[ABE-L] Seminário de Ciência De Dados: Alexandra Schmidt

[Hedibert Lopes]

October 5, 2023
12pm, São Paulo, Brasil
Zoom: https://zoom.us/j/95781336030


Title: Zero-inflated and Hurdle Markov Switching Models for Spatio-temporal Infectious Disease Counts


Speaker: 
Alexandra M. Schmidt

University: 
McGill University

Abstract: 
Spatio-temporal counts of infectious disease cases often contain an excess of zeros. With existing zero-inflated count models applied to such data it is difficult to quantify space-time heterogeneity in the effects of disease spread between areas. Also, existing methods do not allow for separate dynamics to affect the reemergence and persistence of the disease. As an alternative, we develop a new zero-state coupled Markov switching negative binomial model, under which the disease switches between periods of presence and absence in each area through a series of partially hidden non-homogeneous Markov chains coupled between neighbouring locations. When the disease is present, an autoregressive negative binomial model generates the cases with a possible zero representing the disease being undetected. We fit different versions of the proposed model to the weekly number of cases of dengue fever across the districts of Rio de Janeiro. We also fit an extension of the model to weekly cases of Zika across municipalities of Colombia. Finally, we propose a hurdle version of the zero-state coupled Markov switching negative binomial model and compare the models when analyzing the first epidemic of Chikungunya experienced in Rio de Janeiro between 2015 and 2016. This talk comprises joint work with Dirk Douwes-Schultz (Biostatistics PhD Candidate, McGill University), Laís P. Freitas (Post-Doctoral Fellow, Université de Montréal) and Mingchi Xu (Biostatistics PhD Candidate, McGill University).