Weather variability and transmissibility of COVID-19: a time series analysis based on effective reproductive number

Abstract

COVID-19 is causing a significant burden on medical and healthcare resources globally due to high numbers of hospitalisations and deaths recorded as the pandemic continues. This research aims to assess the effects of climate factors (i.e., daily average temperature and average relative humidity) on effective reproductive number of COVID-19 outbreak in Wuhan, China during the early stage of the 2 outbreak. Our research showed that effective reproductive number of COVID-19 will increase by 7.6% (95% Confidence Interval: 5.4% ~ 9.8%) per 1°C drop in mean temperature at prior moving average of 0-8 days lag in Wuhan, China. Our results indicate temperature was negatively associated with COVID-19 transmissibility during early stages of the outbreak in Wuhan, suggesting temperature is likely to effect COVID-19 transmission. These results suggest increased precautions should be taken in the colder seasons to reduce COVID-19 transmission in the future, based on past success in controlling the pandemic in Wuhan, China.

Do Weather Conditions Still Have an Impact on the COVID-19 Pandemic? An Observation of the Mid-2022 COVID-19 Peak in Taiwan

“…Although the prevailing literature generally posits that higher temperatures have a detrimental effect on COVID-19 transmission, leading to fewer confirmed cases [33][34][35], our findings present a different perspective. Our study identified a significant positive correlation between Tmax and confirmed COVID-19 cases across Taiwan, aligning with prior findings [7,8,15,22,36].…”

The impact of weather on the spread of COVID-19. The case of the two largest cities in Greece

“…have relied on different meteorological data (temperature, absolute/relative humidity, wind speed, precipitation, etc.) and different methodological approaches, such as the generalized linear model (GLM) [26, 27,45] and the generalized additive model (GAM) [28, 30,32,46]. In analysing their data, they used various statistical methods and analysis techniques, such as Spearman correlation analysis [38,47], Pearson correlation analysis [33], and correlation and regression analysis [48,49] In our research, the effects of weather on the spread of COVID-19 were estimated by a generalized linear model (GLM) with a Poisson distribution [50].…”

“…Most studies concluded that an increase in temperature and humidity led to a decrease in the rate of disease spread [25][26][27][28][29][30][31]. For example, research conducted in 166 countries worldwide showed that an increase in temperature (+ 1°C) and relative humidity (+ 1%) was associated with a decrease in daily cases and deaths [30].…”

Future perspectives of emerging infectious diseases control: A One Health approach

“…The Great Sydney Area restrictions commenced on June 26th 2021, and first Omicron case detected on 3rd December 2021. Bayesian Estimation theory was used to estimate R eff with a 10-day averaging window [ 3 ]. …”

“…Climate change contributes to the emergence of novel infectious diseases and the spread of existing infectious diseases through the health impacts of climate variability - temperature extremes, and extreme climate and weather events [ 6 ]. Previous research suggests weather, particularly variations in temperature and humidity, contributed to the transmission of COVID-19 [ 7 ]. Human activities contributing to environmental change, such as deforestation, intensive agricultural practices, biodiversity loss and increasing interactions with wild animals and live animal markets, increase the likelihood of emerging zoonoses and spillover events [ 8 ].…”