A statistical framework for reconstructing epidemic curves
Justin Slater, PhD
Assistant Professor
Department of Mathematics & Statistics |
University of Guelph
WHEN:聽Wednesday, February 26, 2025, from 3:30 to 4:30 p.m.
WHERE:聽Hybrid | 2001 不良研究所 College Avenue, Room 1140;
NOTE:聽Justin Slater will be presenting from Guelph
Abstract
Estimating the number of individuals who have had an infectious disease is essential for understanding disease burden, yet this remains challenging as all sources of surveillance data come with their own biases. A comprehensive approach must integrate reported cases, wastewater surveillance, and serosurvey data while addressing biases in each source. In this talk, I present a flexible Bayesian framework that (i) models under-reporting using approximations of count-valued state-space models, (ii) accounts for noisy wastewater signals with differentiable Gaussian processes, and (iii) leverages serosurvey data both for informative priors and model validation. I demonstrate this approach by reconstructing epidemic curves in Toronto and New Zealand, highlighting insights gained and challenges encountered.
Speaker Bio
Dr. Justin Slater is an assistant professor of statistics and data science at the University of Guelph. He received his PhD in Statistical Sciences from the University of Toronto in 2023, supervised by Drs. Patrick Brown and Jeffrey Rosenthal. He is the recent recipient of the Banting-CANSSI discovery award in Biostatistics in 2024. His research focuses on Bayesian methods in biostatistics/epidemiology. Presently, he is working on problems in both contagious infectious diseases and agent-based methods for modelling viral hepatitis. You can read more about his work at .