BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20260201T180902EST-8319H83pVX@132.216.98.100 DTSTAMP:20260201T230902Z DESCRIPTION:Guilherme Oliveira\, PhD\n\nAssociate Professor of Statistics | CEFET-MG\n Visiting Professor | Â鶹´«Ã½ÍøÕ¾\n\nWHEN: Wednesday\, Feb ruary 4\, 2026\, from 3:30 to 4:30 p.m.\n WHERE: Hybrid | 2001 Â鶹´«Ã½ÍøÕ¾ Colle ge Avenue\, Rm 1140\; Zoom\n NOTE: Guilherme Oliveira will be presenting in -person at SPGH \n\nAbstract\n\nUnderreporting of disease cases is a recur ring challenge in epidemiology\, which introduces bias into the statistica l estimation of disease rates. Although many approaches for modeling under reported count data have been proposed in recent years\, there remains a l ack of methods that address data correction within a spatiotemporal framew ork. This limitation is especially pronounced in analyses based on less ag gregated time periods and small geographic areas\, where excess zeros are frequently observed. Zero-inflation can be caused by both the absence of t he disease and underregistration. In this talk\, after briefly revisiting some existing approaches for modeling underreported count data\, I will in troduce a zero-inflated model that explicitly accounts for both the absenc e of the disease (true zeros) and an imperfect counting process. Condition al on disease presence\, the observed count follows a Binomial thinned zer o-truncated negative binomial distribution\, which may lead to the observa tion of zeros even when the disease is present but goes undetected. We con sider a spatiotemporal setting\, and inference follows the Bayesian paradi gm. By taking into account underreporting\, excess zeros\, and spatiotempo ral heterogeneity\, the proposed modeling strategy aims to provide more re alistic estimates for associated disease rates. In this way\, decision-mak ers can make more informed and accurate decisions for disease control and prevention. Simulation studies are performed to explore the model's behavi or under different levels of presence and underreporting\, as well as in d istinct data generation processes. We apply the model to the cases of chik ungunya infection in Rio de Janeiro\, Brazil.\n\nSpeaker Bio\n\nGuilherme Oliveira is an Associate Professor of Statistics at the Federal Center for Technological Education of Minas Gerais (CEFET-MG)\, Department of Comput er Sciences\, in Belo Horizonte\, Brazil. He received his PhD in Statistic s from the Federal University of Minas Gerais (UFMG) in 2020. From May 202 5 to April 2026\, he is on sabbatical leave as a visiting professor at EBO H\, Â鶹´«Ã½ÍøÕ¾. His research and funded projects have focused on Ba yesian methods for analyzing underreported data\, with applications in Pub lic Health\, Epidemiology\, and the Social Sciences. Areas of interest inc lude spatiotemporal modeling\, disease mapping\, measurement error\, and m achine learning. For more information\, please visit: https://sites.google .com/view/guilherme-deoliveira/.\n DTSTART:20260204T203000Z DTEND:20260204T213000Z SUMMARY:A Zero-Inflated Spatiotemporal Model for Underreported Infectious D iseases Counts URL:/epi-biostat-occh/channels/event/zero-inflated-spa tiotemporal-model-underreported-infectious-diseases-counts-370533 END:VEVENT END:VCALENDAR