2025 CMS Winter Meeting
Toronto, Dec 5 - 8, 2025
AI and Mathematical Technologies for Decision Support in Public Health
Org: Qi Deng, Seyed Moghades and Jianhong Wu (York University)
Org: Qi Deng, Seyed Moghades and Jianhong Wu (York University)
- CHRIS BAUCH, University of Waterloo
- DAVID BUCKERIDGE, McGill University
- MONICA COJOCARU, University of Guelph
- QI DENG, York University
- ABBAS GHASEMI, Toronto Metropolitan University
- MICHAEL Y. LI, University of Alberta
- JUNLING MA, University of Victoria
- BOUCHRA NASRI, Université de Montréal
- NATHANIEL OSGOOD, University of Saskatchewan
- AFFAN SHOUKAT, University of Regina
- EDWARD THOMMES, Sanofi
- WOLDEGEBRIEL ASSEFA WOLDEGERIMA, York University
- SICHENG ZHAO, McMaster University
Improving infectious disease prevalence estimation and parameter inference using number of tests and positivity data [PDF]
- DAVID BUCKERIDGE, McGill University
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Prevalence of infection is a critical variable for modeling infectious dynamics and public health decision-making. However, estimating true prevalence from surveillance data remains challenging. Here we discuss three novel attempts to model testing mechanism using the beta-distribution, hazard ratios and odds ratios respectively. These methods aim to link prevalence with the number of tests, test positivity, and test characteristics at each data point in a robust, flexible, and theoretically justified manner. We further present a data-fitting framework based on the odds ratio approach and demonstrate its performance using simulated datasets as a proof of concept.