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Influence of extreme weather and meteorological anomalies on outbreaks of influenza A (H1N1)

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  • Centers for Disease Control and Prevention of Hunan Province

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Biological experiments and epidemiological evidence indicate that variations in environment have important effect on the occurrence and transmission of epidemic influenza. It is therefore important to understand the characteristic patterns of transmission for prevention of disease and reduction of disease burden. Based on case records, we analyzed the environmental characteristics including climate variables in Changsha, and then constructed a meteorological anomaly susceptive-infective-removal (SIR) model on the basis of the results of influenza A (H1N1) transmission. The results showed that the outbreak of influenza A (H1N1) in Changsha showed significant correlation with meteorological conditions; the spread of influenza was sensitive to meteorological anomalies, and that the outbreak of influenza A (H1N1) in Changsha was influenced by a combination of absolute humidity anomalous weather conditions, contact rates of the influenza patients and changes in population movements. These findings will provide helpful information regarding prevention strategies under different conditions, a fresh understanding of the emergence and re-emergence of influenza outbreaks, and a new perspective on the transmission dynamics of influenza.
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*Corresponding author (email: tianhuaiyu@gmail.com)
Article
Preventive Medicine & Hygienics March 2013 Vol.58 No.7: 741749
doi: 10.1007/s11434-012-5571-7
Influence of extreme weather and meteorological anomalies
on outbreaks of influenza A (H1N1)
XIAO Hong1†, TIAN HuaiYu1†*, LIN XiaoLing1, GAO LiDong2, DAI XiangYu1,
ZHANG XiXing3, CHEN BiYun3, ZHAO Jian4 & XU JingZhe1
1 College of Resources and Environmental Science, Hunan Normal University, Changsha 410081, China;
2 Hunan Provincial Center for Disease Control and Prevention, Changsha 410002, China;
3 Changsha Municipal Center for Disease Control and Prevention, Changsha 410001, China;
4 Peking University Health Science Center, Beijing 100191, China
Received June 28, 2012; accepted August 3, 2012; published online December 28, 2012
Biological experiments and epidemiological evidence indicate that variations in environment have important effect on the occur-
rence and transmission of epidemic influenza. It is therefore important to understand the characteristic patterns of transmission for
prevention of disease and reduction of disease burden. Based on case records, we analyzed the environmental characteristics in-
cluding climate variables in Changsha, and then constructed a meteorological anomaly susceptive-infective-removal (SIR) model
on the basis of the results of influenza A (H1N1) transmission. The results showed that the outbreak of influenza A (H1N1) in
Changsha showed significant correlation with meteorological conditions; the spread of influenza was sensitive to meteorological
anomalies, and that the outbreak of influenza A (H1N1) in Changsha was influenced by a combination of absolute humidity
anomalous weather conditions, contact rates of the influenza patients and changes in population movements. These findings will
provide helpful information regarding prevention strategies under different conditions, a fresh understanding of the emergence
and re-emergence of influenza outbreaks, and a new perspective on the transmission dynamics of influenza.
influenza A (H1N1), meteorological anomaly, geographic information system, absolute humidity, SIR model
Citation: Xiao H, Tian H Y, Lin X L, et al. Influence of extreme weather and meteorological anomalies on outbreaks of influenza A (H1N1). Chin Sci Bull, 2013,
58: 741749, doi: 10.1007/s11434-012-5571-7
In 2009, the global influenza A (H1N1) epidemics intro-
duced a great burden of disease to China as well as to the
rest of the world. In our opinion such disasters caused by
the influenza pandemics may very well recur again in the
future [1]. Since the beginning of the 20th century, there
have been three influenza pandemics around the world to-
gether affecting millions of people [2,3]; the ‘Spanish flu
pandemic’ (H1N1) of 1918 [4–7], the influenza A subtype
H2N2 pandemic in 1957 [8], and the global pandemic
caused by the influenza A subtype H3N2 in 1968 [9]. The
outbreak and spread of influenza is a complex process,
clearly influenced by the virulence of the influenza virus,
the immunity of the host, and frequency of contacts between
hosts. Research conducted in the last few decades has de-
scribed a number of factors that influence the transmission
and spread of influenza, including (1) mutation of the vi-
ruses and the fluctuating of population immunity levels,
such as the emergence of new virus subtypes [10,11],
changes in immunity levels caused by changes in melatonin
[12] and vitamin D levels [13–15]; (2) variations in host
behavior and the frequency of population contact and travel
[16], such as the movements associated with school terms
and holidays [17,18]; and (3) the survival and transmission
abilities of viruses affected by variations in temperature and
absolute humidity, and movements of the upper atmosphere
[19].
The influenza virus is mainly spread via the respiratory
742 Xiao H, et al. Chin Sci Bull March (2013) Vol.58 No.7
tract [20]. Virus present in respiratory secretions is trans-
mitted through spitting and latent infections are spread into
the air by coughing and sneezing [21]. The transmission of
influenza in temperate regions correlates significantly with
seasonal variations [22]. In subtropical Southeast Asia the
transmission circle occurs twice a year [23,24], and in trop-
ical regions transmission is closely associated with the rainy
season [25,26]. Previous studies have found that temperature,
relative humidity [27–31] and absolute humidity [32–34]
have great influence on the occurrence and spread of the
influenza virus. The environmental conditions of tempera-
ture and humidity affect the production and infectivity of
virus droplet nuclei and the survival ability of virus [33],
thus providing a biophysical explanation of the relationship
between the variations of season and environment and the
duration and characteristics of influenza epidemics [35].
Understanding the dynamics of these relationships is vital
for planning and prioritizing measures for the control and
prevention of influenza epidemics in different environments
[36].
The peak of the 2009 influenza A (H1N1) pandemic is
over but there is still a lack of understanding of the trans-
mission mechanisms, the meteorological conditions suitable
for the influenza transmission, and the influence of extreme
weather and meteorological anomalies on the outbreaks of
influenza epidemics. The objective of this study was to con-
struct a theoretical model of influenza outbreaks based on
meteorological epidemiology. We collected all the relevant
data relating to the incidence of influenza cases during the
recent outbreak. We then obtained all the meteorological
parameters thought to influence disease outbreak and
transmission using geographic information systems and
remote sensing. We analyzed the data using statistical tests
to explore the meteorological characteristics of influenza
outbreaks, and combining this with the knowledge gained
from biology experiments and susceptive-infective-removal
(SIR) models we designed a meteorological anomaly model
of influenza A (H1N1) epidemics, aiming at providing a
scientifically based model that would aid the control and
prevention of influenza epidemics in the future.
1 Materials and methods
1.1 Cases and meteorological data
All the data on influenza A (H1N1) cases reported in Changsha
from May 22, 2009 to October 11, 2010 (nearly 99.9% cases
concentrated from May 2009 to Jan. 2010, 5477/5483×
100%) were obtained from the Changsha Center for Dis-
ease Control and Prevention and the Hunan Provincial
Center for Disease Control and Prevention (CDC). The data
include information about sex, age, residential address, oc-
cupation, date of disease onset and diagnostic criteria for
each patient.
Meteorological data from 1951 to 2010 was based on
daily observation data at Hunan meteorological sites and on
interpolated data (Table 1); kriging interpolation was used
to calculate the daily meteorological measurements of the
study region from May 22, 2009 to October 11, 2010, for
temperature, air pressure, absolute humidity and relative
humidity relevant to the occurrence of infections in space
and time (Figure 1). Based on the daily series of meteoro-
logical data from 1951 to 2009, the thresholds of extreme
weather conditions were estimated using the 95th and 5th
percentiles. The mean values of the daily meteorological
series were calculated from 1951 to 2008 and compared
with the records from 2009 to obtain the outliers for ana-
lyzing the relationship between the outbreak of epidemics
and meteorological anomalies. Taking absolute humidity as
an example:
2009
AH AH AH, (1)
where AH is absolute humidity (AH) anomaly, AH2009 is
the daily records of AH in 2009, AH is the mean value of
AH in the same periods from 1951 to 2008.
Analysis of the outbreaks and meteorological conditions
during the epidemic periods included the following steps: (1)
analysis of the transmission of influenza A (H1N1) epi-
demics when a meteorological anomaly occurs, (2) classifi-
cation of the daily infections, calculation of the frequency of
different meteorological conditions and the corresponding
incidence and level of epidemic, and (3) calculation of the
frequency of the “combined effects” of multiple meteoro-
logical conditions and their relationship to outbreaks.
2.2 Experimental studies on transmission of the virus
Experimental studies found that the ability of the influenza
virus to survive and spread among mice varies with tempera-
ture, relative humidity and season [37] (Harper [38] and
Hemmes et al. [39]). In recent years, in experiments using
dolphins, Lowen et al. [28–30] found that the transmission
ability of the influenza virus in air varies under different
conditions of temperature and humidity (H3N2, Influen A/
Panama/2007/99; H1N1, Influen A/Netherlands/602/2009).
In these studies the transmission ability of influenza virus
under different conditions of temperature, absolute humidity
and relative humidity was analyzed in 40 sets of experi-
ments, 24 sets using H3N2 and 16 sets using H1N1 (Figure
2). Logistic regression was used to select a suitable model
and to extract epidemiological parameters. The absolute
humidity conditions in different experiments were calculat-
ed using eqs. (2)–(4) below [40]. According to the experi-
mental results, we know that the transmission ability of in-
fluenza A (H1N1) virus is different under different envi-
ronmental conditions.
0
0
11
ex
p
,
ss
v
L
eT eT RT T


 





(2)
Xiao H, et al. Chin Sci Bull March (2013) Vol.58 No.7 743
Table 1 Data source and description
Variables Data source Description
Cases Changsha Center for Disease Control and Prevention Case report
Temperature/day China’s Meteorological Science Data Shared Services Nets (http://cdc.cma.gov.cn/) Site data
Relative humidity/day China’s Meteorological Science Data Shared Services Nets (http://cdc.cma.gov.cn/) Site data
Air pressure/day China’s Meteorological Science Data Shared Services Nets (http://cdc.cma.gov.cn/) Site data
Absolute humidity/day Calculated by eqs. (2)–(4) Site data
Temperature distribution/month http://www.worldclim.org Interpolation data
Precipitation distribution/month http://www.worldclim.org Interpolation data
Figure 1 Daily interpolation of meteorological data and the spatial-temporal records of infections. (a) Meteorological stations in the study area; (b) inter-
polation of daily meteorological data and the distribution of infections.
100%
e
E
 , (3)
w
v
e
R
T
, (4)
where es(T) is the saturation vapor pressure at the tempera-
ture T, T0=273.15 K, es(T0)=611.29 Pa, L is the latent heat
of water evaporation, Rv is the gas constant of water evapo-
ration, 461.5 J kg1 K1;
is relative humidity, e is vapor
pressure; E is saturation vapor pressure;
w is absolute hu-
midity.
1.3 Meteorological anomaly model in the influenza
epidemic outbreak
In this study, the predicted minimum basic reproductive rate
(R0min) is determined by the mutation of the virus and the
immune levels of the population in the outbreak of influen-
za epidemic, while the wave of the basic reproductive rate
(R0maxR0min) is affected by the meteorological anomaly.
Based on experimental research of the transmission ability
of influenza virus and the meteorological statistics, analysis
of the influence of meteorological anomaly on the spread of
influenza was introduced to a susceptive-infective-removal
(SIR) model. According to the general infection mechanism,
SIR classifies the target population of the disease into three
specific groups, namely the susceptible (S), infective (I) and
removed (R) individuals. The model has achieved good
results and been widely used to predict the dynamics of in-
fectious diseases [41,42].
The basic reproductive rate R0 is equal to the contact rate
divided by the removal rate, namely the number of suscep-
tible infected by a single infective case; only when R0>1 can
the disease spread among population: if R0<1, the disease
will disappear; if R0=1, the disease will remain unchanged,
and neither spread nor disappear [43,44]. The meteorologi-
cal anomaly model can therefore be expressed as
744 Xiao H, et al. Chin Sci Bull March (2013) Vol.58 No.7
Figure 2 The experimental results of the transmission ability of influenza virus. Significance of each model fit was assessed by using the t statistic for
which the P value is shown in the legend. (a) Relative humidity regression (H3N2); (b) relative humidity regression (H1N1); (c) temperature regression
(H3N2); (d) temperature regression (H1N1); (e) absolute humidity regression (H3N2); (f) absolute humidity regression (H1N1).
d() ()()()
,
d
St tItSt
tN
 (5)
d() () () () ()
,
d
I
ttItStIt
tND
 (6)
0()
() ,
R
t
t
(7)
0max
0max 0min 0min
() exp AH()
lo
g
,
Rt a t
RR R


(8)
Xiao H, et al. Chin Sci Bull March (2013) Vol.58 No.7 745
() () ()
AH( ) () ( () 0.378 ())
v
Pt RHt Et
t
R
Tt Pt Et

  , (9)
where S(t), I(t),
(t) are the susceptible, infective and re-
moved in t day, N is the total population, D is the mean in-
fective stage,
is regression function of experiments of
transmission ability of influenza virus (H1N1, Influenza A/
Netherlands/602/2009), P is air pressure, T is temperature,
RH is relative humidity, E is saturation vapor pressure at the
temperature of T, R0min, R
0max is the daily maximum and
minimum reproductive, Rv is gas constant of water evapora-
tion, 461.5 J kg1 K1, |
| is meteorological anomaly data,
if
is max, R0=R0max, while
=0, R0 is the transmission
ability under normal conditions.
Simulated annealing (SA) was used to estimate the opti-
mal solution of the model from May 22 to Dec 31 in 2009,
the parameters to be estimated including D, R0min, R0max and
. To predict the epidemic, the cases in 2010 were left as
test data, to validate the model effects. The daily new influ-
enza A (H1N1) cases are obsi (i=1,2,3,…, N), the prediction
of the model is esti (i=1,2,3,…, N), objection function “loss”
is used to evaluate the predictive effect, and the closer R2 is
to 1, the better the predictive results are. In other studies
using this type of model, R0 ranges from 1.3 to 3 [10,45–47],
and D ranges from 2 to 7 d [45,48]. In this research, Rmax
ranges from 1.3 to 4, Rmin in the model ranges from 0 to 1.3,
and D ranges from 2 to 30 d.
2 Meteorological analysis and simulation
results in influenza outbreaks
2.1 Weather conditions during an influenza A (H1N1)
pandemic
The meteorological records for the Changsha City outbreak
of Influenza A (H1N1) from October 22 to December 22
show that temperature, absolute humidity, barometric pres-
sure are within the parameters outlined as extreme records
(Figure 3). Absolute humidity gave an extremely low value
on November 2 (77 cases), November 3 (55 cases), No-
vember 17 (184 cases), November 18 (242 cases) and No-
vember 19 (245 cases), 2009. With the air being drier the
humidity continued to decrease and the incidence of infec-
tion gradually increased. Barometric pressure appeared ex-
tremely high on November 1–4 (258 cases), November 12–
22 (2046 cases), November 28 to December 5 (395 cases),
and December 13–21 (51 cases) in 2009. The disease inci-
dence reached its peak in mid-November.
Taking into account that there was considerable variability
in weather conditions during the study period, we calculated
statistics for the frequency of the various meteorological
conditions and compared them with the corresponding case
incidence from May 22, 2009 to December 31, 2009. The
results showed that over 50% (2929/5439) of the cases ap-
peared in weather when the maximum wind speed was 7–11
km h1, while 64.9% (3534/5439) of the cases appeared in
Figure 3 Weather records and the corresponding prevalence of cases.
746 Xiao H, et al. Chin Sci Bull March (2013) Vol.58 No.7
weather when the average wind speed was 3–6 km h1, sug-
gesting that a lower wind speed contributed to the spread of
influenza A (H1N1). The 3772 cases appeared when the min-
imum temperature was 0–10°C, accounting for 69.4% of the
total number of cases, implying that lower temperatures were
more conducive to the spread of the epidemic and 5402 cases
(99.3%) were found on days where the pressure was greater
than 1008 hPa. As the pressure rises, the probability of cases
incidence rises, showing that higher pressure is more con-
ducive to the development of the epidemic. Measurement of
absolute humidity appeared to show that the dry air helps
the spread of the influenza A (H1N1) since 69.5% cases (3782/
5439) occurred when absolute humidity was 3–7 g m3.
Multivariate statistical analysis of the meteorological
elements (Table 2) shows that weather conditions of the in-
fluenza A (H1N1) epidemic were closely connected with
absolute humidity, and barometric pressure. The largest
number of cases appeared when weather was composed of
the following four meteorological conditions: (1) low tem-
perature, dry, high pressure; (2) dry, low wind speed, high
pressure; (3) low wind speed, high pressure; and (4) low
temperature, dry, low wind speed, high air pressure. The
influenza outbreaks occurred when the temperature was
below 10°C, absolute humidity was lower than 10 g m3,
wind speed lower than 10 km h1 and air pressure higher
than 1010 hPa. Under these circumstances, the high pres-
sure may have allowed virus droplets to sink in the air, the
low temperature may have contributed to the survival of the
virus, and the low wind speed and dry air may have helped
aggregation and thus the spread of the influenza virus. In
addition, in the second half of 2009 the influence of El Niño
was evident in abnormal weather conditions including tem-
perature, pressure and absolute humidity. Minimum tem-
peratures fell below about 7°C, the absolute humidity was
lower than the mean of 5 g m3, and the pressure was higher
than the mean of about 20 hPa in Changsha over this period
and was significantly correlated with incidence of influenza
A (H1N1) (Figure 4).
2.2 Simulation results
Iteration of the simulated annealing algorithm obtained an
optimal solution of epidemiological parameters: R0min=1.28,
R0max=3.20, removal rate=0.17, and average infection period
(D)=5.88 days (including the invisible infection period).
However, the weather anomaly model simulation of the
development of H1N1 influenza in Changsha gives a beffer
degree of fit (Figure 5). The (R2) of predicted results and
actual observations is 0.863. Using the 2010 data this model
predicted that the epidemic of influenza A (H1N1) in
Changsha would finish at the end of January 2010, which
concurred closely with the actual situation and illustrated
the strength of the model.
The model’s forecasting power appeared erroneous from
October 23 to November 1, 2009. Further investigation
showed that this was the result of outbreaks in the two
schools in the study area. In this case the model failed be-
cause, on the one hand, the estimation of basic reproductive
number is mostly based on the height of the statistical pop-
ulation, while in isolated small communities R0 is often
Table 2 Statistical representation of the correlation between the number of cases and days with variable weather conditionsa)
Minimum temperature
<10°C
Humidity
<10 g m3
Average wind
speed <10 km h1
Barometric pressure
>1010 hPa
Outbreak influence cases/day
0 19 1049 5099 10099
>200
2 1 1 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 2 5 3 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 0 0 0 0
0 0 1 0 0 0
0 2 0 4 1 0
75 21 0 2 0 0
14 24 11 5 0 0
0 3 2 0 0 0
0 6 13 11 11 4
a) o: Under a corresponding climate condition.
Xiao H, et al. Chin Sci Bull March (2013) Vol.58 No.7 747
Figure 4 Abnormal weather and corresponding number of cases during influenza A (H1N1) flu outbreak in Changsha City.
Figure 5 Daily incidence of influenza A (H1N1) cases in relation to the period of abnormal meteorological conditions in Changsha City, 2009.
larger [49,50], and on the other hand, because in areas with
special characteristics such as a school or among troops
there is often an abnormally high frequency of susceptible
individuals living in close contact.
3 Discussion
The occurrence, development and transmission of infectious
diseases are affected by pathogen and host, and pathogen
and external environment (natural factors and social factors)
[20]. It is impossible to understand the whole dynamics of
transmission simply by identifying the component parts. By
taking a multidisciplinary and integration approach, we can
study the occurrence and transmission of infectious disease
from a new and much wider perspective. In this study, we
found a close connection between weather conditions and
H1N1 influenza epidemic pattern in Changsha. On the one
hand, extreme or anomalous measurements of single mete-
orological factors affected the transmission of influenza; on
the other hand, large number of influenza cases occurred
during periods of extreme or anomalous instances of various
meteorological factors. Using experimental studies, meteor-
ological observation and epidemiological models, we can
analyze the patterns and explore the dynamics of the laws
governing transmission. Through a combination of infec-
748 Xiao H, et al. Chin Sci Bull March (2013) Vol.58 No.7
tious disease surveillance systems and weather forecasting,
valuable early warning systems can be put in place and
prevention and control of major influenza outbreaks can be
carried out. In addition, especially vulnerable groups, for
example, in schools, offices and other crowded indoor en-
vironment can be targeted with appropriate methods for
reducing the transmission and survival ability of the influ-
enza virus and thus reducing the burden of disease [51].
Different indoor environments will require differing ap-
proaches to control and theoretical and practical knowledge
should be combined in order to design specific measures
that are both economical and feasible for reducing the po-
tential burden of disease.
By statistically analyzing weather conditions associated
with influenza A (H1N1), we found that the daily minimum
temperature, absolute humidity and barometric pressure
have a strong influence on the development of the epidemic
situation. More patients appear during periods of the low
temperature compared with high temperature, more cases
appear when pressure is higher and a large number of cases
appear when absolute humidity is lower (AH<16 g m3). In
addition, our “combined effect” [52] statistical analysis
showed that when different combinations of factors oc-
curred they could affect the epidemic situation further, cre-
ating a dangerously increased risk of disease. These factors
included low temperatures, dryness, low wind speed,
high-pressure and epidemics in Changsha (greater than 200
cases per day) occurred as a result of the combination of
such meteorological factors. It is therefore imperative that
the combined effects of meteorological factors are studied
for meteorological sensitive infectious diseases.
Year 2009 experienced the fifth highest temperatures
since 1980. Extreme global weather and climate conditions
occurred from June to December the same year as well as a
very strong El Niño effect and, the multivariate ENSO in-
dex respectively achieved 0.854, 0.929, 0.738, 0.983, 1.039
and 1.084 [53]. At the same time, the El Niño induced cli-
mate change correlated strongly with infection in mainland
China with a time lag of 3 to 5 months [54]. According to
meteorological records during the period of pandemic in-
fluenza A (H1N1), a persistently abnormal climate occurred
in late September, early October to mid-December at
Changsha (in accordance with the time delay cycle). This is
similar to the result of El Niño on the French influenza epi-
demic cycles, and thus provides a new perspective for a
comprehensive understanding of the influenza A (H1N1)
pandemic events [55]. Accurate interpretation of the trend
signal changes in the weather will provide a valuable early
warning signal for the prevention of influenza outbreaks.
This study provided insight into the outbreak of influenza
A (H1N1) in Changsha particularly in relation to absolute
humidity, the contact rates of infected individuals and pop-
ulation changes, although not all influenza pandemics ap-
peared during extreme weather conditions. Extreme weather
and abnormal weather may play a role in fueling the spread
of influenza. Population immunity levels and the virus mu-
tations also determine the baseline and trend of the epidem-
ic; therefore, the problem was how to reflect the evolution
and variability of the virus in the model. One way of im-
proving the model was to employ Markov Chain Monte
Carlo methods involving simulated annealing algorithms
with the various components although the performance of
the algorithm is related to initial parameter value and pa-
rameter sensitivity. An additional important elements to
consider was the fact that the incidence of influenza is usu-
ally higher in lower age groups and among those working in
the service industry. Students and manual laborers also have
a higher incidence than professional people, and certain
special groups with high mobility are an important factor in
the continuing spread and proliferation of the disease. The
study also found that the composition of the susceptible
population [56] had a significant impact on the fit of the
model to the results, posing a problem of how to quantita-
tively evaluate the effect of population heterogeneity in the
flu outbreak and improve the predictive power of the mod-
els. Future work will concentrate on further refinement of
these models.
We wish to express our thanks to the anonymous referees for their helpful
comments on an earlier draft of this article. This work was supported by
Hunan Provincial Natural Science Foundation of China (11JJ3119), the
Key Discipline Construction Project in Hunan Province (2008001), and
the Scientific Research Fund of Hunan Provincial Education Department
(11K037).
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... Previous studies indicate that low temperature and low humidity contribute to the increased risk of seasonal influenza [10][11][12][13]. Xiao et al. [14] and Zhang et al. [15] argue that the outbreak of influenza A (H1N1) had significant correlation with meteorological conditions. A similar relation for human rotavirus infection was described by Moe and Shirley [16], Brandt et al. [17], Konno et al. [18], Anestad [19], and Reyes et al. [20], with a stronger influence of temperature compared to humidity. ...
... The choice of relevant variables to be analyzed was based on previous studies of Wang et al. [23], Bukhari and Jameel [31], Sajadi et al. [25], Ishmatov [32], Royé et al. [1,2], Brini et al. [33], Sloan et al. [34], Wichmann [6], Zhang et al. [15], Lowen and John [35], Jaakkola et al. [12], Xiao et al. (2013) [14], Alessandrini et al. [5], Chan et al. [21], Żuk et al. [36], Lowen et al. [13], Konno et al. [18], & Moe and Shirley [16] -from where it has been selected air temperature at 2 meters height (T2M) and relative humidity (RH) as two important factors that affect human health in general. ...
... The choice of relevant variables to be analyzed was based on previous studies of Wang et al. [23], Bukhari and Jameel [31], Sajadi et al. [25], Ishmatov [32], Royé et al. [1,2], Brini et al. [33], Sloan et al. [34], Wichmann [6], Zhang et al. [15], Lowen and John [35], Jaakkola et al. [12], Xiao et al. (2013) [14], Alessandrini et al. [5], Chan et al. [21], Żuk et al. [36], Lowen et al. [13], Konno et al. [18], & Moe and Shirley [16] -from where it has been selected air temperature at 2 meters height (T2M) and relative humidity (RH) as two important factors that affect human health in general. ...
... There are many controversies about the impacts of wind speed on the spread of viruses. Xiao et al. [29] used multiple sets of climatic conditions to conduct their research and found that slow wind speeds helped the spread of influenza A virus pandemics. Sundell et al. [30] conducted a study on the impacts of four seasons on the transmission rates of influenza A virus pandemics in temperate climates. ...
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