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DTSTAMP:20260204T145508Z
DESCRIPTION:Title: A Zero-Inflated Spatiotemporal Model for Underreported I
 nfectious Diseases Counts\n\n \n\nAbstract\n\nUnderreporting of disease ca
 ses is a recurring challenge in epidemiology\, which introduces bias into 
 the statistical estimation of disease rates. Although many approaches for 
 modeling underreported count data have been proposed in recent years\, the
 re remains a lack of methods that address data correction within a spatiot
 emporal framework. This limitation is especially pronounced in analyses ba
 sed on less aggregated time periods and small geographic areas\, where exc
 ess zeros are frequently observed. Zero-inflation can be caused by both th
 e absence of the disease and underregistration. In this talk\, after brief
 ly revisiting some existing approaches for modeling underreported count da
 ta\, I will introduce a zero-inflated model that explicitly accounts for b
 oth the absence of the disease (true zeros) and an imperfect counting proc
 ess. Conditional on disease presence\, the observed count follows a Binomi
 al thinned zero-truncated negative binomial distribution\, which may lead 
 to the observation of zeros even when the disease is present but goes unde
 tected. We consider a spatiotemporal setting\, and inference follows the B
 ayesian paradigm. By taking into account underreporting\, excess zeros\, a
 nd spatiotemporal heterogeneity\, the proposed modeling strategy aims to p
 rovide more realistic estimates for associated disease rates. In this way\
 , decision-makers can make more informed and accurate decisions for diseas
 e control and prevention. Simulation studies are performed to explore the 
 model's behavior under different levels of presence and underreporting\, a
 s well as in distinct data generation processes. We apply the model to the
  cases of chikungunya infection in Rio de Janeiro\, Brazil.\n\n \n\nSpeake
 r bio\n\n \n\nGuilherme Oliveira is an Associate Professor of Statistics a
 t the Federal Center for Technological Education of Minas Gerais (CEFET-MG
 )\, Department of Computer Sciences\, in Belo Horizonte\, Brazil. He recei
 ved his PhD in Statistics from the Federal University of Minas Gerais (UFM
 G) in 2020. From May 2025 to April 2026\, he is on sabbatical leave as a v
 isiting professor at EBOH\, ºÚÁÏÍø±¬³Ô¹Ï. His research and funded pro
 jects have focused on Bayesian methods for analyzing underreported data\, 
 with applications in Public Health\, Epidemiology\, and the Social Science
 s. Areas of interest include spatiotemporal modeling\, disease mapping\, m
 easurement error\, and machine learning. For more information\, please vis
 it: https://sites.google.com/view/guilherme-deoliveira/.\n
DTSTART:20260204T203000Z
DTEND:20260204T213000Z
LOCATION:Room 1140\, ºÚÁÏÍø±¬³Ô¹Ï College 2001\, CA\, QC\, Montreal\, H3A 1G1\, 2
 001\, avenue ºÚÁÏÍø±¬³Ô¹Ï College
SUMMARY:Guilherme Oliveira (ºÚÁÏÍø±¬³Ô¹Ï)
URL:/biology/channels/event/guilherme-oliveira-mcgill-
 university-370724
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