Predicting outbreaks in shelters: development and data-driven testing of a computational model of airborne SARS-CoV-2 spread

Swetaprovo Chaudhuri, Faculty of Applied Science & Engineering; Sharmistha Mishra, Temerty Faculty of Medicine; Vijaya Kumar Murty, Faculty of Arts & Science; Stephen Hwang, Temerty Faculty of Medicine

Shelters face the perfect storm during epidemics like COVID. This is in part due to the density of shared living quarters in shelters and high turnover within shelters. One barrier to rapidly mobilizing and activating a response in shelters was that front-line teams were not sure how big a shelter-outbreak could get. We propose to develop an analytic and computer model that shelter and public health teams could use to predict (1) the chance of an outbreak occurring; (2) how many people may get infected in the shelter after one case is found; and (3) what it would take to shrink the size of an outbreak once started or prevent an outbreak from ever starting. Our model uses dynamics of airflow to capture key factors that shape airborne virus transmission related to physical space, crowding, ventilation – alongside the biological properties of the virus itself. We will build and test the model with real-world SARS-CoV-2 data in Toronto from surveillance, shelters, and persons experiencing homelessness as part of a cohort study. The model can then be used to test the impact of intervention measures that could be activated after the detection of the first case in a shelter, or what structural measures need to be put into place to prevent outbreaks before they occur. The model would support public health and front-line teams at shelters in their readiness during the current and evolving SARS-CoV-2 pandemic - to predict, react, but also to prepare.