Geocomputational Approach to Simulate and Understand the Spatial Dynamics of COVID-19 Spread in the City of Montreal, QC, Canada

Abstract

Throughout history, pandemics have forced societies to think beyond typical management and control protocols. The main goals of this study were to simulate and understand the spatial dynamics of COVID-19 spread and assess the efficacy of two policy measures in Montreal, Canada, to mitigate the COVID-19 outbreak. We simulated the COVID-19 outbreak using a Geographical Information System (GIS)-based agent-based model (ABM) and two management scenarios as follows: (1) human mobility reduction; and (2) observation of self-isolation. The ABM description followed the ODD (Overview, Design concepts, Details) protocol. Our simulation experiments indicated that the mainstream of COVID-19 transmissions (i.e., approximately 90.34%) occurred in public places. Besides, the results indicated that the rules aiming to reduce population mobility, led to a reduction of about 63 infected people each week, on average. Furthermore, our scenarios revealed that if instead of 42% (i.e., the adjusted value in the calibration), 10%, 20%, and 30% of infectious people had followed the self-isolation measure, the number of infected people would have risen by approximately 259, 207, and 83 more each week, on average, respectively. The map of critical locations of COVID-19 spreading resulted from our modeling and the evaluated effectiveness of two control measures on the COVID-19 outbreak could assist health policymakers to navigate through the pandemic.