Assessment of Temperature Variables in Modeling Global Mapping and Distribution of COVID-19: A key factor in identification of risk region

Since the outbreak of COVID-19 in Wuhan, China, in December 2019, several works have been done and published on the role of environmental factors, especially climate in general and temperature in particular on the spread of the virus, some of which are contradictory. It has also been observed that most mapping has been overgeneralized without identifying the core infection areas. This work creatively uses distribution models to map the spread and infectivity of COVID-19 using biologically relevant temperature variables. We built ensemble COVID-19 global distribution models by fitting the selected temperature variables with over 650,000 occurrence data of COVID -19 across the globe; the ensemble models combined three algorithms: Maximum Entropy (Maxent), Generalized Linear Model (GLM), and Random Forest (RF) and was implemented in R package “SSDM†using a simple average of each SDM and display in Arc Map. Results show that the mean temperature of the coldest quarter (0-15°c) and annual mean temperature (10-22°c) are the main drivers of the virus’s spread. These thresholds defined the level of risk to COVID-19 and were scales between 0 -1, with 0 being low or no risk and one the highest risk. Analysis of the regions at risk by the proportion of areas shows that Western Europe, United States, and mainland China had the most elevated regions under very high risk. At the same time, Africa, except for South Africa and Maghreb nations, was relatively at low risk. Despite few data obtained from Canada, the model predicted a high-risk zone in the eastern provinces of Toronto and Quebec and south of British Columbia. Overgeneralization in mapping was resolved in this work as a high-risk cluster was conspicuously highlighted even in an area of presumably low risk.