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Background: Our understanding of climate factors and their links to the Middle East Respiratory Syndrome Coronavirus (MERS-CoV) outbreaks is incomplete. This study aimed to estimate the monthly incidence of MERS-CoV cases and to investigate their correlation to climate factors. Methods: The study used aggregated monthly MERS-CoV cases that reported to the Saudi Center for Disease Prevention and Control from the Riyadh Region between November 1, 2012 and December 31, 2018. Data on the meteorological situation throughout the study period was calculated based on Google reports on the Riyadh Region (24.7136°N, 46.6753°E). The Poisson regression was used to estimate the incidence rate ratio (IRR) and its 95% confidence intervals (CI) for each climate factor. Results: A total of 712 MERS-CoV cases were included in the analysis (mean age 54.2±9.9 years), and more than half (404) (56.1%) MERS-CoV cases were diagnosed during a five-month period from April to August. The highest peak timing positioned in August 2015, followed by April 2014, June 2017, March 2015, and June 2016. High temperatures (IRR=1.054, 95% CI: 1.043-1.065) and a high ultraviolet index (IRR=1.401, 95% CI: 1.331-1.475) were correlated with a higher incidence of MERS-CoV cases. However, low relative humidity (IRR=0.956, 95% CI: 0.948-0.964) and low wind speed (IRR=0.945, 95% CI: 0.912-0.979) were correlated with a lower incidence of MERS-CoV cases. Conclusion: The novel coronavirus, MERS-CoV, is influenced by climate conditions with increasing incidence between April and August. High temperature, high ultraviolet index, low wind speed, and low relative humidity are contributors to increased MERS-CoV cases. The climate factors must be evaluated in hospitals and community settings and integrated into guidelines to serve as source of control measures to prevent and eliminate the risk of infection.
Content may be subject to copyright.
Please
cite
this
article
in
press
as:
Altamimi
A,
Ahmed
AE.
Climate
factors
and
incidence
of
Middle
East
respiratory
syndrome
coronavirus.
J
Infect
Public
Health
(2019),
https://doi.org/10.1016/j.jiph.2019.11.011
ARTICLE IN PRESS
G Model
JIPH-1216;
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of
Pages
5
Journal
of
Infection
and
Public
Health
xxx
(2019)
xxx–xxx
Contents
lists
available
at
ScienceDirect
Journal
of
Infection
and
Public
Health
journa
l
h
om
epa
ge:
http://www.elsevier.com/lo
cate/jiph
Climate
factors
and
incidence
of
Middle
East
respiratory
syndrome
coronavirus
Asmaa
Altamimia,
Anwar
E.
Ahmedb,
aTropical
Diseases
Center,
National
Health
Laboratory,
Saudi
Center
for
Disease
Prevention
and
Control
(Saudi
CDC),
Riyadh,
Saudi
Arabia
bUniformed
Services
University
of
the
Health
Sciences,
F.
Edward
Hébert
School
of
Medicine,
Department
of
Preventive
Medicine
&
Biostatistics/Henry
M
Jackson
Foundation
for
the
Advancement
of
Military
Medicine,
Bethesda,
MD,
USA
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
26
August
2019
Received
in
revised
form
17
October
2019
Accepted
10
November
2019
Keywords:
Weather
conditions
Meteorological
factors
MERS-CoV
a
b
s
t
r
a
c
t
Background:
Our
understanding
of
climate
factors
and
their
links
to
the
Middle
East
Respiratory
Syndrome
Coronavirus
(MERS-CoV)
outbreaks
is
incomplete.
This
study
aimed
to
estimate
the
monthly
incidence
of
MERS-CoV
cases
and
to
investigate
their
correlation
to
climate
factors.
Methods:
The
study
used
aggregated
monthly
MERS-CoV
cases
that
reported
to
the
Saudi
Center
for
Disease
Prevention
and
Control
from
the
Riyadh
Region
between
November
1,
2012
and
December
31,
2018.
Data
on
the
meteorological
situation
throughout
the
study
period
was
calculated
based
on
Google
reports
on
the
Riyadh
Region
(24.7136 N,
46.6753 E).
The
Poisson
regression
was
used
to
estimate
the
incidence
rate
ratio
(IRR)
and
its
95%
confidence
intervals
(CI)
for
each
climate
factor.
Results:
A
total
of
712
MERS-CoV
cases
were
included
in
the
analysis
(mean
age
54.2
±
9.9
years),
and
more
than
half
(404)
(56.1%)
MERS-CoV
cases
were
diagnosed
during
a
five-month
period
from
April
to
August.
The
highest
peak
timing
positioned
in
August
2015,
followed
by
April
2014,
June
2017,
March
2015,
and
June
2016.
High
temperatures
(IRR
=
1.054,
95%
CI:
1.043–1.065)
and
a
high
ultraviolet
index
(IRR
=
1.401,
95%
CI:
1.331–1.475)
were
correlated
with
a
higher
incidence
of
MERS-CoV
cases.
However,
low
relative
humidity
(IRR
=
0.956,
95%
CI:
0.948–0.964)
and
low
wind
speed
(IRR
=
0.945,
95%
CI:
0.912–0.979)
were
correlated
with
a
lower
incidence
of
MERS-CoV
cases.
Conclusion:
The
novel
coronavirus,
MERS-CoV,
is
influenced
by
climate
conditions
with
increasing
inci-
dence
between
April
and
August.
High
temperature,
high
ultraviolet
index,
low
wind
speed,
and
low
relative
humidity
are
contributors
to
increased
MERS-CoV
cases.
The
climate
factors
must
be
evaluated
in
hospitals
and
community
settings
and
integrated
into
guidelines
to
serve
as
source
of
control
measures
to
prevent
and
eliminate
the
risk
of
infection.
©
2019
Published
by
Elsevier
Ltd
on
behalf
of
King
Saud
Bin
Abdulaziz
University
for
Health
Sciences.
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Introduction
The
most
recent
human
coronavirus
(CoV),
Middle
East
Respi-
ratory
Syndrome
(MERS)
or
MERS-CoV
[1],
continues
circulating
and
impacting
healthcare
systems
in
Saudi
Arabia
[2].
Despite
the
virus
having
been
linked
to
high
mortality
[3,4],
its
transmission
mechanisms
remain
inadequately
understood
[5].
As
of
June
30,
2019,
there
have
been
a
number
of
recurrent
outbreaks
reported,
in
which
there
were
2449
laboratory-confirmed
MERS-CoV
cases,
including
845
deaths
[1,3].
The
incidence
rate
of
MERS-CoV
in
Saudi
Arabia
is
the
highest
compared
to
any
other
infected
country
[6].
Another
study
reported
Corresponding
author
at:
Uniformed
Services
University
of
the
Health
Sciences,
4301
Jones
Bridge
Rd,
Bethesda,
MD
20814,
USA.
E-mail
address:
anwar.ahmed.ctr@usuhs.edu
(A.E.
Ahmed).
that
seasonal
variation
of
respiratory
viruses
was
inversely
asso-
ciated
with
temperature
and
relative
humidity
among
children
during
MERS-endemic
to
the
Riyadh
Region
[7].
To
date,
limited
studies
investigate
climate
parameters
as
fac-
tors
that
could
promote
the
MERS
virus
activity
and
transmission.
Although
MERS-CoV
cases
were
reported
throughout
the
year
in
Saudi
Arabia,
a
seasonality
pattern
appears
to
be
higher
during
cer-
tain
months
of
the
year
[8].
Similar
seasonality
may
exist
in
the
Riyadh
Region
as
well.
Gardner
et
al.
have
recently
reported
an
association
between
climate
factors
and
the
appearance
of
virus
in
primary
MERS-CoV
cases
[9].
The
findings
of
this
study
may
represent
primary
MERS-
CoV
cases.
In
addition,
Gardner
categorized
climate
variables
into
groups:
tertiles
and
halves.
However,
our
study
tends
to
report
the
correlation
between
climate
variables
(as
quantitative)
and
the
occurrence
of
all
laboratory-confirmed
MERS-CoV
cases.
The
study
also
assesses
the
effects
of
five
climate
factors:
temperature,
rela-
https://doi.org/10.1016/j.jiph.2019.11.011
1876-0341/©
2019
Published
by
Elsevier
Ltd
on
behalf
of
King
Saud
Bin
Abdulaziz
University
for
Health
Sciences.
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please
cite
this
article
in
press
as:
Altamimi
A,
Ahmed
AE.
Climate
factors
and
incidence
of
Middle
East
respiratory
syndrome
coronavirus.
J
Infect
Public
Health
(2019),
https://doi.org/10.1016/j.jiph.2019.11.011
ARTICLE IN PRESS
G Model
JIPH-1216;
No.
of
Pages
5
2
A.
Altamimi,
A.E.
Ahmed
/
Journal
of
Infection
and
Public
Health
xxx
(2019)
xxx–xxx
Table
1
Monthly
climate
factors
and
MERS-CoV
cases
in
Riyadh
Region,
November
1,
2012–December
31,
2018.
Monthly
MERS-CoV
frequency
Temperature C
Humidity
%
Ultraviolet
index
Cloud
Winds
Mean
11.30
26.46
22.70
6.76
8.65
13.117
Std.
Deviation
20.305
7.666
10.776
1.563
5.984
2.0756
Minimum
1
14
9
4
0
7.6
Maximum
128
38
48
9
27
17.1
Median
4.00
27.00
22.00
7.00
7.00
13.200
25th Percentiles
3.00
20.00
13.00
5.00
4.00
11.700
75th Percentiles 9.00
34.00
30.00
8.00
12.00
14.800
tive
humidity,
ultraviolet
index,
cloud,
and
wind
speed,
which
have
been
reported
to
be
mostly
associated
with
viral
activity
in
primary
cases
[9].
The
monthly
variations
in
MERS-CoV
frequencies
[10]
should
be
further
investigated
by
exploring
the
role
of
climate
factors
on
the
incidence
of
MERS-CoV
infection.
This
may
provide
better
under-
standing
and
the
ability
to
prepare
and
implement
an
efficient
health
information
system
to
reduce
the
incidence
of
MERS-CoV
infection
in
Saudi
Arabia.
Therefore,
we
aimed
to
estimate
the
monthly
incidence
and
to
investigate
the
effects
of
climate
factors
on
the
monthly
frequency
of
MERS-CoV
infections
reported
from
Riyadh,
Saudi
Arabia.
It
was
hypothesized
that
the
monthly
aver-
age
of
temperature,
ultraviolet
index,
relative
humidity,
cloud,
and
wind
speed
are
important
factors
of
the
MERS-CoV
occurrence
in
Riyadh,
Saudi
Arabia.
Methods
The
study
utilized
a
retrospective
design
to
study
a
cohort
of
MERS
patients
reported
in
Riyadh,
Saudi
Arabia.
The
study
site
is
Riyadh,
which
is
the
capital
city
of
Saudi
Arabia,
located
in
the
cen-
ter,
and
with
a
population
of
over
5
million.
We
chose
to
study
cases
reported
from
the
Riyadh
Region
because
this
region
has
a
more
substantial
number
of
cases
than
any
other
regions
in
the
country.
Also
investigating
one
location
may
provide
a
more
precise
estimate
of
the
climate
factors
where
the
cases
are
reported.
The
Ethics
Committee
at
the
Saudi
Ministry
of
Health
has
granted
approval
to
conduct
the
study,
IRB
log
number:18-138E.
The
study
has
been
exempted
from
patient
and
publication
con-
sent
due
its
design.
Data
were
obtained
from
Saudi
Center
for
Disease
Prevention
and
Control,
Riyadh,
Saudi
Arabia.
All
hospitals
in
Riyadh
and
other
regions
are
required
to
report
laboratory-
confirmed
MERS-CoV
cases
to
the
Saudi
Ministry
of
Health
through
this
center.
The
study
included
all
laboratory-confirmed
MERS-CoV
cases
reported
from
the
Riyadh
Region
between
November
1,
2012
and
December
31,
2018.
The
study
aggregated
daily
and
weekly
inci-
dence
of
MERS-CoV
cases
and
patients’
data
into
monthly
reports.
Data
on
the
climate
situation
throughout
the
study
period,
based
on
Google
reports,
were
calculated
using
the
monthly
average
of
temperature C,
relative
humidity
(%),
ultraviolet
index,
cloud,
and
wind
speed
in
Riyadh
(24.7136 N,
46.6753 E).
Statistical
analysis
The
statistical
analysis
in
this
study
was
performed
using
STATA
V
15
(STATA
Corp.,
Texas,
USA).
The
monthly
incidence
of
MERS-
CoV
cases
was
treated
as
a
discrete
random
variable,
where
MERS-
CoV
cases
were
counted
over
each
month
from
November
2012
to
December
2018.
Climate
factors
and
the
number
of
MERS-CoV
cases
were
summarized
by
descriptive
statistics
(Table
1).
The
Poisson
model
is
commonly
used
to
model
discrete
random
variables,
and
the
Poisson
regression
was
used
to
estimate
the
monthly
incidence
Table
2
The
effects
of
months
and
years
on
frequency
of
MERS-CoV
cases,
November
1,
2012–December
31,
2018.
Month
B
SE
z
P
IRR
95%
CI
for
IRR
LCL
UCL
2
0.271
0.224
1.210
0.227
1.311
0.845
2.035
3
0.307
0.230
1.340
0.181
1.360
0.866
2.135
4
0.352
0.221
1.590
0.111
1.422
0.922
2.194
5
1.110
0.211
5.270
0.001a3.033
2.008
4.583
6
0.887
0.211
4.210
0.001a2.427
1.606
3.666
7
0.862
0.293
2.940
0.003a0.422
0.238
0.750
8
1.821
0.201
9.050
0.001a6.178
4.164
9.166
9
0.427
0.219
1.950
0.051
1.533
0.999
2.354
10
0.093
0.240
0.390
0.698
0.911
0.569
1.459
11
0.560
0.258
2.170
0.030a0.571
0.344
0.948
12
0.292
0.263
1.110
0.266
0.747
0.446
1.250
Year
2013
0.616
0.594
1.040
0.300
1.852
0.578
5.937
2014
1.648
0.582
2.830
0.005a5.194
1.660
16.252
2015
2.156
0.580
3.710
0.001a8.636
2.769
26.937
2016
0.726
0.591
1.230
0.219
2.067
0.649
6.584
2017
1.006
0.588
1.710
0.087
2.733
0.864
8.651
2018
0.360
0.597
0.600
0.547
1.433
0.445
4.620
aSignificant
at
˛
=
0.05.
Table
3
The
effects
of
climate
factors
on
the
frequency
of
MERS-CoV,
November
1,
2012–December
31,
2018.
B
SE
z
P
IRR
95%
CI
for
IRR
LCL
UCL
Temperature C
0.052
0.005
9.790
0.001a1.054
1.043
1.065
Humidity
%
0.045
0.004
10.850
0.001a0.956
0.948
0.964
Ultraviolet
index
0.337
0.026
12.890
0.001a1.401
1.331
1.475
Cloud
0.012
0.006
1.880
0.06
0.988
0.975
1.000
Wind
speed
0.057
0.018
3.130
0.002a0.945
0.912
0.979
aSignificant
at
˛
=
0.05.
Incidence
rate
ratio
(IRR).
rate
ratio
(IRR)
of
MERS-CoV
and
its
95%
confidence
intervals
(CI)
by
months
and
years
(Table
2)
and
by
climate
factors
(Table
3).
In
this
analysis,
we
tested
a
number
of
hypotheses
in
which
the
monthly
incidence
rate
is
influenced
by
climatic
conditions.
A
two-
sided
P
0.05
was
considered
significant.
Line
plots
were
used
to
illustrate
the
correlation
between
each
of
the
climate
factors
and
the
monthly
number
of
MERS-CoV
cases
(Figs.
2–6).
Results
A
total
of
712
MERS-CoV
cases
were
included
in
the
analysis
(mean
age
54.2
±
9.9
years),
and
404
(56.1%)
MERS-CoV
cases
were
diagnosed
during
a
five-month
period
from
April
to
August
over
the
study
period.
Table
1
summarizes
the
climate
factors
and
distribu-
tion
of
MERS-CoV
cases.
The
median
number
of
MERS-CoV
cases
was
4
(IQR
ranges
from
3
to
9)
per
month.
The
median
values
of
monthly
average
temperature,
relative
humidity,
ultraviolet
index,
cloud,
and
wind
speed
were
27 C,
22%,
7,
7,
and
13.2,
respectively.
Please
cite
this
article
in
press
as:
Altamimi
A,
Ahmed
AE.
Climate
factors
and
incidence
of
Middle
East
respiratory
syndrome
coronavirus.
J
Infect
Public
Health
(2019),
https://doi.org/10.1016/j.jiph.2019.11.011
ARTICLE IN PRESS
G Model
JIPH-1216;
No.
of
Pages
5
A.
Altamimi,
A.E.
Ahmed
/
Journal
of
Infection
and
Public
Health
xxx
(2019)
xxx–xxx
3
Fig.
1.
Monthly
MERS-CoV
cases
in
Riyadh
Region,
November
1,
2012–December
31,
2018.
Fig.
2.
Monthly
MERS-CoV
cases
and
temperature
in
Riyadh
Region,
November
1,
2012–December
31,
2018.
Fig.
3.
Monthly
MERS-CoV
cases
and
humidity
in
Riyadh
Region,
November
1,
2012–December
31,
2018.
Fig.
4.
Monthly
MERS-CoV
cases
and
Ultraviolet
index
in
Riyadh
Region,
November
1,
2012–December
31,
2018.
Please
cite
this
article
in
press
as:
Altamimi
A,
Ahmed
AE.
Climate
factors
and
incidence
of
Middle
East
respiratory
syndrome
coronavirus.
J
Infect
Public
Health
(2019),
https://doi.org/10.1016/j.jiph.2019.11.011
ARTICLE IN PRESS
G Model
JIPH-1216;
No.
of
Pages
5
4
A.
Altamimi,
A.E.
Ahmed
/
Journal
of
Infection
and
Public
Health
xxx
(2019)
xxx–xxx
Fig.
5.
Monthly
MERS-CoV
cases
and
cloud
index
in
Riyadh
Region,
November
1,
2012–December
31,
2018.
Fig.
6.
Monthly
MERS-CoV
cases
and
wind
index
in
Riyadh
Region,
November
1,
2012–December
31,
2018.
The
incidence
of
the
MERS-CoV
cases
by
time
in
the
Riyadh
Region
is
presented
in
Fig.
1.
The
highest
peak
timing
was
posi-
tioned
on
August
2015,
followed
by
April
2014,
June
2017,
March
2015,
and
June
2016.
Findings
of
these
figures
were
confirmed
with
the
estimated
IRRs
in
Table
2.
The
highest
IRRs
were
noted
in
August
(IRR
=
6.178,
95%
confidence
interval
(CI):
4.164–9.166)
and
followed
by
May
(IRR
=
3.033,
95%
CI:
2.008–4.583)
and
June
(IRR
=
2.427,
95%
CI:
1.606–3.666).
The
IRR
was
greater
in
2015
(IRR
=
8.636,
95%
CI:
2.769–26.937)
and
2014
(IRR
=
5.194,
95%
CI:
1.660–16.252).
Table
3
demonstrates
the
effects
of
climate
factors
on
the
inci-
dence
of
the
MERS-CoV
cases.
With
the
exception
of
cloud,
four
climate
factors
were
found
to
be
statistically
correlated
with
a
reduction
or
increase
in
the
incidence
of
the
MERS-CoV
cases.
High
temperature
(IRR
=
1.054,
95%
CI:
1.043–1.065)
and
a
high
ultravi-
olet
index
(IRR
=
1.401,
95%
CI:
1.331–1.475)
were
correlated
with
a
higher
incidence
of
the
MERS-CoV
cases.
However,
low
humidity
(IRR
=
0.956,
95%
CI:
0.948–0.964)
and
low
wind
speed
(IRR
=
0.945,
95%
CI:
0.912–0.979)
were
correlated
with
a
lower
incidence
of
the
MERS-CoV
cases.
Figs.
2–6
support
the
analysis
in
Table
3,
whereas
MERS-CoV
detection
was
high
with
high
temperatures
and
ultra-
violet
index
values
and
low
MERS-CoV
detection,
with
increased
relative
humidity
and
wind
speed
values.
Discussion
In
the
eight
years
since
its
circulation,
the
variations
in
MERS-
CoV
frequencies
over
time
have
been
consistently
unclear.
This
study
estimated
the
monthly
incidence
of
MERS-CoV
infection
and
evaluated
its
correlation
with
climate
factors
in
the
Riyadh
region,
Saudi
Arabia.
The
MERS
viral
activity
and
development
is
highly
influenced
by
seasonality,
as
multiple
peaks
occur
in
hot
seasons
(April
to
August)
over
the
study
period.
A
one C
increase
in
monthly
average
temperature
correlated
with
increasing
the
monthly
inci-
dence
of
MERS-CoV
infection
by
5.4%.
This
finding
contradicts
the
Gardner
et
al.
study,
where
they
reported
low
temperatures
associ-
ated
with
high
risk
in
primary
MERS-CoV
infections
[9].
However,
our
report
was
in
agreement
with
the
Alghamdi
et
al.
study
where
they
found
that
high
temperatures
enhance
the
spread
of
MERS-
CoV
in
the
population
[11].
The
effects
of
temperature
on
viral
viability
can
be
clarified
in
future
research
studies.
We
found
that
relative
humidity
was
inversely
associated
with
MERS-CoV
cases.
The
link
between
the
monthly
relative
humidity
and
the
monthly
number
of
MERS-CoV
cases
could
be
described
by
a
4.4%
increase
in
the
monthly
incidence
of
MERS-CoV
infections
for
every
percent
of
increase
in
relative
humidity.
This
correlation
is
clearly
illustrated
in
Fig.
3.
This
is
similar
to
previous
reports
where
MERS-CoV
cases
increased
with
low
humidity
[9,11–13].
Our
findings
may
clarify
viral
seasonality,
as
it
explains
why
most
MERS-CoV
outbreaks
occurred
between
April
and
August,
where
temperatures
reached
the
highest
and
humidity
reached
the
low-
est
[14].
This
timing
may
facilitate
an
optimal
environment
for
the
virus
to
survive
and
transmit.
Another
important
finding
of
our
study
is
that
the
monthly
aver-
age
of
the
ultraviolet
index
was
found
to
be
positively
correlated
with
the
monthly
number
of
MERS-CoV
cases.
To
date,
no
other
studies
have
considered
the
ultraviolet
index
among
the
climate
factors
that
could
increase
the
risk
of
MERS-CoV
infection.
These
climate
factors
must
be
evaluated
in
hospitals
and
community
set-
tings,
as
the
virus
appears
more
viable
in
certain
weather
[11]
conditions
and
geographic
regions
[3].
Finding
of
such
studies
can
serve
as
source
of
control
measures
to
prevent
and
eliminate
the
risk
of
infection,
particularly
in
Saudi
Arabia.
The
incidence
of
MERS-CoV
infection
tends
to
decrease
with
wind
speed.
Gardner
et
al.
have
shown
that
low
wind
speed
increased
the
odds
of
primary
MERS-CoV
infections.
Risk
communi-
Please
cite
this
article
in
press
as:
Altamimi
A,
Ahmed
AE.
Climate
factors
and
incidence
of
Middle
East
respiratory
syndrome
coronavirus.
J
Infect
Public
Health
(2019),
https://doi.org/10.1016/j.jiph.2019.11.011
ARTICLE IN PRESS
G Model
JIPH-1216;
No.
of
Pages
5
A.
Altamimi,
A.E.
Ahmed
/
Journal
of
Infection
and
Public
Health
xxx
(2019)
xxx–xxx
5
cation
strategy
may
improve
public’s
perception
about
the
impact
of
climate
factors
on
the
MERS-CoV
infections
and
outbreaks.
The
strength
of
this
study
is
the
large
number
of
MERS-CoV
patients
included
in
the
analysis,
and
the
study
identifies
impor-
tant
factors
affecting
MERS-CoV
viral
seasonality.
In
agreement
with
previous
reports
[11,13],
the
study
highlights
the
need
for
considering
climate
factors
on
MERS-CoV
guidelines
for
control
measures
and
prevention.
The
overall
findings
of
the
study
are
that
the
higher
the
temperature
and
ultraviolet
index,
the
higher
incidence
of
MERS-CoV
infections,
and
vice
versa
for
humidity
and
wind
speed.
However,
we
noted
limitations
in
this
study.
A
number
of
potential
confounding
factors
such
as
patient
characteristics
and
source
of
the
infections
were
not
included
in
our
investigation.
The
climate
data
were
taken
retrospectively
from
Google
reports
on
a
monthly
basis
during
the
span
in
which
the
cases
were
reported.
The
authors
were
not
able
to
assess
the
influence
of
climate
fac-
tors
on
the
source
of
infection,
the
route
of
transmission,
or
the
susceptible
population.
The
impact
of
climate
factors
on
incidence
of
MERS-CoV
in
small
environments
such
as
families
and
hospitals
can
be
addressed
in
future
studies.
Conclusion
The
novel
coronavirus,
MERS-CoV,
is
influenced
by
climate
con-
ditions
with
increased
incidence
between
April
and
August.
The
virus
appears
more
viable
in
certain
weather
conditions.
High
temperature,
high
ultraviolet
index,
low
wind
speed,
and
low
rel-
ative
humidity
are
contributors
to
increased
MERS-CoV
cases.
The
climate
factors
must
be
evaluated
in
hospitals
and
community
set-
tings
and
integrated
into
guidelines
to
serve
as
a
source
of
control
measures
to
eliminate
the
risk
of
infection.
Funding
No
funding
sources.
Competing
interests
None
declared.
Ethical
approval
Ethical
approval
was
obtained
from
Saudi
Ministry
of
Health.
This
study
has
been
completed
prior
to
Dr
Anwar
Ahmed
joining
the
Uniformed
Services
University
of
the
Health
Sciences
and
Henry
M
Jackson
Foundation
for
the
Advancement
of
Military
Medicine.
Disclaimer
The
contents,
views
or
opinions
expressed
in
this
publication
or
presentation
are
those
of
the
authors
and
do
not
necessarily
reflect
official
policy
or
position
of
Uniformed
Services
University
of
the
Health
Sciences,
the
Department
of
Defense
(DoD),
or
Departments
of
the
Army,
Navy,
or
Air
Force.
Mention
of
trade
names,
commercial
products,
or
organizations
does
not
imply
endorsement
by
the
U.S.
Government.
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... However, our findings, indicated that the number of cases increased as humidity decreased. These findings are consistent with those reported by Al Tamimi et al in Saudi Arabia [39] and Ward et al in Australia [40], in which it was found that a 1% decrease in relative humidity was associated with a 7-8% increase in COVID-19. Additionally, an inverse association was observed between COVID-19 deaths and humidity in the UAE and China [13,15,36]. ...
... Consequently, COVID-19 cases and deaths may decrease with increasing humidity. However, since data are obtained from meteorological recording stations, they do not take into consideration the differences between indoor and outdoor environments [39]. It is worth noting that our findings regarding the effect of humidity on the number of cases reported in the Northern Hemisphere are similar to those reported in the Southern Hemisphere [40], suggesting that humidity might be an important predictor of COVID-19 cases and deaths. ...
... Consistent with previous reports, our results demonstrated that wind speed was significantly and positively correlated with the number of COVID-19 cases in Qatar [33], and was inversely correlated with the number of cases in the UAE and deaths in KSA. These findings indicate that lower wind speed is associated with less reporting of cases and deaths, which has been previously reported in Saudi Arabia with other viral infections, where low wind speed was correlated with a lower incidence of MERS-CoV cases [17,39]. Even though these correlations are statistically significant, they are relatively small. ...
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... Various vector-borne zoonotic viral diseases including COVID-19, dengue, Chikungunia, malaria, Ebola, and Zika are increasing with changes in climatic conditions [31][32][33][34][35]. Previous studies have shown that meteorological factors affect the growth and activity of respiratory viral diseases including SARS-CoV [36][37][38]. Experimental studies have shown that SARS-CoV-2 is highly active in conditions with low relative humidity and temperature, whereas genetic materials decay rapidly in an environment with high relative humidity and temperature [4,39,40]. When the virus is exposed to increased relative humidity, temperatures, and simulated solar light, the virus becomes even less stable (half-life, 3 min) [41,42]. ...
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A growing number of studies suggest that climate may impact the spread of COVID-19. This hypothesis is supported by data from similar viral contagions, such as SARS and the 1918 Flu Pandemic, and corroborated by US influenza data. However, the extent to which climate may affect COVID-19 transmission rates and help modeling COVID-19 risk is still not well understood. This study demonstrates that such an understanding is attainable through the development of regression models that verify how climate contributes to modeling COVID-19 transmission, and the use of feature importance techniques that assess the relative weight of meteorological variables compared to epidemiological, socioeconomic, environmental, and global health factors. The ensuing results show that meteorological factors play a key role in regression models of COVID-19 risk, with ultraviolet radiation (UV) as the main driver. These results are corroborated by statistical correlation analyses and a panel data fixed-effect model confirming that UV radiation coefficients are significantly negatively correlated with COVID-19 transmission rates.
... Several studies have analysed the influence of climate and geographical factors on the incidence of COVID-19. [1][2][3][4] Seasonality, described for other respiratory viruses, 5,6 could also play a role in the transmission of SARS-CoV-2. [7][8][9][10] This temporal pattern seems to be attributed more to environmental factors than to changes in the genomic composition of the virus. ...
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Introduction Several studies have analyzed the influence of meteorological and geographical factors on the incidence of COVID-19. Seasonality could be important in the transmission of SARS-CoV-2. This study aims to evaluate the geographical pattern of COVID-19 in Spain and its relationship with different meteorological variables. Methods A provincial ecological study analyzing the influence of meteorological and geographical factors on the cumulative incidence of COVID-19 in the 52 (24 coastal and 28 inland) Spanish provinces during the first three waves was carried out. The cumulative incidence was calculated with data from the National Statistical Institute (INE) and the National Epidemiological Surveillance Network (RENAVE), while the meteorological variables were obtained from the Spanish Meteorological Agency (AEMET). Results The total cumulative incidence, in all three waves, was lower in the coastal provinces than in the inland ones (566 ± 181 vs. 782 ± 154; P = 2.5 × 10⁻⁵). The cumulative incidence correlated negatively with mean air temperature (r = −0.49; P = 2.2 × 10⁻⁴) and rainfall (r = −0.33; P = .01), and positively with altitude (r = 0.56; P = 1.4 × 10⁻⁵). The Spanish provinces with an average temperature <10 °C had almost twice the cumulative incidence than the provinces with temperatures >16 °C. The mean air temperature and rainfall were associated with the cumulative incidence of COVID-19, regardless of other factors (Beta Coefficient of −0.62; P = 3.7 × 10⁻⁷ and −0.47; P = 4.2 × 10⁻⁵ respectively) Conclusions Meteorological and geographical factors could influence the evolution of the pandemic in Spain. Knowledge regarding the seasonality of the virus would help to predict new waves of COVID-19 infections
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Coronaviruses are Baltimore Class I viruses of the family Coronaviridae. Similarities and differences to other members of these groups are discussed. Proposed reservoir/intermediate hosts of severe acute respiratory system coronavirus (SARS-CoV), Middle Eastern respiratory system coronavirus, and SARS-CoV-2 are presented. Bats appear to be reservoir hosts for these and some animal coronaviruses. Other potential reservoir/intermediate hosts of pathogenic coronaviruses are presented, with particular emphasis on rodents and birds. Potential methods to predict or prevent future pandemics include the One Health Approach and SpillOver. Factors driving epidemics and pandemics are discussed, particularly microbial, host-related, and environmental factors as well as ‘The Human Factor,’ medical and behavioral interventions that decrease disease spread and severity. The author’s vision for Infectious Disease Centers (IDCs), similar to Ebola Centers, is presented. IDCs would respond to a broad range of infectious diseases, utilizing separated, negative-pressure areas of existing hospitals with specialized, trained healthcare personnel, microbiologists, public health officials, and lab technicians on call. The proposed IDCs would have stockpiles of personal protective equipment (PPE), equipment, and laboratory facilities on hand to respond to a range of infections. Equipment could include ventilators, autoclaves, dialysis equipment, and three-dimensional printers. The latter was used to produce PPE and ventilators during the COVID-19 pandemic. Other innovative plans would be encouraged, such as the conversions of a deck of a long-distance Italian ferry for patients needing an intermediate level of care during the COVID-19 pandemic. Problems associated with infectious disease epidemics in developing countries are examined, with suggestions for the inclusion of appropriate personnel, such as local cultural experts and interpreters, as well as innovative planners and, perhaps, 3-D printers.
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Background: Although Middle East respiratory syndrome coronavirus (MERS-CoV) diagnostic delays remain a major challenge in health systems, the source of delays has not been recognized in the literature. The aim of this study is to quantify patient and health-system delays and to identify their associated factors. Methods: The study of 266 patients was based on public source data from the World Health Organization (WHO) (January 2, 2017-May 16, 2018). The diagnostic delays, patient delays, and health-system delays were calculated and modelled using a Poisson regression analysis. Results: In 266 MERS-CoV patients reported during the study period, the median diagnostic delays, patient delays, and health-system delays were 5 days (interquartile [IQR] range: 3-8 days), 4 days (IQR range: 2-7 days), and 2 days (IQR range: 1-2 days), respectively. Both patient delay (r = 0.894, P = 0.001) and health-system delay (r = 0.163, P = 0.025) were positively correlated with diagnostic delay. Older age was associated with longer health-system delay (adjusted relative ratios (aRR), 1.011; 95% confidence intervals (CI), 1.004-1.017). Diagnostic delay (aRR, 1.137; 95% CI, 1.006-1.285) and health-system delays (aRR, 1.217; 95% CI, 1.003-1.476) were significantly longer in patients who died. Conclusion: Delays in MERS-CoV diagnosis exist and may be attributable to patient delay and health-system delay as both were significantly correlated with longer diagnosis delay. Early MERS-CoV diagnosis may require more sensitive risk assessment tools to reduce avoidable delays, specifically those related to patients and health system.
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Background Middle East respiratory syndrome coronavirus (MERS-CoV) is endemic in dromedary camels in the Arabian Peninsula, and zoonotic transmission to people is a sporadic event. In the absence of epidemiological data on the reservoir species, patterns of zoonotic transmission have largely been approximated from primary human cases. This study aimed to identify meteorological factors that may increase the risk of primary MERS infections in humans. Methods A case-crossover design was used to identify associations between primary MERS cases and preceding weather conditions within the 2-week incubation period in Saudi Arabia using univariable conditional logistic regression. Cases with symptom onset between January 2015 – December 2017 were obtained from a publicly available line list of human MERS cases maintained by the World Health Organization. The complete case dataset (N = 1191) was reduced to approximate the cases most likely to represent spillover transmission from camels (N = 446). Data from meteorological stations closest to the largest city in each province were used to calculate the daily mean, minimum, and maximum temperature (οC), relative humidity (%), wind speed (m/s), and visibility (m). Weather variables were categorized according to strata; temperature and humidity into tertiles, and visibility and wind speed into halves. Results Lowest temperature (Odds Ratio = 1.27; 95% Confidence Interval = 1.04–1.56) and humidity (OR = 1.35; 95% CI = 1.10–1.65) were associated with increased cases 8–10 days later. High visibility was associated with an increased number of cases 7 days later (OR = 1.26; 95% CI = 1.01–1.57), while wind speed also showed statistically significant associations with cases 5–6 days later. Conclusions Results suggest that primary MERS human cases in Saudi Arabia are more likely to occur when conditions are relatively cold and dry. This is similar to seasonal patterns that have been described for other respiratory diseases in temperate climates. It was hypothesized that low visibility would be positively associated with primary cases of MERS, however the opposite relationship was seen. This may reflect behavioural changes in different weather conditions. This analysis provides key initial evidence of an environmental component contributing to the development of primary MERS-CoV infections.
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Middle East respiratory syndrome coronavirus (MERS-CoV) remains a notable disease and poses a significant threat to global public health. The Arabian Peninsula is considered a major global epicentre for the disease and the virus has crossed regional and continental boundaries since 2012. In this study, we focused on exploring the temporal dynamics of MERS-CoV in human populations in the Arabian Peninsula between 2012 and 2017, using publicly available data on case counts and combining two analytical methods. Disease progression was assessed by quantifying the time-dependent reproductive number (TD-Rs), while case series temporal pattern was modelled using the AutoRegressive Integrated Moving Average (ARIMA). We accounted for geographical variability between three major affected regions in Saudi Arabia including Eastern Province, Riyadh and Makkah. In Saudi Arabia, the epidemic size was large with TD-Rs >1, indicating significant spread until 2017. In both Makkah and Riyadh regions, the epidemic progression reached its peak in April 2014 (TD-Rs > 7), during the highest incidence period of MERS-CoV cases. In Eastern Province, one unique super-spreading event (TD-R > 10) was identified in May 2013, which comprised of the most notable cases of human-to-human transmission. Best-fitting ARIMA model inferred statistically significant biannual seasonality in Riyadh region, a region characterised by heavy seasonal camel-related activities. However, no statistical evidence of seasonality was identified in Eastern Province and Makkah. Instead, both areas were marked by an endemic pattern of cases with sporadic outbreaks. Our study suggested new insights into the epidemiology of the virus, including inferences about epidemic progression and evidence for seasonality. Despite the inherent limitations of the available data, our conclusions provide further guidance to currently implement risk-based surveillance in high-risk populations and, subsequently, improve related interventions strategies against the epidemic at country and regional levels.
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This study set out to identify and analyse trends and seasonal variations of monthly global reported cases of the Middle East respiratory syndrome coronavirus (MERS-CoV). It also made a prediction based on the reported and extrapolated into the future by forecasting the trend. Finally, the study assessed contributions of various risk factors in the reported cases. The motivation for this study is that MERS-CoV remains among the list of blueprint priority and potential pandemic diseases globally. Yet, there is a paucity of empirical literature examining trends and seasonality as the available evidence is generally descriptive and anecdotal. The study is a time series analysis using monthly global reported cases of MERS-CoV by the World Health Organisation between January 2015 and January 2018. We decomposed the series into seasonal, irregular and trend components and identified patterns, smoothened series, generated predictions and employed forecasting techniques based on linear regression. We assessed contributions of various risk factors in MERS-CoV cases over time. Successive months of the MERS-CoV cases suggest a significant decreasing trend ( P = 0.026 for monthly series and P = 0.047 for Quarterly series). The MERS-CoV cases are forecast to wane by end 2018. Seasonality component of the cases oscillated below or above the baseline (the centred moving average), but no association with the series over time was noted. The results revealed contributions of risk factors such as camel contact, male, old age and being from Saudi Arabia and Middle East regions to the overall reported cases of MERS-CoV. The trend component and several risk factors for global MERS-CoV cases, including camel contact, male, age and geography/region significantly affected the series. Our statistical models appear to suggest significant predictive capacity and the findings may well inform healthcare practitioners and policymakers about the underlying dynamics that produced the globally reported MERS-CoV cases.
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Infectious diseases caused by enveloped viruses, such as influenza, severe acute respiratory syndrome (SARS), and Middle East respiratory syndrome (MERS), cause thousands of deaths and billions of dollars of economic losses per year. Studies have found a relationship among temperature, humidity, and influenza virus incidence, transmission, or survival; however, there are contradictory claims about whether absolute humidity (AH) or relative humidity (RH) is most important in mediating virus infectivity. Using the enveloped bacteriophage Phi6, which has been suggested as a surrogate for influenza viruses and coronaviruses, we designed a study to discern whether AH, RH, or temperature is a better predictor of virus survival in droplets. Our results show that Phi6 survived best at high (>85%) and low (<60%) RHs, with a significant decrease in infectivity at mid-range RHs (~60 to 85%). At an AH of less than 22 g · m-³, the loss in infectivity was less than 2 orders of magnitude; however, when the AH was greater than 22 g · m⁻³, the loss in infectivity was typically greater than 6 orders of magnitude. At a fixed RH of 75%, infectivity was very sensitive to temperature, decreasing two orders of magnitude between 19°C and 25°C. We used random forest modeling to identify the best environmental predictors for modulating virus infectivity. The model explained 83% of variation in Phi6 infectivity and suggested that RH is the most important factor in controlling virus infectivity in droplets. This research provides novel information about the complex interplay between temperature, humidity, and the survival of viruses in droplets.
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Background The mortality rate of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) patients is a major challenge in all healthcare systems worldwide. Because the MERS-CoV risk-standardized mortality rates are currently unavailable in the literature, the author concentrated on developing a method to estimate the risk-standardized mortality rates using MERS-CoV 3- and 30-day mortality measures. MethodsMERS-CoV data in Saudi Arabia is publicly reported and made available through the Saudi Ministry of Health (SMOH) website. The author studied 660 MERS-CoV patients who were reported by the SMOH between December 2, 2014 and November 12, 2016. The data gathered contained basic demographic information (age, gender, and nationality), healthcare worker, source of infection, pre-existing illness, symptomatic, severity of illness, and regions in Saudi Arabia. The status and date of mortality were also reported. Cox-proportional hazard (CPH) models were applied to estimate the hazard ratios for the predictors of 3- and 30-day mortality. Results3-day, 30-day, and overall mortality were found to be 13.8%, 28.3%, and 29.8%, respectively. According to CPH, multivariate predictors of 3-day mortality were elderly, non-healthcare workers, illness severity, and hospital-acquired infections (adjusted hazard ratio (aHR) =1.7; 8.8; 6.5; and 2.8, respectively). Multivariate predictors of 30-day mortality were elderly, non-healthcare workers, pre-existing illness, severity of illness, and hospital-acquired infections (aHR =1.7; 19.2; 2.1; 3.7; and 2.9, respectively). Conclusions Several factors were identified that could influence mortality outcomes at 3 days and 30 days, including age (elderly), non-healthcare workers, severity of illness, and hospital-acquired infections. The findings can serve as a guide for healthcare practitioners by appropriately identifying and managing potential patients at high risk of death.
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Background: Although the literature indicates the potential outcomes of a patient's delay in seeking medical support is associated with poor clinical outcomes, delays in the diagnosis itself remain poorly understood in patients with Middle East Respiratory Syndrome - Coronavirus (MERS-CoV). This study aims to estimate the median time interval of confirmed diagnosis after symptom onset and identify its potential predictors in Saudi Arabian MERS patients. Methods: A retrospective study involved patients confirmed with MERS who were publicly reported by the World Health Organization (WHO). Results: 537 symptomatic cases of MERS-CoV infection were included. The median time between symptom onset and confirming MERS diagnosis was 4days (IQR: 2-7), ranging from 0 to 36 days. According to a negative binomial model, the unadjusted rate ratio (RR) of delays in the diagnosis was significantly higher in older patients (>65years) (RR=1.42), non-healthcare workers (RR=1.74), patients with severity of illness (RR=1.22), those with unknown sources of infections (RR=1.84), and those who were in close contact with camels (RR=1.74). After accounting for confounders, the adjusted rate ratio (aRR) of delays in the diagnosis was independently associated with unknown sources of infections (aRR=1.68) and those in close contact with camels (aRR=1.58). Conclusion: The time interval from onset until diagnosis was greater in older patients, non-healthcare workers, patients with severity of illness, patients with unknown sources of infections, and patients in close contact with camels. The findings warrant educational intervention to raise the general public awareness on the importance of early-symptom notification.
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Although Middle East respiratory syndrome coronavirus (MERS-CoV) has a recorded 5 years of circulation in 27 countries worldwide, there is no international study to assess whether there is variation in mortality by region. Neither has there been a comprehensive study detailing how the disease characteristics of MERS-CoV influence mortality in patients presenting symptoms. This study aimed to assess how region, patient and disease characteristics influence 14- and 45-day mortality in MERS patients. The author utilised publically available data on MERS-CoV. The study included 883 MERS patients reported between 5 January 2015 and 10 March 2017. Data on patient and disease characteristics were collected. The mean age at MERS-CoV diagnosis was 54.3 years: 69.1% were male, and 86.7% of the cases were reported from Saudi Arabia. About 40% of MERS patients studied were over the age of 60. The study estimated 14- and 45-day survival rates after initial onset of symptoms: 83.67% and 65.9%, respectively. Saudi Arabian MERS patients exhibited 4.1 and 5.0 times higher 14-day (adjusted hazard risk (aHR) = 4.1; 95% confidence interval (CI) 1.012–16.921) and 45-day (aHR = 5.0; 95% CI 1.856–13.581) mortality risk compared with MERS patients in the Republic of Korea or other countries. Similarly, Middle Eastern MERS patients showed 5.3 and 4.1 times higher 14-day (aHR = 5.3; 95% CI 1.070–25.902) and 45-day (aHR = 4.1; 95% CI 1.288–113.076) mortality risk compared with MERS patients in the Republic of Korea or other countries. The results demonstrated a link between mortality and geography, disease and patient factors such as regions, symptoms, source of infections, underlying medical conditions, modes of transmission, non-healthcare workers and those of older age. Educational programmes, access to healthcare and early diagnosis could be implemented as modifiable factors to reduce the higher mortality rates in MERS patients.
Article
Introduction: In the past five years, there have been 1,936 laboratory-confirmed cases of Middle East Respiratory Syndrome coronavirus (MERS-CoV) in 27 countries, with a mortality rate of 35.6%. Most cases have arisen in the Middle East, particularly the Kingdom of Saudi Arabia, however there was a large hospital-associated outbreak in the Republic of Korea in 2015. Exposure to dromedary camels has been recognized by the World Health Organization (WHO) as a risk factor in primary cases, but the exact mechanisms of transmission are not clear. Rigorous application of nationally defined infection prevention and control measures has reduced the levels of healthcare facility-associated outbreaks. There is currently no approved specific therapy or vaccine available. Areas covered: This review presents an overview of MERS-CoV within the last five years, with a particular emphasis on the key areas of transmission, infection control and prevention, and therapies and vaccines. Expert commentary: MERS-CoV remains a significant threat to public health as transmission mechanisms are still not completely understood. There is the potential for mutations that could increase viral transmission and/or virulence, and zoonotic host range. The high mortality rate highlights the need to expedite well-designed randomized clinical trials for direct, effective therapies and vaccines.