Document Type


Date of Degree

Spring 2012

Degree Name

PhD (Doctor of Philosophy)

Degree In


First Advisor

Oleson, Jacob J

First Committee Member

Chaloner, Kathryn

Second Committee Member

Cavanaugh, Joseph

Third Committee Member

Smith, Brian

Fourth Committee Member

Polgreen, Philip


Despite being developed in the late 1920s, compartmental epidemic modeling is still a rich and fruitful area of research. The original compartmental epidemic models were SIR (Susceptible, Infectious, Removed) models, which assume permanent immunity after recovery. SIR models, along with the more recent SEIR (Susceptible, Exposed, Infectious, Removed) models are still the gold standard in modeling pathogens that confer permanent immunity. This dissertation expands the SEIR structure to include a new class of spatial SEIR models. The exponential assumption of these models states that the latent and infectious times of the pathogen are exponentially distributed. Work that relaxes this assumption and still allows for mixing to occur at the population level is limited, thereby making strong assumptions about these times. We relax this assumption in a flexible way, by considering a hybrid approach that contains characteristics of both population level and individual level approaches. Next, we expand the Conditional Autoregressive (CAR) class of spatial models. This is to account for the Mumps data set we have procured, which contains mismatched lattice structures that cannot be handled by traditional CAR models. The use of CAR models is desirable here, as these models are known to produce spatial smoothing on lattices, and are a natural way to draw strength spatially in estimating spatial effects. Finally, we develop a pair of spatial SEIR models utilizing our CAR structure. The first utilizes the exponential assumption, which is very robust. The second develops a highly flexible spatial SEIR model by embedding the CAR structure into the SEIR structure. This allows for a realistic analysis of epidemic data occurring on a lattice. These models are applied to the Iowa Mumps epidemic of 2006. There are three questions of interest. First, what improvement do the methods proposed here provide over the current models in the literature? Second, did spring break, which occurred approximately 40 days into the epidemic, have an effect on the overall number of new infections? Thirdly, did the public's awareness of the epidemic change the rate at which mixing occurred over time? The spatial models in this dissertation are adequately constructed to answer these questions, and the results are provided.


Bayesian, CAR, Hierarchical, SEIR


x, 110 pages


Includes bibliographical references (pages 103-110).


Copyright 2012 Aaron Thomas Porter

Included in

Biostatistics Commons