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DESCRIPTION:Justin Slater\, PhD\n\nAssistant Professor\n	Department of Mathe
 matics & Statistics |\n	University of Guelph\n\nWHEN: Wednesday\, February 
 26\, 2025\, from 3:30 to 4:30 p.m.\n	WHERE: Hybrid | 2001 McGill College Av
 enue\, Room 1140\; Zoom\n	NOTE: Justin Slater will be presenting from Guelp
 h\n\nAbstract\n\nEstimating the number of individuals who have had an infe
 ctious disease is essential for understanding disease burden\, yet this re
 mains challenging as all sources of surveillance data come with their own 
 biases. A comprehensive approach must integrate reported cases\, wastewate
 r surveillance\, and serosurvey data while addressing biases in each sourc
 e. 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 pr
 ocesses\, and (iii) leverages serosurvey data both for informative priors 
 and model validation. I demonstrate this approach by reconstructing epidem
 ic curves in Toronto and New Zealand\, highlighting insights gained and ch
 allenges encountered.\n\nSpeaker Bio\n\nDr. Justin Slater is an assistant 
 professor of statistics and data science at the University of Guelph. He r
 eceived his PhD in Statistical Sciences from the University of Toronto in 
 2023\, supervised by Drs. Patrick Brown and Jeffrey Rosenthal. He is the r
 ecent recipient of the Banting-CANSSI discovery award in Biostatistics in 
 2024. His research focuses on Bayesian methods in biostatistics/epidemiolo
 gy. Presently\, he is working on problems in both contagious infectious di
 seases and agent-based methods for modelling viral hepatitis. You can read
  more about his work at https://www.justinslater.ca/.\n
DTSTART:20250226T203000Z
DTEND:20250226T213000Z
SUMMARY:A statistical framework for reconstructing epidemic curves
URL:https://www.mcgill.ca/epi-biostat-occh/channels/event/statistical-frame
 work-reconstructing-epidemic-curves-363558
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