Frontiers in Public Health,
Journal Year:
2021,
Volume and Issue:
9
Published: Dec. 16, 2021
The
COVID-19
pandemic
has
sparked
an
intense
debate
about
the
hidden
factors
underlying
dynamics
of
outbreak.
Several
computational
models
have
been
proposed
to
inform
effective
social
and
healthcare
strategies.
Crucially,
predictive
validity
these
often
depends
upon
incorporating
behavioral
responses
infection.
Among
tools,
analytic
framework
known
as
“dynamic
causal
modeling”
(DCM)
applied
pandemic,
shedding
new
light
on
We
DCM
data
from
northern
Italian
regions,
first
areas
in
Europe
contend
with
outbreak,
analyzed
model
also
its
suitability
highlighting
governing
diffusion.
By
taking
into
account
beginning
could
faithfully
predict
outbreak
diffusion
varying
region
region.
appears
be
a
reliable
tool
investigate
mechanisms
spread
SARS-CoV-2
identify
containment
control
strategies
that
efficiently
used
counteract
further
waves
Manufacturing & Service Operations Management,
Journal Year:
2021,
Volume and Issue:
24(1), P. 1 - 23
Published: July 14, 2021
We
reviewed
research
papers
related
to
pandemics/epidemics
(disease
outbreaks
of
a
global/regional
scope)
published
in
major
operations
management,
research,
and
management
science
journals
through
the
end
2019.
evaluate
categorize
these
papers.
study
trends,
explore
gaps,
provide
directions
for
more
efficient
effective
future.
In
addition,
our
recommendations
include
lessons
learned
from
ongoing
pandemic,
COVID-19.
discuss
following
categories:
(a)
warning
signals/surveillance,
(b)
disease
propagation
leading
pandemic
conditions,
(c)
mitigation,
(d)
vaccines
therapeutics
development,
(e)
resource
(f)
supply
chain
configuration,
(g)
decision
support
systems
managing
pandemics/epidemics,
(h)
risk
assessment.
PLoS ONE,
Journal Year:
2021,
Volume and Issue:
16(3), P. e0248161 - e0248161
Published: March 11, 2021
The
first
case
of
the
novel
coronavirus
in
Brazil
was
notified
on
February
26,
2020.
After
21
days,
reported
second
largest
State
Brazilian
Amazon.
Pará
presented
difficulties
combating
pandemic,
ranging
from
underreporting
and
a
low
number
tests
to
large
territorial
distance
between
cities
with
installed
hospital
capacity.
Due
these
factors,
mathematical
data-driven
short-term
forecasting
models
can
be
promising
initiative
assist
government
officials
more
agile
reliable
actions.
This
study
presents
an
approach
based
artificial
neural
networks
for
daily
cumulative
forecasts
cases
deaths
caused
by
COVID-19,
forecast
demand
beds.
Six
scenarios
different
periods
were
used
identify
quality
generated
period
which
they
start
deteriorate.
Results
indicated
that
computational
model
adapted
capably
training
able
make
consistent
forecasts,
especially
variables
PLoS ONE,
Journal Year:
2020,
Volume and Issue:
15(11), P. e0242045 - e0242045
Published: Nov. 9, 2020
Coronavirus
Disease
2019
(COVID-19)
has
recently
become
a
public
emergency
and
worldwide
pandemic.
However,
the
information
on
risk
factors
associated
with
mortality
of
COVID-19
their
prognostic
potential
is
limited.
In
this
retrospective
study,
clinical
characteristics,
treatment
outcome
data
were
collected
analyzed
from
676
patients
stratified
into
140
non-survivors
536
survivors.
We
found
that
levels
Dimerized
plasmin
fragment
D
(D-dimer),
C-reactive
protein
(CRP),
lactate
dehydrogenase
(LDH),
procalcitonin
(PCT)
significantly
higher
in
non-survivals
admission
(non-survivors
vs.
survivors:
D-Dimer
≥
0.5
mg/L,
83.2%
44.9%,
P<
0.01;
CRP
≥10
50.4%
6.0%,
LDH
250
U/L,
73.8%
20.1%,
PCT
ng/ml,
27.7%
1.8%,
0.01).
Moreover,
dynamic
tracking
showed
D-dimer
kept
increasing
non-survivors,
while
CRP,
remained
relatively
stable
after
admission.
highest
C-index
to
predict
in-hospital
mortality,
≥0.5
mg/L
had
incidence
(Hazard
Ratio:
4.39,
P
<0.01).
Our
study
suggested
could
be
potent
marker
COVID-19,
which
may
helpful
for
management
patients.
PLoS ONE,
Journal Year:
2020,
Volume and Issue:
15(10), P. e0241163 - e0241163
Published: Oct. 23, 2020
The
events
of
the
recent
SARS-CoV-2
epidemics
have
shown
importance
social
factors,
especially
given
large
number
asymptomatic
cases
that
effectively
spread
virus,
which
can
cause
a
medical
emergency
to
very
susceptible
individuals.
Besides,
virus
survives
for
several
hours
on
different
surfaces,
where
new
host
contract
it
with
delay.
These
passive
modes
infection
transmission
remain
an
unexplored
area
traditional
mean-field
epidemic
models.
Here,
we
design
agent-based
model
simulations
in
open
system
driven
by
dynamics
activity;
takes
into
account
personal
characteristics
individuals,
as
well
survival
time
and
its
potential
mutations.
A
growing
bipartite
graph
embodies
this
biosocial
process,
consisting
active
carriers
(host)
nodes
produce
viral
during
their
infectious
period.
With
directed
edges
passing
through
between
two
successive
hosts,
contains
complete
information
about
routes
leading
each
infected
individual.
We
determine
temporal
fluctuations
exposed
viruses
at
hourly
resolution.
simulated
processes
underpin
latent
transmissions,
contributing
significantly
within
window.
More
precisely,
being
brought
currently
existing
infection,
individual
passes
state
until
eventually
spontaneously
recovers
or
otherwise
is
moves
controlled
hospital
environment.
Our
results
reveal
complex
feedback
mechanisms
shape
dependence
curve
intensity
other
sociobiological
factors.
In
particular,
show
how
lockdown
reduces
increases
again
after
removed.
Furthermore,
reduced
level
activity
but
prolonged
exposure
individuals
adverse
effects.
On
hand,
mutations
gradually
reduce
rate
hopping
along
path
extent
not
stop
spreading
without
additional
strategies.
stochastic
processes,
based
graphs
interface
biology
dynamics,
provide
mathematical
framework
various
control
strategies
high
resolution
traceability.
Results in Physics,
Journal Year:
2021,
Volume and Issue:
31, P. 104895 - 104895
Published: Oct. 25, 2021
The
COVID-19
outbreak
has
generated,
in
addition
to
the
dramatic
sanitary
consequences,
severe
psychological
repercussions
for
populations
affected
by
pandemic.
Simultaneously,
these
consequences
can
have
related
effects
on
spread
of
virus.
Pandemic
fatigue
occurs
when
stress
rises
beyond
a
threshold,
leading
person
feel
demotivated
follow
recommended
behaviours
protect
themselves
and
others.
In
present
paper,
we
introduce
new
susceptible-infected-quarantined-recovered-dead
(SIQRD)
model
terms
system
ordinary
differential
equations
(ODE).
considers
countermeasures
taken
authorities
effect
pandemic
fatigue.
latter
be
mitigated
fear
disease's
modelled
with
death
rate
mind.
mathematical
well-posedness
is
proved.
We
show
numerical
results
consistent
transmission
dynamics
data
characterising
epidemic
Italy
2020.
provide
measure
possible
impact.
used
evaluate
public
health
interventions
prevent
specific
actions
damages
resulting
from
social
phenomenon
relaxation
concerning
observance
preventive
rules
imposed.
