(RMSE)
values.
For
most
data
countries,
learning-based
outperformed
models,
an
RMSE
values
that
40
folds
less
than
But
some
countries
(ARIMA,
Further,
we
emphasize
importance
various
factors
such
as
age,
preventive
measures
healthcare
facilities
etc.
play
vital
role
rapid
spread
pandemic.
Mathematics,
Год журнала:
2023,
Номер
11(14), С. 3069 - 3069
Опубликована: Июль 12, 2023
This
comprehensive
overview
focuses
on
the
issues
presented
by
pandemic
due
to
COVID-19,
understanding
its
spread
and
wide-ranging
effects
of
government-imposed
restrictions.
The
examines
utility
autoregressive
integrated
moving
average
(ARIMA)
models,
which
are
often
overlooked
in
forecasting
perceived
limitations
handling
complex
dynamic
scenarios.
Our
work
applies
ARIMA
models
a
case
study
using
data
from
Recife,
capital
Pernambuco,
Brazil,
collected
between
March
September
2020.
research
provides
insights
into
implications
adaptability
predictive
methods
context
global
pandemic.
findings
highlight
models’
strength
generating
accurate
short-term
forecasts,
crucial
for
an
immediate
response
slow
down
disease’s
rapid
spread.
Accurate
timely
predictions
serve
as
basis
evidence-based
public
health
strategies
interventions,
greatly
assisting
management.
model
selection
involves
automated
process
optimizing
parameters
autocorrelation
partial
plots,
well
various
precise
measures.
performance
chosen
is
confirmed
when
comparing
forecasts
with
real
reported
after
forecast
period.
successfully
both
recovered
COVID-19
cases
across
preventive
plan
phases
Recife.
However,
model’s
observed
extend
future.
By
end
period,
error
substantially
increased,
it
failed
detect
stabilization
deceleration
cases.
highlights
challenges
associated
such
under-reporting
recording
delays.
Despite
these
limitations,
emphasizes
potential
while
emphasizing
need
further
enhance
long-term
predictions.
Scientific Reports,
Год журнала:
2022,
Номер
12(1)
Опубликована: Фев. 14, 2022
This
study
aims
to
develop
an
assumption-free
data-driven
model
accurately
forecast
COVID-19
spread.
Towards
this
end,
we
firstly
employed
Bayesian
optimization
tune
the
Gaussian
process
regression
(GPR)
hyperparameters
efficient
GPR-based
for
forecasting
recovered
and
confirmed
cases
in
two
highly
impacted
countries,
India
Brazil.
However,
machine
learning
models
do
not
consider
time
dependency
data
series.
Here,
dynamic
information
has
been
taken
into
account
alleviate
limitation
by
introducing
lagged
measurements
constructing
investigated
models.
Additionally,
assessed
contribution
of
incorporated
features
prediction
using
Random
Forest
algorithm.
Results
reveal
that
significant
improvement
can
be
obtained
proposed
In
addition,
results
highlighted
superior
performance
GPR
compared
other
(i.e.,
Support
vector
regression,
Boosted
trees,
Bagged
Decision
tree,
Forest,
XGBoost)
achieving
averaged
mean
absolute
percentage
error
around
0.1%.
Finally,
provided
confidence
level
predicted
based
on
showed
predictions
are
within
95%
interval.
presents
a
promising
shallow
simple
approach
predicting
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
Remote Sensing,
Год журнала:
2022,
Номер
14(21), С. 5515 - 5515
Опубликована: Ноя. 2, 2022
Floods,
one
of
the
most
common
natural
hazards
globally,
are
challenging
to
anticipate
and
estimate
accurately.
This
study
aims
demonstrate
predictive
ability
four
ensemble
algorithms
for
assessing
flood
risk.
Bagging
(BE),
logistic
model
tree
(LT),
kernel
support
vector
machine
(k-SVM),
k-nearest
neighbour
(KNN)
used
in
this
zoning
Jeddah
City,
Saudi
Arabia.
The
141
locations
have
been
identified
research
area
based
on
interpretation
aerial
photos,
historical
data,
Google
Earth,
field
surveys.
For
purpose,
14
continuous
factors
different
categorical
examine
their
effect
flooding
area.
dependency
analysis
(DA)
was
analyse
strength
predictors.
comprises
two
input
variables
combination
(C1
C2)
features
sensitivity
selection.
under-the-receiver
operating
characteristic
curve
(AUC)
root
mean
square
error
(RMSE)
were
utilised
determine
accuracy
a
good
forecast.
validation
findings
showed
that
BE-C1
performed
best
terms
precision,
accuracy,
AUC,
specificity,
as
well
lowest
(RMSE).
performance
skills
overall
models
proved
reliable
with
range
AUC
(89–97%).
can
also
be
beneficial
flash
forecasts
warning
activity
developed
by
disaster
Neural Computing and Applications,
Год журнала:
2024,
Номер
36(11), С. 6119 - 6132
Опубликована: Янв. 18, 2024
Abstract
IoT
devices
convert
billions
of
objects
into
data-generating
entities,
enabling
them
to
report
status
and
interact
with
their
surroundings.
This
data
comes
in
various
formats,
like
structured,
semi-structured,
or
unstructured.
In
addition,
it
can
be
collected
batches
real
time.
The
problem
now
is
how
benefit
from
all
this
gathered
by
sensing
monitoring
changes
temperature,
light,
position.
paper,
we
propose
a
predictive
analytics
framework
constructed
on
top
open-source
technologies
such
as
Apache
Spark
Kafka.
focuses
forecasting
temperature
time
series
using
traditional
deep
learning
methods.
analysis
prediction
tasks
were
performed
Autoregressive
Integrated
Moving
Average
(ARIMA),
Seasonal
(SARIMA),
Long
Short-Term
Memory
(LSTM),
novel
hybrid
model
based
Convolution
Neural
Network
(CNN)
LSTM.
purpose
paper
determine
whether
recently
developed
learning-based
models
outperform
algorithms
the
data.
empirical
studies
conducted
reported
demonstrate
that
models,
specifically
LSTM
CNN-LSTM,
exhibit
superior
performance
compared
traditional-based
algorithms,
ARIMA
SARIMA.
More
specifically,
average
reduction
error
rates
obtained
CNN-LSTM
substantial
when
other
indicating
superiority
learning.
Moreover,
CNN-LSTM-based
exhibits
higher
degree
closeness
actual
values
LSTM-based
model.
Osong Public Health and Research Perspectives,
Год журнала:
2024,
Номер
15(2), С. 115 - 136
Опубликована: Март 28, 2024
Objectives:
The
coronavirus
disease
2019
(COVID-19)
pandemic
continues
to
pose
significant
challenges
the
public
health
sector,
including
that
of
United
Arab
Emirates
(UAE).
objective
this
study
was
assess
efficiency
and
accuracy
various
deep-learning
models
in
forecasting
COVID-19
cases
within
UAE,
thereby
aiding
nation’s
authorities
informed
decision-making.Methods:
This
utilized
a
comprehensive
dataset
encompassing
confirmed
cases,
demographic
statistics,
socioeconomic
indicators.
Several
advanced
deep
learning
models,
long
short-term
memory
(LSTM),
bidirectional
LSTM,
convolutional
neural
network
(CNN),
CNN-LSTM,
multilayer
perceptron,
recurrent
(RNN)
were
trained
evaluated.
Bayesian
optimization
also
implemented
fine-tune
these
models.Results:
evaluation
framework
revealed
each
model
exhibited
different
levels
predictive
precision.
Specifically,
RNN
outperformed
other
architectures
even
without
optimization.
Comprehensive
perspective
analytics
conducted
scrutinize
dataset.Conclusion:
transcends
academic
boundaries
by
offering
critical
insights
enable
UAE
deploy
targeted
data-driven
interventions.
model,
which
identified
as
most
reliable
accurate
for
specific
context,
can
significantly
influence
decisions.
Moreover,
broader
implications
research
validate
capability
techniques
handling
complex
datasets,
thus
transformative
potential
healthcare
sectors.