Chinese Physics B,
Год журнала:
2023,
Номер
33(3), С. 030205 - 030205
Опубликована: Дек. 12, 2023
We
investigate
the
impact
of
pairwise
and
group
interactions
on
spread
epidemics
through
an
activity-driven
model
based
time-dependent
networks.
The
effects
pairwise/group
interaction
proportion
intensity
are
explored
by
extensive
simulation
theoretical
analysis.
It
is
demonstrated
that
altering
can
either
hinder
or
enhance
epidemics,
depending
relative
social
interactions.
As
decreases,
reducing
diminishes.
ratio
affect
effect
scale
infection.
A
weak
heterogeneous
activity
distribution
raise
epidemic
threshold,
reduce
These
results
benefit
design
control
strategy.
Applied Mathematics and Computation,
Год журнала:
2023,
Номер
458, С. 128252 - 128252
Опубликована: Авг. 2, 2023
In
this
paper,
we
present
a
Markov
chain
model
to
study
infectious
disease
outbreaks
assuming
that
healthcare
facilities,
specifically
the
number
of
hospital
beds
for
infected
individuals,
are
limited.
Therefore,
only
restricted
individuals
can
be
admitted
ward
and
receive
medical
care
at
same
time.
Since
pathogen
spreads
both
inside
outside
ward,
modeling
dynamics
epidemic
involves
SIS-
SI-type
models
inherently
linked
each
other,
in
such
way
potential
transmission
is
possible
when
working
functionally
full.
Our
goal
influence
resource-limited
environment
on
performance
measures
related
operations,
as
time
until
reaches
its
maximum
capacity,
critical
events
—occurring
capacity—,
limited
facilities
should
continuously
active,
or
economic
impact
administering
therapeutic
treatments,
which
could
evaluated
terms
admissions
treatments
provided
case
reinfection.
Mathematics,
Год журнала:
2023,
Номер
11(24), С. 4904 - 4904
Опубликована: Дек. 8, 2023
The
mutual
influence
between
information
and
infectious
diseases
during
the
spreading
process
is
becoming
increasingly
prominent.
To
elucidate
impact
of
factors
such
as
higher-order
interactions,
interpersonal
distances,
asymptomatic
carriers
on
coupled
propagation
diseases,
a
novel
model
constructed
based
two-layer
complex
network,
where
one
layer
network
another
weighted
network.
interactions
in
are
characterized
using
2-simplex,
sUARU
(simplicial
unaware-aware-removed-unaware)
employed
to
articulate
propagation.
inter-individual
social
distances
disease
represented
by
weights
an
SEIS
(susceptible-exposed-infected-susceptible)
utilized
describe
dynamic
equations
formulated
utilizing
microscopic
Markov
chain
approach.
An
analytical
expression
for
epidemic
threshold
obtained
deriving
it
from
steady-state
form
equations.
Comprehensive
simulations
conducted
scrutinize
characteristics
model.
findings
indicate
that
enhancing
effects
increasing
both
lead
higher
outbreak
thresholds
greater
diseases.
Additionally,
stronger
infectivity
among
extended
incubation
period
favorable
spread
epidemic.
These
can
provide
meaningful
guidance
prevention
control
real-world
epidemics.
Physical Review Research,
Год журнала:
2024,
Номер
6(3)
Опубликована: Авг. 12, 2024
Human
behavior
strongly
influences
the
spread
of
infectious
diseases:
understanding
interplay
between
epidemic
dynamics
and
adaptive
behaviors
is
essential
to
improve
response
strategies
epidemics,
with
goal
containing
while
preserving
a
sufficient
level
operativeness
in
population.
Through
activity-driven
temporal
networks,
we
formulate
general
framework
which
models
wide
range
mitigation
strategies,
observed
real
populations.
We
analytically
derive
conditions
for
widespread
diffusion
epidemics
presence
arbitrary
behaviors,
highlighting
crucial
role
correlations
agents
infected
susceptible
state.
focus
on
effects
sick
leave,
comparing
effectiveness
different
reducing
impact
system
operativeness.
show
critical
relevance
heterogeneity
individual
behavior:
homogeneous
all
sick-leave
are
equivalent
poorly
effective,
heterogeneous
targeting
most
vulnerable
nodes
able
effectively
mitigate
epidemic,
also
avoiding
deterioration
activity
maintaining
low
absenteeism.
Interestingly,
targeted
both
minimum
population
maximum
absenteeism
anticipate
infection
peak,
flattened
delayed,
so
that
full
almost
restored
when
peak
arrives.
provide
realistic
estimates
model
parameters
influenza-like
illness,
thereby
suggesting
managing
Published
by
American
Physical
Society
2024
Chaos An Interdisciplinary Journal of Nonlinear Science,
Год журнала:
2024,
Номер
34(10)
Опубликована: Окт. 1, 2024
The
spread
of
misinformation
on
social
media
is
inextricably
related
to
each
user’s
forwarding
habits.
In
this
paper,
given
that
users
have
heterogeneous
probabilities
their
neighbors
with
varied
relationships
when
they
receive
misinformation,
we
present
a
novel
ignorant-spreader-refractory
(ISR)
spreading
model
rates
activity-driven
networks
various
types
links
encode
these
differential
relationships.
More
exactly,
in
model,
the
same
type
has
an
identical
rate,
while
different
distinct
ones.
Using
mean-field
approach
and
Monte
Carlo
simulations,
investigate
how
heterogeneity
affects
outbreak
threshold
final
prevalence
misinformation.
It
demonstrated
no
effect
link
follows
uniform
distribution.
However,
it
significant
impact
for
non-uniform
distributions.
For
example,
increases
normal
distribution
lowers
exponent
comparison
situation
homogeneous
whether
improves
or
decreases
also
determined
by
distributions
links.
