Известия Российской академии наук Серия биологическая,
Год журнала:
2023,
Номер
8, С. 103 - 114
Опубликована: Дек. 1, 2023
The
“hot
spot
analysis”
was
applied
to
materials
from
51
Landsat
satellite
images
using
the
example
of
“Burtinskaya
Steppe”
area
Orenburgsky
Nature
Reserve,
study
dynamics
areas
positive
phytomass
anomalies
relative
a
neighborhood
with
radius
300
m.
purpose
establish
dependence
variability
increased
on
landscape
structure
and
hydrothermal
conditions.
We
concluded
that
switching
phytocenoses
in
transition
zones
steppe
meadow
type
functioning
is
ensured
by
varying
ratio
xerophytes
mesophytes
depending
fluctuations
frequency
bottoms
gullies
correlates
their
partially
forested
slopes,
which
indicates
role
forest
vegetation
stabilization
moisture
influx
into
bottoms.
In
deforested
catchment
south-facing
determined
supply
snow
moisture,
north-facing
warm-period
precipitation.
binding
factors
for
most
stable
are
convergence
landform
concavity
rather
than
area.
Heliyon,
Год журнала:
2024,
Номер
10(3), С. e25810 - e25810
Опубликована: Фев. 1, 2024
There
is
evidence
in
literature
that
the
spread
of
COVID-19
can
be
influenced
by
various
geographic
factors,
including
territorial
features,
climate,
population
density,
socioeconomic
conditions,
and
mobility.
The
objective
paper
to
provide
an
updated
review
on
geographical
studies
analysing
factors
which
spreading.
This
took
into
account
not
only
aspects
but
also
COVID-19-related
outcomes
(infections
deaths)
allowing
discern
potential
influencing
role
per
type
outcome.
A
total
112
scientific
articles
were
selected,
reviewed
categorized
according
subject
area,
aim,
country/region
study,
considered
variables,
spatial
temporal
units
analysis,
methodologies,
main
findings.
Our
showed
features
may
have
played
a
determining
uneven
geography
COVID-19;
for
instance,
certain
agreement
was
found
regarding
direct
relationship
between
urbanization
degree
infections.
For
what
concerns
climatic
temperature
variable
correlated
best
with
Together
socio-demographic
ones
extensively
taken
account.
Most
analysed
agreed
density
human
mobility
had
significant
infections
deaths.
analysis
different
approaches
used
investigate
spreading
pandemic
revealed
significance/representativeness
outputs
scale
due
great
variability
aspects.
In
fact,
more
robust
association
conducted
at
subnational
or
local
rather
than
country
scale.
Abstract
Background
Spatial
variability
of
COVID-19
cases
may
suggest
geographic
disparities
social
determinants
health.
analyses
population-level
data
provide
insight
on
factors
that
contribute
to
transmission,
hospitalization,
and
death.
Methods
Generalized
additive
models
were
used
map
risk
from
March
2020
February
2021
in
Orange
County
(OC),
California.
We
geocoded
analyzed
221,843
OC
census
tracts
within
a
Poisson
framework
while
smoothing
over
tract
centroids.
Location
was
randomly
permuted
1000
times
test
for
randomness.
also
separated
the
temporally
observe
if
changed
time.
cases,
hospitalizations,
deaths
mapped
across
adjusting
demographic
crude
adjusted
models.
Results
Risk
statistically
significant
northern
OC.
Adjustment
substantially
decreased
spatial
risk,
but
areas
remained
significant.
Inclusion
location
our
considerably
magnitude
compared
univariate
However,
percent
minority
(adjusted
RR:
1.06,
95%CI:
1.07),
average
household
size
(aRR:
1.05,
service
industry
1.04,
1.06)
significantly
associated
with
In
addition,
did
not
change
between
surges
ratios
similar
hospitalizations
deaths.
Conclusion
Significant
increased
identified
suggests
environmental
spread
communities.
Areas
north
despite
adjustment,
decreased.
Additional
investigation
how
protect
vulnerable
populations
future
infectious
disease
outbreaks.
Spatial and Spatio-temporal Epidemiology,
Год журнала:
2023,
Номер
45, С. 100579 - 100579
Опубликована: Фев. 3, 2023
This
paper
investigated
the
spatiotemporal
pattern
of
COVID-19
mortality
and
its
socioeconomic
environmental
determinants
in
first
second
wave
pandemic
England.
The
rates
for
middle
super
output
areas
from
March
2020
to
April
2021
were
used
analysis.
SaTScan
was
analysis
geographically
weighted
Poisson
regression
(GWPR)
investigate
association
with
factors.
results
show
that
there
significant
variation
hotspots
deaths
moving
regions
where
outbreak
initiated
then
spread
other
parts
country.
GWPR
revealed
age
composition,
ethnic
deprivation,
care
home
pollution
all
related
mortality.
Althoughthe
relationship
varied
over
space
these
factors
fairly
consistent
wave.
Infectious Disease Modelling,
Год журнала:
2024,
Номер
9(2), С. 387 - 396
Опубликована: Фев. 8, 2024
At
the
end
of
year
2019,
a
virus
named
SARS-CoV-2
induced
coronavirus
disease,
which
is
very
contagious
and
quickly
spread
around
world.
This
new
infectious
disease
called
COVID-19.
Numerous
areas,
such
as
economy,
social
services,
education,
healthcare
system,
have
suffered
grave
consequences
from
invasion
this
deadly
virus.
Thus,
thorough
understanding
COVID-19
required
in
order
to
deal
with
outbreak
before
it
becomes
an
disaster.
In
research,
daily
reported
cases
92
sub-districts
Johor
state,
Malaysia,
well
population
size
associated
each
sub-district,
are
used
study
propagation
across
space
time
Johor.
The
frame
research
about
190
days,
started
August
5,
2021,
until
February
10,
2022.
clustering
technique
known
spatio-temporal
clustering,
considers
metric
was
adapted
determine
hot-spot
areas
at
sub-district
level.
results
indicated
that
does
spike
dynamic
populated
state's
economic
centre
(Bandar
Bahru),
during
festive
season.
These
findings
empirically
prove
transmission
rate
directly
proportional
human
mobility
presence
holidays.
On
other
hand,
result
will
help
authority
charge
stopping
preventing
spreading
become
worsen
national
Frontiers in Public Health,
Год журнала:
2023,
Номер
11
Опубликована: Апрель 13, 2023
The
COVID-19
pandemic
represents
a
worldwide
threat
to
health.
Since
its
onset
in
2019,
the
has
proceeded
different
phases,
which
have
been
shaped
by
complex
set
of
influencing
factors,
including
public
health
and
social
measures,
emergence
new
virus
variants,
seasonality.
Understanding
development
incidence
spatiotemporal
patterns
at
neighborhood
level
is
crucial
for
local
authorities
identify
high-risk
areas
develop
tailored
mitigation
strategies.
However,
analyses
are
scarce
mostly
limited
specific
phases
pandemic.
aim
this
study
was
explore
scale
an
intra-urban
setting
over
several
(March
2020–December
2021).
We
used
reported
case
data
from
department
district
Berlin-Neukölln,
Germany,
additional
socio-demographic
data,
text
documents
materials
on
implemented
measures.
examined
time
context
measures
other
with
particular
focus
age
groups.
maps
spatial
scan
statistics
reveal
changing
patterns.
Our
results
show
that
factors
may
influenced
incidence.
In
particular,
far-reaching
contact
reduction
showed
substantial
impact
Neukölln.
observed
group-specific
effects:
school
closures
had
effect
younger
population
(<
18
years),
whereas
start
vaccination
campaign
primarily
among
elderly
(>
65
years).
analysis
revealed
were
heterogeneously
distributed
across
district.
location
also
changed
phases.
study,
existing
studies
supplemented
our
investigation
course
underlying
processes
small
long
period
time.
findings
provide
insights
authorities,
community
planners,
policymakers
about
level.
These
guiding
decision-makers
implementing
PLoS ONE,
Год журнала:
2024,
Номер
19(2), С. e0297772 - e0297772
Опубликована: Фев. 1, 2024
During
the
SARS-CoV-2
pandemic,
governments
and
public
health
authorities
collected
massive
amounts
of
data
on
daily
confirmed
positive
cases
incidence
rates.
These
sets
provide
relevant
information
to
develop
a
scientific
understanding
pandemic’s
spatiotemporal
dynamics.
At
same
time,
there
is
lack
comprehensive
approaches
describe
classify
patterns
underlying
dynamics
COVID-19
across
regions
over
time.
This
seriously
constrains
potential
benefits
for
understand
disease
that
would
allow
better
risk
communication
strategies
improved
assessment
mitigation
policies
efficacy.
Within
this
context,
we
propose
an
exploratory
statistical
tool
combines
functional
analysis
with
unsupervised
learning
algorithms
extract
meaningful
about
main
mainland
Portugal.
We
focus
timeframe
spanning
from
August
2020
March
2022,
considering
at
municipality
level.
First,
temporal
evolution
by
as
function
outline
variability
using
principal
component
analysis.
Then,
municipalities
are
classified
according
their
similarities
through
hierarchical
clustering
adapted
spatially
correlated
data.
Our
findings
reveal
disparities
in
between
northern
coastal
versus
those
southern
hinterland.
also
distinguish
effects
occurring
during
2020–2021
period
2021–2022
autumn-winter
seasons.
The
results
proof-of-concept
proposed
approach
can
be
used
detect
incidence.
novel
expands
enhances
existing
tools
International Journal of Geoinformatics,
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 31, 2024
The
aim
of
this
study
is
to
identify
hotspot
regions
COVID-19
in
India
from
March
2020
August
2023.
Identifying
hotspots
essential
for
effective
pandemic
management,
as
it
helps
policymakers
understand
the
dynamics
virus
spread
and
allows
more
precise
public
health
campaigns.
present
a
district
level
analysis
at
five
different
points
time,
where
we
calculate
cumulative
incidence
rate
(CIR),
fatality
(CFR)
recovery
(RR)
COVID-19.
Further,
apply
Global
Moran's
I,
Getis-Ord
Gi*
Anselin
local
I
index
by
using
Geographic
Information
Systems
(GIS)
technology.
results
show
that
spatial
temporal
variation
CIR
very
high
across
India.
was
recorded
lower
May
affected
people
were
immobilized
due
lockdown.
However,
CFR
RR
low
inadequate
medical
facilities
treatment.
findings
revealed
mainly
two
existed
until
2021,
National
Capital
Region,
Haryana,
Punjab,
Rajasthan,
Uttar
Pradesh
Maharashtra
south.
scenario
has
entirely
changed
since
January
2022,
when
northern
into
cold-spot
southern
coastal
states
have
become
hot-spot
region.
Combining
with
&
offers
method
locating
statistically
significant
case
cluster
areas
identifying
high-risk
areas.
Scientific Reports,
Год журнала:
2023,
Номер
13(1)
Опубликована: Авг. 31, 2023
Abstract
Low
socio-economic
status
is
associated
with
higher
SARS-CoV-2
incidences.
In
this
paper
we
study
whether
a
result
of
differences
in
(1)
the
frequency,
(2)
intensity,
and/or
(3)
duration
local
outbreaks
depending
on
housing
situations.
So
far,
there
not
clear
evidence
which
three
factors
dominates.
Using
small-scale
data
from
neighborhoods
German
city
Essen
and
flexible
estimation
approach
does
require
prior
knowledge
about
specific
transmission
characteristics
SARS-CoV-2,
behavioral
responses
or
other
potential
model
parameters,
find
for
last
hypotheses.
Outbreaks
do
happen
more
often
less
well-off
areas
are
severe
(in
terms
number
cases),
but
they
longer.
This
indicates
that
gradient
infection
levels
at
least
parts
sustained
spread
infections
worse
conditions
after
suggests
case
an
epidemic
allocating
scarce
resources
containment
measures
to
poor
might
have
greatest
benefit.