| Summary: | A viral infection which is named as Coronavirus disease 2019 (COVID-19) is triggered by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To date, almost two million cases and over 100,000 deaths from the disease caused by this virus were reported worldwide. The environmental and meteorological factors are claimed to stimulate the spread of the virus in which the transmissibility in terms of climatic fluctuations increases exponentially with high humidity and low temperature. In an attempt to understand this epidemic, there is a need to investigate the factors that could impact the spread and death of COVID-19. We, therefore, proposed to investigate global geographical climate impacts on the COVID-19 spread and death in Asia and America. The Artificial Neural Network (ANN) is a network that seeks to replicate neuronal functionality in the human brain. It is the preferred instrument for several predictive applications of data mining, due to its strength, versatility, and simplicity. A dataset of COVID-19 cases and deaths revealed from 49 states in America and 41 countries in Asia during April 2020 were tested. Nine covariates were used in the networks which are Cases, Death, High Temperature, Low Temperature, Average Temperature, Population, and Percentage of Cases over Population, Percentage of Death over Population, and Total Cases. Based on the analysis conducted, the global geographic climate is observed to have the least impacts on the COVID-19 spread and death in Asia and America particularly. However, different results could be reflected by different datasets used in the future. © 2021 Shafaf Ibrahim et al.
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