Control technology of pathogenic biological aerosol: Review and prospect
Building and Environment,
Journal Year:
2023,
Volume and Issue:
243, P. 110679 - 110679
Published: July 31, 2023
Language: Английский
Adapting COVID-19 Contact Tracing Protocols to Accommodate Resource Constraints, Philadelphia, Pennsylvania, USA, 2021
Seonghye Jeon,
No information about this author
Lydia Watson-Lewis,
No information about this author
Gabriel Rainisch
No information about this author
et al.
Emerging infectious diseases,
Journal Year:
2024,
Volume and Issue:
30(2)
Published: Jan. 5, 2024
Because
of
constrained
personnel
time,
the
Philadelphia
Department
Public
Health
(Philadelphia,
PA,
USA)
adjusted
its
COVID-19
contact
tracing
protocol
in
summer
2021
by
prioritizing
recent
cases
and
limiting
staff
time
per
case.
This
action
reduced
required
hours
to
prevent
each
case
from
21-30
8-11
hours,
while
maintaining
program
effectiveness.
Language: Английский
Use Tabu Search Particle Swarm Optimization Algorithm to Detect COVID-19
Published: Jan. 1, 2025
Language: Английский
Home-Based Testing and COVID-19 Isolation Recommendations, United States
Emerging infectious diseases,
Journal Year:
2023,
Volume and Issue:
29(9), P. 1921 - 1924
Published: Aug. 29, 2023
Using
a
nationally
representative
panel
survey,
we
examined
isolation
behaviors
among
persons
in
the
United
States
who
had
positive
SARS-CoV-2
test
results
during
January
2021-March
2022.
Compared
with
received
provider-administered
results,
home-based
29%
(95%
CI
5%-47%)
lower
odds
of
following
recommendations.
Language: Английский
Cross-national benchmarking and acceptance of pandemic mitigation policies: human value approach
Benchmarking An International Journal,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 30, 2024
Purpose
This
study
analyzes
the
reasons
for
satisfaction
or
dissatisfaction
among
people
with
public
health
mitigation
policies,
particularly
focus
on
human
values.
Recent
studies
reveal
that
citizenry
of
various
nations
reacted
to
government
policy
measures
differently
when
asked
if
they
are
satisfied
handling
COVID-19.
Human
values
such
as
openness-to-change
and
conservation
might
influence
acceptance
pandemic
measures.
Design/methodology/approach
A
structural
equation
model
(SEM)
is
proposed,
which
incorporates
strategies
value
variables.
National
survey
data
COVID-19
in
Great
Britain
Italy
used
test
several
hypotheses.
Findings
The
analysis
suggests
prioritizing
health,
monitoring
tracking
people,
border
closures
restricting
people’s
movement
played
important
roles
pandemic.
Individuals
a
high
more
likely
have
higher
probability
government’s
During
pandemic,
citizens
willing
trade
good
economy
health.
They
also
sacrifice
privacy
efforts
track
spread.
Originality/value
unique
combines
variables
policies
determining
during
national
crisis.
SEM
modeling
framework
presented
analyze
empirically
Language: Английский
Home-Based Testing and COVID-19 Isolation Recommendations, United States
Emerging infectious diseases,
Journal Year:
2023,
Volume and Issue:
29(9)
Published: Aug. 24, 2023
Abstract
Using
a
nationally
representative
panel
survey,
we
examined
isolation
behaviors
among
persons
in
the
United
States
who
had
positive
SARS-CoV-2
test
results
during
January
2021–March
2022.
Compared
with
received
provider-administered
results,
home-based
29%
(95%
CI
5%–47%)
lower
odds
of
following
recommendations.
Language: Английский
DP-UNet:Dual Branch Attention Multi-Layer Encoder and Progressive Fused Pyramid Pooling Network for Covid-19 Infection Region Segmentation
Qi Mao,
No information about this author
Wenfeng Wang,
No information about this author
Yi Tian
No information about this author
et al.
Published: Jan. 1, 2023
Computer-aided
diagnostic
imaging
plays
a
crucial
role
in
diagnosis
of
the
Corona
Virus
Disease
2019
(COVID-19)
infection.
U-Net
is
popular
COVID-19
segmentation,
but
during
encoder
pooling
and
decoder
upsampling
operations,
it
tends
to
lose
global
contextual
information,
which
leads
semantic
gap
between
encoding
decoding
stages.
To
solve
these
problems,
novel
model
using
dual
branch
attention
multi-layer
progressive
fusion
pyramid
network
(DP-UNet)
proposed
developed
this
work.
The
module
fully
utilizes
enough
information
from
input
lung
infection
images
through
extraction
operations.
Its
lateral
comprises
an
enhanced
Parallel
concurrent
spatial
channel
Squeeze
Excitation
(PscSE),
designed
for
recalibrating
attention.
At
interface
decoder,
we
propose
module.
This
multi-scale
continuous
operations
expand
utilization
by
integrating
different
scales.
It
aims
increase
ability
finely
delineate
boundaries
lesions
facilitate
integration
various-scale
details
within
infected
regions
while
minimizing
addition
parameters.
experimental
results
revealed
that
method
had
DSC
0.8459,
indicating
outperforms
other
comparative
models
on
region
segmentation.
Therefore,
has
potential
application
detection,
labeling
segmentation
lesion
areas.
Language: Английский
Predicting Long-term Covid-19 Symptoms Using Machine Learning: A Case Study in Kurdistan Region of Iraq
Aveen Kakamen Mustafa,
No information about this author
Ibrahim Ismael Hamarash
No information about this author
The Journal of The University of Duhok,
Journal Year:
2023,
Volume and Issue:
26(2), P. 605 - 612
Published: Dec. 21, 2023
The
COVID-19
pandemic
has
introduced
substantial
challenges
to
individuals,
communities,
and
healthcare
systems
worldwide.
While
initial
responses
primarily
addressed
the
acute
impact
of
virus,
emerging
evidence
highlights
a
noteworthy
portion
individuals
grappling
with
persistent
symptoms
even
after
recuperating
from
phase.
This
research
delves
into
domain
algorithms
their
application
context
COVID-19.
Specifically,
we
employ
Machine
Learning
(ML)
techniques
formulate
robust
model
for
assessing
likelihood
enduring
long-term
among
in
recovery
Our
investigation
revolves
around
comprehensive
dataset
drawn
3,500
patients
residing
Kurdistan
Region
Iraq,
all
whom
had
previously
contracted
Employing
combination
hospital
records
direct/mobile
interviews,
systematically
capture
information
pertaining
six
prevalent
symptoms.
Rigorous
preprocessing
are
then
applied
collected
data,
ensuring
standardization
mitigating
any
inherent
inconsistencies
or
biases.
To
achieve
our
objective,
harness
capabilities
TensorFlow
Keras
libraries,
leveraging
deep
learning
algorithm.
algorithm
plays
pivotal
role
predicting
probability
sustained
recovered
patients.
endeavor
demonstrates
potential
learning,
especially
when
harnessed
within
well-structured
coupled
adept
methodologies.
Consequently,
findings
underscore
viability
utilizing
as
potent
tools
forecasting
propensity
symptom
manifestation
diagnosed
Language: Английский