Challenges in the control of COVID-19 outbreaks caused by the delta variant during periods of low humidity: an observational study in Sydney, Australia

Abstract

Background:
Since the appearance of severe acute respiratory coronavirus 2 (SARS-CoV-2) and the coronavirus disease 2019 (COVID-19) pandemic, a growing body of evidence has suggested that weather factors, particularly temperature and humidity, influence transmission. This relationship might differ for the recently emerged B.1.617.2 (delta) variant of SARS-CoV-2. Here we use data from an outbreak in Sydney, Australia that commenced in winter and time-series analysis to investigate the association between reported cases and temperature and relative humidity.

Methods:
Between 16 June and 10 September 2021, the peak of the outbreak, there were 31,662 locally-acquired cases reported in five local health districts of Sydney, Australia. The associations between daily 9:00 am and 3:00 pm temperature (°C), relative humidity (%) and their difference, and a time series of reported daily cases were assessed using univariable and multivariable generalized additive models and a 14-day exponential moving average. Akaike information criterion (AIC) and the likelihood ratio statistic were used to compare different models and determine the best fitting model. A sensitivity analysis was performed by modifying the exponential moving average.

Results:
During the 87-day time-series, relative humidity ranged widely (< 30-98%) and temperatures were mild (approximately 11-17 °C). The best-fitting (AIC: 1,119.64) generalized additive model included 14-day exponential moving averages of 9:00 am temperature (P < 0.001) and 9:00 am relative humidity (P < 0.001), and the interaction between these two weather variables (P < 0.001). Humidity was negatively associated with cases no matter whether temperature was high or low. The effect of lower relative humidity on increased cases was more pronounced below relative humidity of about 70%; below this threshold, not only were the effects of humidity pronounced but also the relationship between temperature and cases of the delta variant becomes apparent.

Conclusions:
We suggest that the control of COVID-19 outbreaks, specifically those due to the delta variant, is particularly challenging during periods of the year with lower relative humidity and warmer temperatures. In addition to vaccination, stronger implementation of other interventions such as mask-wearing and social distancing might need to be considered during these higher risk periods.

Full text

Wardetal. Infectious Diseases of Poverty (2021) 10:139
https://doi.org/10.1186/s40249-021-00926-0
RESEARCH ARTICLE
Challenges inthecontrol ofCOVID-19
outbreaks caused bythedelta variant
duringperiods oflow humidity:
anobservational study inSydney, Australia
Michael P. Ward1* , Yuanhua Liu2, Shuang Xiao3 and Zhijie Zhang2*
Abstract
Background: Since the appearance of severe acute respiratory coronavirus 2 (SARS-CoV-2) and the coronavirus
disease 2019 (COVID-19) pandemic, a growing body of evidence has suggested that weather factors, particularly
temperature and humidity, influence transmission. This relationship might differ for the recently emerged B.1.617.2
(delta) variant of SARS-CoV-2. Here we use data from an outbreak in Sydney, Australia that commenced in winter and
time-series analysis to investigate the association between reported cases and temperature and relative humidity.
Methods: Between 16 June and 10 September 2021, the peak of the outbreak, there were 31,662 locally-acquired
cases reported in five local health districts of Sydney, Australia. The associations between daily 9:00 am and 3:00 pm
temperature (°C), relative humidity (%) and their difference, and a time series of reported daily cases were assessed
using univariable and multivariable generalized additive models and a 14-day exponential moving average. Akaike
information criterion (AIC) and the likelihood ratio statistic were used to compare different models and determine the
best fitting model. A sensitivity analysis was performed by modifying the exponential moving average.
Results: During the 87-day time-series, relative humidity ranged widely (< 30–98%) and temperatures were mild
(approximately 11–17 °C). The best-fitting (AIC: 1,119.64) generalized additive model included 14-day exponential
moving averages of 9:00 am temperature (P < 0.001) and 9:00 am relative humidity (P < 0.001), and the interaction
between these two weather variables (P < 0.001). Humidity was negatively associated with cases no matter whether
temperature was high or low. The effect of lower relative humidity on increased cases was more pronounced below
relative humidity of about 70%; below this threshold, not only were the effects of humidity pronounced but also the
relationship between temperature and cases of the delta variant becomes apparent.
Conclusions: We suggest that the control of COVID-19 outbreaks, specifically those due to the delta variant, is par-
ticularly challenging during periods of the year with lower relative humidity and warmer temperatures. In addition to
vaccination, stronger implementation of other interventions such as mask-wearing and social distancing might need
to be considered during these higher risk periods.
Keywords: Meteorological factor, Climate, Humidity, Temperature, SARS-CoV-2, COVID-19, Australia
© The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the
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regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this
licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco
mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Open Access
*Correspondence: michael.ward@sydney.edu.au; zhj_zhang@fudan.edu.cn
1 Sydney School of Veterinary Science, The University of Sydney, Camden,
NSW, Australia
2 School of Public Health, Fudan University, Shanghai, China
Full list of author information is available at the end of the article

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Wardetal. Infectious Diseases of Poverty (2021) 10:139
Background
Severe acute respiratory coronavirus 2 (SARS-CoV-2),
the cause of the coronavirus disease 2019 (COVID-19)
pandemic, spreads among people predominantly via res-
piratory droplets and aerosols, as well as fomites [1] and
possibly fecal-oral [2]. Although the effect of weather
on the airborne spread of SARS-CoV-2 has been inves-
tigated in some detail [3], the conclusions are inconsist-
ent. Qi etal. [4] found that temperature had significantly
negative associations with COVID-19 incidence in China
using province-based data, but Guo et al. [5] showed
that warmer weather might not affect COVID-19 spread
based on city-scale data from China. In contrast, evi-
dence from Jakarta, Indonesia showed that increased
temperature was an environmental driver of COVID-19
outbreaks [6]; a similar conclusion was made in a global
study of 190 countries conducted on data from early in
the pandemic [7]. Similar contradictory results exist for
humidity [6, 8–11]. One explanation is that interactions
exist between temperature and humidity and their effect
on SARS-CoV-2 transmission, so that the roles of these
factors might be heterogenous depending on different
climatic zones [12]. But whether and how the interaction
among weather factors affects the spread of SARS-CoV-2
remains unclear.
Of more concern, the B.1.617.2 (delta) variant of
SARS-CoV-2, first detected in India in December 2020,
has spread throughout most of the world during 2021
to become the dominant strain of SARS-CoV-2 [13].
On 16 June, 2021 New South Wales Health was noti-
fied of a new COVID-19 case who was residing in Syd-
ney’s eastern suburbs. e case, a man in his 60s, had
no recent overseas travel history. However it was noted
that he worked as a driver, which included transporting
international flight crew [14]. Subsequently, an outbreak
of COVID-19 caused by local-transmission of the delta
variant commenced during early winter (June) in Syd-
ney. To our knowledge, no studies have reported the
relationship between weather and transmission of this
new variant of SARS-COV-2 specifically in the southern
hemisphere winter. us, the aim of the present study
was to describe the association between reported cases
ofCOVID-19 due to the delta variant and temperature
and relative humidity.
Whilst this study builds on previous research con-
ducted in Sydney in 2020, it also extends our knowledge
about how temperature and humidity interact in terms of
COVID-19 outbreaks, facilitating rational explanations
for previously inconsistent study results and the behavior
of a new SARS-CoV-2 variant in a predominantly suscep-
tible population during winter, a risk period during which
transmission is likely facilitated.
Methods
Case data
Case reports from the local health districts (LHDs) of
Sydney, Australia from the beginning of the SARS-CoV-2
delta variant outbreak on 16 June to 30 September 2021
were accessed [15]. COVID-19 surveillance is based on
testing (PCR on nasal and oral swabs) contacts of con-
firmed cases and also testing symptomatic patients. Prior
to the index case, the most recent locally-acquired case
had been identified on 5 May 2021. During the study
period, whole genome sequencing was used to identify
variants of concern on a proportion of samples from con-
firmed cases, and only the delta variant was found. More
broadly between 29 November 2020 and 2 October 2021,
Graphical Abstract
0
500
1,000
1,500
10.0012.00 14.00
Te mperature(°C)
Count
Humidity(%)
P10: 54.14
P20: 58.58
P30: 63.08
P40: 66.47
P50: 69.82
P60: 76.14
P70: 81.79
P80: 88.05
0
500
1,000
1,500
60.00 70.00 80.00 90.00
Humidity(%)
Count
Te mperature(°C
)
P10: 8.99
P20: 10.21
P30: 10.84
P40: 11.23
P50: 11.74
P60: 12.35
P70: 13.14
P80: 13.75
AB

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Wardetal. Infectious Diseases of Poverty (2021) 10:139
a total of 11,166 cases (about 21% of all cases) were sub-
ject to whole genome sequencing and of these 11,173
(> 99.9%) were identified as the delta variant; only 6
cases of the alpha variant and one case of the beta were
identified [16]. Although it is unlikely that all cases that
occurred during the study period were ascertained in the
surveillance system, this likely represents minor non-dif-
ferential bias.
ose cases whose infection source was reported to
be locally-acquired, and whose LHD of residence was
reported as Northern Sydney, South Eastern Sydney,
South Western Sydney, Sydney or Western Sydney LHD,
were included. A daily time-series of cases was created as
the sum of cases reported each day. Daily reports were
plotted and using a 7-day moving-average, the peak of
the outbreak (1181 cases) was identified on 10 Septem-
ber, 2021 (Fig.1). erefore, further analysis was focused
on the period from the beginning of the outbreak to its
peak, a period of 87days.
Descriptive analysis
Based on the reported LHD, cases were linked to weather
observation stations [17]. Daily observations of tempera-
ture (°C) and relative humidity (%) recorded at 9:00 am
and at 3:00pm during June to September were accessed
[17]. Additional series of daily differences between 9:00
am and 3:00pm temperature and 9:00 am and 3:00pm
relative humidity were created to indicate the varia-
tions. In addition, daily reported number of COVID-19
tests and number of COVID-19 vaccinations adminis-
tered [18], and the mobility index for Sydney [19], were
accessed and time-series created. ese variables were
included for descriptive purposes only, to provide con-
text regarding this outbreak of COVID-19 caused by the
SARS-CoV-2 delta variant.
Statistical modelling
e same method used in previous studies was applied
to investigate the relationship between reported cases
of COVID-19 and weather variables [4, 8, 9, 12, 20]. A
Spearman correlation (ρ) coefficient matrix was first
calculated to avoid multicollinearity among the predic-
tor variables, and a 14-day exponential moving aver-
age (EMA) was used to represent the effects of weather
factors. en, a univariable generalized additive model
(GAM) was fitted and variables with a P-value < 0.1 were
selected for multivariable analysis. Different multivari-
able analysis models were then fit to analyze the relation-
ship between the selected weather variables and cases of
COVID-19. Akaike information criterion (AIC) and like-
lihood ratio test were used to compare different models
and determine the best model. R4.0.1 software [21] was
used to perform all analyses.
A sensitivity analysis was performed by modifying the
EMA (14days to 7 or 21days) to verify the robustness of
model results.
Results
Epidemic characteristics
Between 16 June and 10 September 2021, 31,662 locally-
acquired cases with a residence within the five target
LHDs were notified, excluding 6 and 191 cases in which
infection was acquired interstate and overseas, respec-
tively. During this period, relative humidity was variable,
0
200
400
600
800
1,000
1,200
1,400
16-Jun 26-Jun6-Jul 16-Jul 26-Jul5-Aug 15-Aug 25-Aug 4-Sep14-Sep24-Sep
Number of reported COVID-19 cases
Date
Fig. 1 Time-series plot of cases of COVID-19 due to SARS-CoV-2 delta variant reported from Sydney, Australia during the period of 16 June and 30
September, 2021. The grey line indicates the 7-day moving average of reported cases, and the peak of the outbreak (10 September) is indicated by
the arrow

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Wardetal. Infectious Diseases of Poverty (2021) 10:139
ranging from < 30% to 98%, and temperatures were mild
(approximately 11–17 °C) (Table 1). e relationship
between 9:00 am temperature and reported cases, and
9:00 am relative humidity and reported cases, as exam-
ples are shown in Fig. 2. In this outbreak during the
period 16 June to 10 September there was a strong posi-
tive correlation between reported cases and the daily
number of vaccine doses administered [Spearman rank
correlation (ρ) = 0.81, P < 0.001] and daily number of
COVID-19 tests performed (ρ = 0.95, P < 0.001). ese
metrics increased from approximately 30,000 to 120,000
doses per day, and 17,000 to 148,000 tests per day, dur-
ing the period. e relationship between reported cases
and the mobility index for Sydney was strongly negative
(ρ = −0.86, P < 0.001); the mobility index dropped from
0.46 to 0.09 during the study period. us, there was a
strong response to the occurrence of this outbreak.
Relationship betweenCOVID‑19 andweather
Based on correlation coefficients (Table2), 9:00 am tem-
perature, 9:00 am humidity and the difference between
9:00 am and 3:00 pm humidity were selected for fur-
ther analysis; the latter did not show significance in uni-
variable modelling (P = 0.621; Table3). In multivariable
modelling, four models with 9:00 am humidity, 9:00 am
temperature or an interaction between these variables
were fit to the data (Table4). e model which included
the interaction term showed the best fit (Table5). Lower
humidity was a consistent driver of COVID-19 cases
caused by the SARS-CoV-2 delta variant, with a slight
modification due to a significant interaction term (from
−0.110 to −0.185, setting 9:00 am temperature at its
median value). In contrast, the impact of higher tem-
peratures on increased COVID-19 cases was mostly
affected by the interaction term [from 0.25 (significant)
to 0.04 (almost non-significant), setting 9:00 am humid-
ity at its median value]. ese exposure-response curves
indicate that when humidity is high there is no effect of
temperature on cases (Fig.3A). However, when humid-
ity is low, high 9:00 am temperatures are associated with
more cases. But it should be noted that the range of 9:00
am temperature values during the study period (2.5th–
97.5th percentile range: 7.3‒18.1°C) was relatively nar-
row and temperatures were mild, favorable conditions
for COVID-19 transmission. Relative humidity remained
negatively associated with cases no matter whether
the 9:00 am temperature was high or low. e effect of
lower relative humidity on increased cases was more
pronounced below relative humidity values of about 70%
(Fig.3B); below this threshold, not only are the effects of
humidity pronounced but also the relationship between
temperature and cases of the delta variant becomes
apparent (Fig.3A).
e sensitivity analysis confirmed that the results of
modelling were robust (Table6).
Discussion
During the outbreak of COVID-19 in Sydney, beginning
June 2021 and caused by the B.1.617.2 (delta) variant of
SARS-CoV-2, low humidity was consistently associated
with reported cases. Furthermore, and with implications
for the response to outbreaks caused by the delta variant,
we identified a threshold effect of low relative humidity
(< 70%) whereby warmer, drier conditions might promote
transmission of the SARS-CoV-2 delta variant. In addi-
tion to vaccination, stronger implementation of other
interventions—such as mask-wearing and social dis-
tancing—might need to be considered during these risk
periods to control outbreaks of COVID-19 caused by the
delta variant.
Previously, in 2020, we identified a relationship between
the original SARS-CoV-2 and relative humidity [8, 9]. In
these earlier studies, a 1% decrease in relative humidity
was predicted to increase cases in the range of 6–8%. In
contrast, in the current study in the univariable model
which included 9:00 am humidity, a 1% decrease in rela-
tive humidity was predicted to increase cases by 16.9%
(setting 9:00 am temperature at its median value).
Although outbreaks in Sydney in 2020 and 2021 cannot
be directly compared, results suggest that the influence
Table 1 Summary statistics of weather variables, Sydney, Australia from 16 June to 10 September, 2021, used in a study of an outbreak
of COVID-19 caused by the B.1.617.2 (delta) variant of SARS-CoV-2
Variables Minimum 2.5th percentile Median Mean 97.5th percentile Maximum
9:00 am humidity, % 32.7 49.6 69.8 72.1 96.6 98.2
3:00 pm humidity, % 21.3 27.8 47.2 50.9 91.7 98.4
9:00 am temperature, °C 7.1 7.3 11.8 12.1 18.1 21.0
3:00 pm temperature, °C 9.1 11.6 17.1 17.2 23.0 26.3
Humidity difference, % 0.3 3.0 24.8 23.1 43.1 53.6
Temperature difference, °C 0.1 0.5 5.0 5.2 9.2 11.4

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Wardetal. Infectious Diseases of Poverty (2021) 10:139
Fig. 2 Reported cases of COVID-19 caused by the B.1.617.2 (delta) variant of SARS-CoV-2, Sydney, Australia 16 June to 10 September 2021 (primary
axis) and 9:00 am temperature (°C) and 9:00 am relative humidity (%) (secondary axes)
Table 2 Spearman correlation coefficient matrix among weather variables used to model reported cases of COVID-19 caused by the
B.1.617.2 (delta) variant of SARS-CoV-2, Sydney, Australia from 16 June to 10 September, 2021
*Signicant bivariate correlation, P value < 0.05
Variables 9:00 am
temperature 9:00 am humidity 3:00pm
temperature 3:00pm humidity Temperature
dierence Humidity
dierence
9:00 am temperature 1 − 0.53* 0.64* − 0.33* − 0.15 − 0.10
9:00 am humidity ‒1− 0.31* 0.63* 0.11 0.24*
3:00 pm temperature ‒ ‒ 1− 0.57* 0.59* 0.42*
3:00 pm humidity ‒ ‒ ‒ 1− 0.47* − 0.41*
Temperature difference ‒ ‒ ‒ ‒ 1 0.69*
Humidity difference ‒ ‒ ‒ ‒ ‒ 1

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Wardetal. Infectious Diseases of Poverty (2021) 10:139
of humidity on transmission of the delta variant might be
greater than that for the original SARS-CoV-2. e SARS-
CoV-2 delta variant emerged recently and it has spread
globally. Reports suggest that it might be more than twice
as transmissible as the original SARS-CoV-2 that emerged
in 2019–2020 [22]. To our knowledge there are no pub-
lished studies specifically focusing on the relationship
between transmission of the delta variant and weather.
Further studies are needed to confirm the stronger asso-
ciation found in this study between transmission of the
delta variant and humidity, and whether increased trans-
missibility might be partly explained by weather factors.
is might provide useful information for policymakers to
control transmission of the delta variant, for example by
increasing indoors relative humidity to more than 70% in
high-risk environments during times of the year in which
transmission of this variant is favored.
Table 3 Univariable analysis of the association between weather
factors and reported cases of COVID-19 caused by the B.1.617.2
(delta) variant of SARS-CoV-2, Sydney, Australia from 16 June to
10 September, 2021
CI condence interval
Factor Estimate (95% CI) Likelihood ratio P‑value
9:00 am humidity − 0.15 (− 0.18, − 0.12) − 10.49 < 0.001
9:00 am tempera-
ture 0.68 (0.52, 0.85) 8.00 < 0.001
Humidity difference − 0.03 (− 0.15, 0.09) − 0.49 0.621
Table 4 Multivariable analysis of weather factors (14-day exponential moving average) and reported cases of COVID-19 caused by the
B.1.617.2 (delta) variant of SARS-CoV-2, Sydney, Australia from 16 June to 10 September, 2021
CI condence interval
Model Factor Estimate (95% CI) Likelihood ratio P‑value
1 Intercept 16.37 (14.31, 18.44) 15.52 < 0.001
9:00 am humidity − 0.15 (− 0.18, − 0.12) − 10.49 < 0.001
2 Intercept − 2.74 (− 4.76, − 0.73) − 2.66 0.008
9:00 am temperature 0.68 (0.52, 0.85) 8.00 < 0.001
3 Intercept 10.24 (5.03, 15.45) 3.86 < 0.001
9:00 am humidity − 0.11 (− 0.15, − 0.07) − 5.30 < 0.001
9:00 am temperature 0.25 (0.03, 0.47) 2.21 0.027
4Intercept − 34.29 (− 51.80, − 16.78) − 3.84 < 0.001
9:00 am humidity 0.52 (0.27, 0.76) 4.20 < 0.001
9:00 am temperature 4.23 (2.76, 5.70) 5.65 < 0.001
9:00 am humidity × 9:00 am tem-
perature
− 0.06 (− 0.08, − 0.04) − 5.34 < 0.001
Table 5 Comparison of three multivariable models of the association between weather factors (14-day exponential moving average)
and reported cases of COVID-19 caused by the B.1.617.2 (delta) variant of SARS-CoV-2, Sydney, Australia from 16 June to 10 September,
2021
AIC Akaike information criterion measure of model t
Model Parameter AIC Comparison Likelihood ratio P‑value
1 Intercept 1,137.50 ‒ ‒ ‒
9:00 am humidity
2 Intercept 1,151.62 Model 2 vs Model 3 18.53 < 0.001
9:00 am temperature
3 Intercept 1,135.09 Model 1 vs Model 3 4.40 0.036
9:00 am humidity
9:00 am temperature
4Intercept 1,119.64 Model 3 vs Model 4 17.46 < 0.001
9:00 am humidity
9:00 am temperature
9:00 am humidity × 9:00 am
temperature

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Wardetal. Infectious Diseases of Poverty (2021) 10:139
e effect of humidity on transmission of SARS-
CoV-2 virus has received substantial attention during
2020 and 2021, including studies conducted in Bangla-
desh [10] and China [4, 23, 24], and systematic reviews
on the topic [25]. However, no consistent conclusion
has been made. In several studies from 166 countries
[24], China and the US [26] and Bangladesh [10], high
relative humidity was found to be associated with a
reduction in the daily number of COVID-19 cases or
the effective reproductive number of SARS-CoV-2.
In 30 Chinese provincial capitals, Liu etal. [23] found
that low humidity likely favors SARS-CoV-2 transmis-
sion. In addition, using quantitative time-series analy-
sis techniques, Qi etal. [4] estimated that for every 1%
increase in relative humidity, daily confirmed cases
decreased by 11% to 22% when temperature was in the
range of 5.04‒8.2°C. In the current study, we also found
that humidity was negatively related to daily COVID-19
cases and was a stable driver of SARS-CoV-2 transmis-
sion, consistent with the above studies. However, the
0
500
1,000
10.00 12.00 14.00
Te mperature(°C)
Count
Humidity(%)
P10: 54.14
P20: 58.58
P30: 63.08
P40: 66.47
P50: 69.82
P60: 76.14
P70: 81.79
P80: 88.05
0
500
1,000
1,500
1,500
60.00 70.00 80.00 90.00
Humidity(%)
Count
Te mperature(°C)
P10: 8.99
P20: 10.21
P30: 10.84
P40: 11.23
P50: 11.74
P60: 12.35
P70: 13.14
P80: 13.75
AB
Fig. 3 Interaction plots between 9:00 am temperature and 9:00 am relative humidity in a model of reported cases (counts) of COVID-19 caused by
the B.1.617.2 (delta) variant of SARS-CoV-2, Sydney, Australia from 16 June to 10 September 2021. Interactions are shown using deciles of humidity
(left, A) and temperature (right, B). The different lines represent the percentiles indicated
Table 6 Sensitivity analysis for the comparison of three multivariable models of the association between weather factors (14-day
exponential moving average) and reported cases of COVID-19 caused by the B.1.617.2 (delta) variant of SARS-CoV-2, Sydney, Australia
from 16 June to 10 September, 2021
Model Factor Days used for exponential moving average
7‑day 14‑day 21‑day
Estimate (SE) Z P‑value Estimate (SE) Z P‑value Estimate (SE) Z P‑value
1 Intercept 14.59 (1.13) 12.89 < 0.001 16.37 (1.06) 15.52 < 0.001 17.30 (0.82) 21.16 < 0.001
9:00 am humidity − 0.13 (0.02) − 8.23 < 0.001 − 0.15 (0.01) − 10.49 < 0.001 − 0.16 (0.01) − 14.55 < 0.001
2 Intercept − 1.03 (0.05) − 22.3 < 0.001 − 2.74 (1.03) − 2.66 0.008 − 3.64 (0.86) − 4.23 < 0.001
9:00 am temperature 0.54 (0.003) 158.0 < 0.001 0.68 (0.09) 8.00 < 0.001 0.77 (0.07) 10.70 < 0.001
3 Intercept 3.89 (0.12) 33.18 < 0.001 10.24 (2.66) 3.86 < 0.001 11.28 (2.29) 4.93 < 0.001
9:00 am humidity − 0.05 (0.001) − 44.97 < 0.001 − 0.11 (0.02) − 5.30 < 0.001 − 0.12 (0.02) − 7.00 < 0.001
9:00 am temperature 0.40 (0.005) 86.44 < 0.001 0.25 (0.11) 2.21 0.027 0.25 (0.10) 2.55 0.011
4Intercept − 8.33 (8.05) − 1.03 0.301 − 34.29 (8.93) − 3.84 < 0.001 − 42.85 (7.65) − 5.60 < 0.001
9:00 am humidity 0.13 (0.11) 1.22 0.224 0.52 (0.12) 4.20 < 0.001 0.66 (0.11) 6.10 < 0.001
9:00 am temperature 1.67 (0.66) 2.55 0.011 4.23 (0.75) 5.65 < 0.001 5.17 (0.66) 7.83 < 0.001
9:00 am humid-
ity × 9:00 am tem-
perature
− 0.02 (0.01) − 2.05 0.040 − 0.06 (0.01) − 5.34 < 0.001 − 0.07 (0.01) − 7.48 < 0.001

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Wardetal. Infectious Diseases of Poverty (2021) 10:139
observed relationship between COVID-19 cases and
humidity has not always been consistent; for exam-
ple, this relationship was found to be heterogeneous
between different cities in China [12], and in a global
study of 190 countries an inverse J-shaped relationship
was found between relative humidity and COVID-19
incidence, in which risk was greatest at 72% relative
humidity [7]. It is likely that a range of other factors
influences the relationship between transmission of
SARS-CoV-2 and humidity, particularly climatic zone.
Also, the various control strategies implemented—such
as mandatory mask-wearing, social distancing, testing
and vaccination—make characterization of the rela-
tionship between weather factors and SARS-CoV-2
transmission within outbreak situations challenging.
e relationship between temperature and COVID-
19 cases has not yet been fully characterized. In a study
in China, no relationship between temperature and
COVID-19 cases was found [27]. A negative correla-
tion between temperature and COVID-19 cases—less
transmission at higher temperatures—has been reported
by Xie etal. [28] and Notari etal. [29]. However, in our
study, a positive correlation between temperature and
COVID-19 cases caused by the SARS-CoV-2 delta vari-
ant was found, but only in the situation when relative
humidity values are around 70% or lower. is is consist-
ent with positive associations between average tempera-
ture and daily COVID-19 cases reported in nine Asian
cities [30].
e interactions among weather factors might pro-
vide a reasonable explanation for the above contradic-
tory results. Previous research has suggested a potential
interaction between relative humidity and temperature
and COVID-19 case reports, but the exact mechanism of
the interaction is unclear [4]. is might be due to both
temperature and humidity affecting the function of the
respiratory mucosa as a barrier to the virus and infec-
tion, and hence affecting the spread of SARS-CoV-2 [31].
e same phenomenon has been previously described in
influenza studies: temperature was inversely associated
with influenza and the relationship could be modified by
humidity [32]. Hence the suggestion has been made that
COVID-19 might develop into a seasonal disease [33].
However, studies on the correlation between weather
factors such as temperature and humidity and their
interaction and transmission of the SARS-CoV-2 delta
variant have not yet been reported, and further research
is urgently needed to support policy and control. Our
results suggest that weather could be a more important
consideration during outbreaks of this delta variant.
erefore, it is important in future research to focus on
those specific periods in which transmission might be
increased to better understand the mechanisms involved
and how public health advice and interventions might
be targeted. Given the advances that have been made in
forecasting seasonal influenza outbreaks [34], the same
approach can be anticipated for seasonal COVID-19
once the mechanisms of spread are better understood.
In this study we assumed that cases were infected
within their LHD and that temperature and humid-
ity measured at meteorological recording stations was a
proxy for the conditions experienced when transmission
occurred. More precise measures of exposure are diffi-
cult to access in the field and within an outbreak setting.
Temperature and humidity measurements represent out-
doors conditions, and so are a proxy for the conditions
experienced by the population exposed to infection. We
also assumed that case reporting in this outbreak was
high and that differential bias was not present. During
this outbreak high levels of testing occurred—an aver-
age daily testing rate of > 80,000 tests were reported—and
confirmation rates remained consistent [16], so reporting
bias is likely small. Although observational studies such
as the current one suffer from measurement and infor-
mation biases, the coherence of evidence from a growing
number of studies strengthens the hypothesis that SARS-
CoV-2 transmission is influenced by weather.
Conclusions
To date, countries in the northern hemisphere have not
experienced large outbreaks of COVID-19 caused by the
delta variant of SARS-CoV-2 during the winter months.
e present study contributes to our growing knowledge
of the relationship between SARS-CoV-2 transmission
and weather, and specifically the transmission of the delta
variant, which is currently the dominant variant globally.
It suggests that both relative humidity and temperature
play a role via an interactive effect. In similar climatic
zones to this study in which low humidity and mild tem-
peratures are experienced during the winter months, the
control of the delta variant might be more difficult than
previously expected. Increasing the ambient humidity
(e.g., > 70%) might be a useful alternative measurement
for reducing the transmission risk of the delta variant of
SARS-CoV-2 in the future, and during periods of antici-
pated increased risk (such as the winter months) addi-
tional disease control interventions might be warranted.
Abbreviations
AIC: Akaike information criterion; EMA: Exponential moving average; GAM:
Generalized additive model; LHD: Local health district; ρ: Spearman rank
correlation.

Page 9 of 10
Wardetal. Infectious Diseases of Poverty (2021) 10:139
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s40249- 021- 00926-0.
Additional le1. Data used to analyse the association between weather
variables and reported cases of COVID-19 caused by the B.1.617.2 (delta)
variant of SARS-CoV-2 in Sydney, Australia between June and September
2021.
Acknowledgements
NSW Ministry of Health is thanked for freely making available COVID-19 case
notification data.
Authors’ contributions
MPW conceptualized the study, curated the data, designed the methodology,
interpreted analysis and drafted, reviewed and edited the paper. YL undertook
data analysis and drafted the paper. SX undertook data visualization and
analysis. ZZ conceptualized the study, designed the methodology, interpreted
analysis and drafted, reviewed and edited the paper. All authors read and
approved the final manuscript.
Funding
ZZ is supported by the Major Project of Scientific and Technical Winter
Olympics from National Key Research and Development Program of China
(2021YFF0306000), Public Health Talents Training Program of Shanghai Munici-
pality (GWV-10.2-XD21), and the National Natural Science Foundation of China
(81973102).
Availability of data and materials
All data analysed during this study are included in this published article (and
its Additional files).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Sydney School of Veterinary Science, The University of Sydney, Camden,
NSW, Australia. 2 School of Public Health, Fudan University, Shanghai, China.
3 Department of HIV/STD Prevention and Control, Shanghai Municipal Centre
for Disease Control and Prevention, Shanghai, China.
Received: 21 October 2021 Accepted: 15 December 2021
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