[CARMA 2022]

The COVID-19 pandemic has required governments to introduce various public health measures in order to contain and manage the pandemic’s unprecedented impacts in terms of illnesses and deaths. This study analyzes the spatiotemporal distribution of emergency incidents in Vaughan, a medium-sized city in the Canadian province of Ontario, comparing occurrences prior to and during the pandemic. Emergency calls received and responded to by the Vaughan Fire and Rescue Service were examined using spatial density and emerging hotspot analysis based on 11 periods of various public health measures and restrictions set in place from 17 March 2020 to 15 July 2021, as compared with corresponding pre-pandemic periods in the preceding three years (2017-2019). The resulting analyses show significant spatiotemporal changes in emergency incident patterns, particularly during periods of more stringent public health measures such as ‘stay at home’ orders or lockdowns of nonessential business establishments. Results of the study could provide useful insights for managing emergency service resources and operations during public health emergencies.

[Royal Society Open Science, 5 October 2022]

We present a simple model for the spread of an infection that incorporates spatial variability in population density. Starting from first-principle considerations, we explore how a novel partial differential equation with state-dependent diffusion can be obtained. This model exhibits higher infection rates in the areas of higher population density—a feature that we argue to be consistent with epidemiological observations. The model also exhibits an infection wave, the speed of which varies with population density. In addition, we demonstrate the possibility that an infection can ‘jump’ (i.e. tunnel) across areas of low population density towards areas of high population density. We briefly touch upon the data reported for coronavirus spread in the Canadian province of Nova Scotia as a case example with a number of qualitatively similar features as our model. Lastly, we propose a number of generalizations of the model towards future studies.

[Urban Science, 12 June 2023]

Cities have experienced different realities during the COVID-19 pandemic due to its impacts and public health measures undertaken to respond to and manage the pandemic. These measures revealed significant implications for municipal functions, particularly emergency services. The aim of this study is to examine the spatiotemporal distribution of emergency calls during different stages/periods of the pandemic in the City of Vaughan, Canada, using spatial density and the emerging hotspot analysis. The Vaughan Fire and Rescue Service (VFRS) provided the dataset of all emergency calls responded to within the City of Vaughan for the period of 1 January 2017 to 15 July 2021. The dataset was divided according to 11 periods during the pandemic, each period associated with certain levels of public health restrictions. A spatial analysis was carried out by converting the data into shapefiles using geographic coordinates of each call. Study findings show significant spatiotemporal changes in patterns of emergency calls during the pandemic, particularly during more stringent public health measures such as lockdowns and closures of nonessential businesses. The results could provide useful information for both resource management in emergency services as well as understanding the underlying causes of such patterns.