Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis

Abstract

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.

Climatic factors influence the spread of COVID-19 in Russia

“…It might, therefore, appear that COVID-19 needs a 4°C of a minimum level of temperature for smooth transmission. Also, in the temperate and subtropical regions, COVID-19 transmission was lower when the temperature remains less than 10°C (Pramanik et. al.…”

Mapping the distributions of blood-sucking mites and mite-borne agents in China: a modeling study

“…The BRT model, a machine learning algorithm, has been widely used for risk mapping of infectious diseases such as leishmaniasis [ 44 ], avian influenza [ 45 ] and even COVID-19 [ 46 ], due to its advantages in allowing for nonlinear relationships between outcomes and covariates and multicollinearity among covariates [ 47 ]. Based on BRT models, estimated ecological niches for mites are complex, and the key predictors differ even within the same genus.…”

A district-level susceptibility and vulnerability assessment of the COVID-19 pandemic's footprint in India

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