Simulating and Predicting the Active Cases and Hospitalization Considering the Second Wave of COVID-19

Coronavirus disease 2019 (COVID-19) has been an ongoing threat to the world's health system. Millions of people died all over the world because of this deadly virus outbreak. Although health sectors are equipped with modern technologies yet struggling every day to control this outbreak. However, predicting the active COVID-19 cases and hospitalization in advance can be helpful to minimize the catastrophe of this persistent outbreak. This study proposed a novel Agent-based modelling (ABM) framework based on various temporal and non-pharmaceuticals parameters to predict active cases and hospitalization cases. We evaluated the model's performance based on COVID-19 data of Windsor-Essex county region of Ontario, Canada, and eventually achieved satisfactory results in predicting active cases and hospitalization. Experimental results have demonstrated that the simulations provide helpful information that could help take advanced steps to cover up for the shortage in hospital resources and take necessary steps to reduce the number of infections.