Disease diffusion modelling to understand the transmission dynamics of COVID-19 in Toronto

Keywords: COVID-19, Disease Diffusion Modeling, Transmission Dynamics, Toronto
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 09:40 AM (Eastern Time (US & Canada)) - 2/25/2022 11:00 AM (Eastern Time (US & Canada))
Room: Virtual 8

Abstract

Existing geospatial diffusion models in infectious disease surveillance often do not provide direction and magnitude of transmission patterns of the disease, valuable information required for controlling a highly transmittable disease like COVID-19. We developed a novel spatial model to understand spatial diffusion of the COVID-19 transmissions in Toronto, specifically, how the disease was transmitted to the surrounding neighbourhoods over time in the study area. We used COVID-19 cases from January 2020 to July 2021 and the population data of the 2016 census. In the diffusion model, if the observed number of cases in a week in a neighbourhood was equal to or more than the average weekly number of cases of all neighbourhoods, it was considered an outbreak event in the neighbourhood. The starting point of the outbreak event was the source, where the path indicates transmissions of the disease in space over time. The resulting maps show spatial diffusion of COVID-19, specifically the direction and magnitude of the COVID-19 transmissions in the neighbourhoods of Toronto. Some affected neighbourhoods did not transmit the disease to any neighbourhood, while some other affected neighbourhoods transmitted the disease to a maximum of five neighbourhoods. In our model, we used useful parameters for tracking the diffusion dynamics of COVID-19 in Toronto. The findings from our model suggest that earlier intervention would have been critical for controlling the spread of the COVID-19 in the city. Understanding the spatial diffusion of a disease may effectively help in controlling the disease.