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Activity Number: 30
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #321166
Title: A Spatiotemporal Hierarchical Bayesian Model for Understanding Vectors and Vector-Borne Disease
Author(s): Erica Billig* and Jason Roy and Michael Levy and Michelle Ross
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Keywords: spatiotemporal ; Bayesian ; vector ; spatial

Understanding the dynamics of vector-borne disease is difficult due to the complex intra-species interactions that occur. To intervene, we often target the vector of transmission to effectively minimize the spread of disease. We present a joint spatiotemporal model to elucidate the distinct and common covariate factors and spatiotemporal movement of vectors and parasite-infected vectors. By understanding factors associated with the probability of a positively-infected vector, we can more effectively intervene to prevent disease transmission. We use a recent study of the Chagas disease vector T. infestans in Arequipa, Peru. We observe the counts of all vectors and positively-infected vectors at each location at four unique time points within a small-scale geographic area. We hypothesize that the factors and movement associated with positively-infected vectors is unique compared to all vectors, contributing to the observed bi-modal prevalence of infection among vector colonies. We test this hypothesis using a joint hierarchical Bayesian model that includes separate and shared spatial components for positively-infected vectors and all vectors.

Authors who are presenting talks have a * after their name.

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