Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms

The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.