Date of Degree
PhD (Doctor of Philosophy)
Smith, Brian J.
First Committee Member
Second Committee Member
Third Committee Member
Cowles, Mary Kathryn
Fourth Committee Member
Fifth Committee Member
Sixth Committee Member
Infectious diseases, including influenza, measles, and sexually transmitted diseases, spread from person to person. Different attempts have been made to modify or extend traditional epidemic models to relax homogeneity assumptions, so as to handle more complex and realistic situations. We propose a network-based approach to the modeling and prediction of infectious disease outbreaks.
Our focus is on heterogeneous populations where there is variation in individual susceptibility, infectivity, and person-to-person contact patterns. To address the complexity of disease propagation over a contact network, we develop a Bayesian survival model that maps the network onto a latent space and uses latent positions to predict disease transmission.
We present an R package (`epinet') implementation of our methods and an application to a high school contact network. The package uses C code to implement an MCMC algorithm to efficiently estimate parameters and predict disease outcomes. Our application involves contact data collected by mobile sensors distributed to individuals, and provides estimates of disease transmission in line with the network structure. In it, we address issues that are of direct interest to public health professionals, such as prediction of future outbreaks of diseases. Questions such as whether quarantine will help mitigate an outbreak can also be explored using our proposed model.
Bayesian, Contact Network, Epidemics, Latent Space, Survival
viii, 110 pages
Includes bibliographical references (pages 103-110).
Copyright © 2014 Jun Yin
Yin, Jun. "Bayesian statistical modeling in epidemics and the contact networks that transmit them." PhD (Doctor of Philosophy) thesis, University of Iowa, 2014.