A Bio-Inspired Neural Network for Modelling COVID-19 Transmission in Canada

The 2019 novel coronavirus (COVID-19) has turned the world into turmoil and variant of the virus keeps emerging and creating new surges in cases. Mathematical modelling of infectious diseases can reveal how diseases spread, forecast the future course of an outbreak, and in practice, guide the management of public health care to effectively control the disease. This paper presents a nonlinear dynamic model to forecast the potential upsurge of COVID-19 transmission cases. The developed model can be used to forecast the trends of COVID-19 case rates, prepare the health system for the projected number of patients, and evaluate the effectiveness of public health measures such as travel restrictions, social distancing and quarantine.