Impact of climatic, demographic and disease control factors on the transmission dynamics of COVID-19 in large cities worldwide

Abstract

Approximately a year into the COVID-19 pandemic caused by the SARS-CoV-2 virus, many countries have seen additional “waves” of infections, especially in the temperate northern hemisphere. Other vulnerable regions, such as South Africa and several parts of South America have also seen cases rise, further impacting local economies and livelihoods. Despite substantial research efforts to date, it remains unresolved as to whether COVID-19 transmission has the same sensitivity to climate observed for other common respiratory viruses such as seasonal influenza. Here, we look for empirical evidence of seasonality using a robust estimation framework. For 359 large cities across the world, we estimated the basic reproduction number (R 0 ) using logistic growth curves fitted to cumulative case data. We then assess evidence for association with climatic variables through ordinary least squares (OLS) regression. We find evidence of seasonality, with lower R 0 within cities experiencing greater surface radiation (coefficient = −0.005, p ≤ 0.001), after adjusting for city-level variation in demographic and disease control factors. Additionally, we find association between R 0 and temperature during the early phase of the epidemic in China. However, climatic variables had much weaker explanatory power compared to socioeconomic and disease control factors. Overall, we detect a weak signal of climate variables on COVID-19 transmission. Rates of transmission and health burden of the continuing pandemic will be ultimately determined by population factors and disease control policies.

Potential Contribution of Climate Conditions on COVID-19 Pandemic Transmission over West and North African Countries

“…In other terms, climatic variables have much weaker explanatory power compared to non-climatic factors. Such findings are also consistent with previous studies available in the literature (e.g., [32,58]), supporting the hypothesis that socio-economic and epidemic response factors explained the greatest proportion of variance in the COVID-19 cases estimated. Our results also support the WHO's guidelines not to raise expectations that the COVID-19 outbreak will significantly slow because of changes to climate conditions alone.…”

“…Note that due to a lack of sufficient data for the considered countries, the non-climatic factors such as socio-economic, epidemic response, demographic, and geographic factors are not modeled. This approach is similar to that one used in [32].…”

“…The five most affected countries are South Africa, Algeria, Morocco, Egypt, and Cameroon, with a very slow infection rate compared to the mid-latitudes or South American countries (see Figure 1). Note that even the statistics on the total number of cases in Africa might also be affected by the number of tests, the reliability of reporting the results, and the first establishment of the arrival of the virus in each area [31,32], a mix of socio-economic factors interacting with climate factors is highly expected to contribute to slow down this transmission pattern seen in Africa. In the opening statement of the COVID-19 Press Conference on 24 September 2020 Remarks, the WHO Regional Director for Africa confirmed that relatively low infections had marked COVID-19 transmission in Africa due to a variety of socio-economic factors and early and robust public health measures taken by governments across the region [33].…”

How Is COVID-19 Affected by Weather? Metaregression of 158 Studies and Recommendations for Best Practices in Future Research

“…The reason why 2,491 valid correlation coefficients were obtained instead of 2,777 observations is that some of the original studies did not provide sufficient information on the specific modelling factors they used. For example, some studies did not mention the significance level (e.g., Ghosh et al 2020;Lin et al 2020;Daneshvar et al 2021); other studies either did not mention the number of observation days or time period (e.g., Wu et al 2020;Metelmann et al 2021;Pan et al 2021). Thus, these correlation coefficients were excluded from the meta-regression analysis.…”

Environment and COVID-19 incidence: A critical review

“…Generally, in geographical terms, environment factors are categorized into natural environmental factors and human environmental factors ( Wu et al, 2021 ). Some worldwide studies have indicated that both natural and human environmental factors are crucial for COVID-19 transmission and the SARS-CoV-2 virus ( Fronteira et al, 2021 ; Metelmann et al, 2021 ; Rahimi et al, 2021 ; Srivastava, 2021 ). As an airborne transmission pandemic, SARS-CoV-2 and the severity of COVID-19 infection have been proven to be affected by climate conditions and air pollutants ( Domingo et al, 2020 ; Al Huraimel et al, 2020 ; Nottmeyer and Sera, 2021 ).…”