Dalla Lana School of Public Health

Links

• https://www.eventbrite.ca/e/catalyst-seminar-series-december-tickets-1661371369489?aff=oddtdtcreator

Presented by the Institute of Health Emergencies & Pandemics …

Join us for Catalyst Series, a dynamic seminar series showcasing groundbreaking research funded by the Institute of Health Emergencies & Pandemics Catalyst and Research Development Grants. Since 2023, IfP has provided over $1 million to research teams at the University of Toronto, to support collaborative, interdisciplinary research projects. These presentations spotlight the innovative work of interdisciplinary teams tackling urgent challenges in pandemic preparedness, response, and recovery.

Projects Highlighted:

Transforming Patient and Provider Experiences into Actionable Insights for Pandemic Readiness, Resilience, and Recovery with an AI-Enabled Hospital System

• Presenter: Zahra Shakeri (Dalla Lana School of Public Health)
• Hospitals are essential in supporting communities during public health crises, and patients’ experiences provide important insight into the broader challenges shaped by social determinants of health (SDoH). This research applies AI-supported analysis to more than 111,000 open-text comments collected from Ontario hospitals between 2017 and 2021. By comparing feedback before and during the COVID-19 pandemic, we identified how barriers related to access, equity, and communication affected patient care and intensified under pandemic pressures. The findings point to opportunities for hospitals to strengthen continuity of care, improve responsiveness to diverse populations, and build public trust. In future phases, this work will contribute to the development of an intelligent, patient-centered system that incorporates SDoH into hospital preparedness and recovery planning. Such a system can advance health education, counter misinformation, and guide preventive actions, supporting a more resilient and equitable healthcare system for Canadians.

Predicting Outbreaks in Shelters: Development and data-driven testing of a computational model of airborne SARS-CoV-2 spread

• Presenter: Swetaprovo Chaudhuri (Faculty of Applied Science & Engineering)
• 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 developed 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 built and tested 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 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 supports 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.