The study is the first attempt to assess the role of climatic predictors in the rise of COVID-19 intensity in the Russian climatic region. The study used the Random Forest algorithm to understand the underlying associations and monthly scenarios. The results show that temperature seasonality (29.2 ± 0.9%) has the highest contribution for COVID-19 transmission in the humid continental region. In comparison, the diurnal temperature range (26.8 ± 0.4%) and temperature seasonality (14.6 ± 0.8%) had the highest impacts in the sub-arctic region. Our results also show that September and October have favorable climatic conditions for the COVID-19 spread in the sub-arctic and humid continental regions, respectively. From June to August, the high favorable zone for the spread of the disease will shift towards the sub-arctic region from the humid continental region. The study suggests that the government should implement strict measures for these months to prevent the second wave of COVID-19 outbreak in Russia.
We investigate the climatic influence on COVID-19 transmission risks in 228 cities globally across three climatic zones. The results, based on the application of a Boosted Regression Tree algorithm method, show that average temperature and average relative humidity explain significant variations in COVID-19 transmission across temperate and subtropical regions, whereas in the tropical region, the average diurnal temperature range and temperature seasonality significantly predict the infection outbreak. The number of positive cases showed a decrease sharply above an average temperature of 10°C in the cities of France, Turkey, the US, the UK, and Germany. Among the tropical countries, COVID-19 in Indian cities is most affected by mean diurnal temperature, and those in Brazil by temperature seasonality. The findings have implications on public health interventions, and contribute to the ongoing scientific and policy discourse on the complex interplay of climatic factors determining the risks of COVID-19 transmission.
This study examines the association between community transmission of COVID-19 cases and climatic predictors, considering travel information and annual parasite index across the three climatic zones, i.e., tropical, subtropical, and temperate. A Boosted Regression Tree model has been employed to understand the association between the COVID-19 cases. The results show that average temperature and average relative humidity are the major contributors in explaining the differentials of COVID-19 transmission in temperate and subtropical regions whereas the mean diurnal temperature range and temperature seasonality are the most significant determinants in tropical regions. The average temperature is the most influential factor affecting the number of COVID-19 cases in France, Turkey, the US, the UK, and Germany, and the cases decrease sharply above 10oC. Among the tropical countries, India found to be most affected by mean diurnal temperature, and Brazil fazed by temperature seasonality. Most of the temperate countries like France, USA, Turkey, UK, and Germany with an average temperature between 5-12oC had high number of COVID-19 cases. The findings are expected to add to the ongoing debates on the influence of climatic factors influencing the number of COVID-19 cases and could help researchers and policymakers to make appropriate decisions for preventing the spread.
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