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Activity Number: 80 - Advancement in Spatial and Spatiotemporal Point Process
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #329064
Title: Combining Disease Surveillance and Animal Movement Data to Predict Infectious Disease Spread
Author(s): Sahar Zarmehri* and Ephraim Hanks and Lin Lin
Companies: The Pennsylvania State University and The Pennsylvania State University and The Pennsylvania State University
Keywords: Spatio-temporal model; Infectious disease spread; Animal movement

In this paper, we build a spatio-temporal disease spread model by combining disease surveillance and animal movement data. We propose a binary Generalized Linear Mixed Effect Model (GLMM) with latent spatio-temporal random effects that capture mechanistic models of infectious disease dynamics and spatio-temporal spread. We consider a Susceptible-Infected-Removed (SIR) model for local disease progression and use host movement data to inform spatio-temporal rates of transmission. We also develop methods that allow for Euler approximations of this mechanistic process to be efficiently estimated within a probit regression model. We apply this model to elk serology data of Brucellosis in the Greater Yellowstone Ecosystem (GYE) combined with the GPS data of 1400 collared elk.

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

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