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