Author: Sahar Zarmehri

Publisher:

Published: 2021

Total Pages:

ISBN-13:

The field of landscape genetics enables the study of infectious disease dynamics by connecting the landscape features with evolutionary changes. Quantifying genetic correlation across space is helpful in providing insight into the rate of spread of an infectious disease. We investigate two genetic patterns in spatially referenced single-nucleotide polymorphisms (SNPs): isolation by distance and isolation by resistance. We model the data using a Generalized Linear Mixed effect Model (GLMM) with spatially referenced random effects and provide a novel approach for estimating parameters in spatial GLMMs. In this approach, we use the links between binary probit models and bivariate normal probabilities to directly compute the model-based covariance function for spatial binary data. Parameter estimation is based on minimizing sum of squared distance between the elements of sample covariance and model-based covariance matrices. We analyze Brucella Abortus SNP data from spatially referenced hosts in the Greater Yellowstone Ecosystem (GYE). B. abortus is a bacterium which causes Brucellosis in human, wildlife, and livestock. We propose a hierarchical model to describe the transmission of Brucellosis in elk in the GYE. We model the disease spread process using a dynamical stochastic spatiotemporal susceptible-infected-susceptible (SIS) model that captures spatial heterogeneity in dynamics using a conditional autoregressive (CAR) covariance structure in parameter model. To inform spatial rates of transmission, we propose estimating elk movement and migration rates using two different migration/immigration models. Our proposed disease spread process is constrained and we propose a numerical approximation method to find the numerical solution of the constrained process by projection of the numerical solution of unconstrained process. Movement behavior of animal changes over longer time scales. Multistate time series models based on Hidden Markov Models (HMMs) are popular that enable capturing variability in movement behavior while accounting for temporal autocorrelation. Recent studies have found evidence that movement behavior of animals cannot be easily classified into a small number of states. We propose a Bayesian non-parametric mixture model for stochastic differential equation (SDE) animal movement model by adapting a flexible clustering algorithm described as a probit stick-breaking process (PSBP). By clustering the SDE model parameters, we account for time-varying movement behavior. We apply this method to migratory lesser black-backed gulls data. Analyzing their movement behavior provides insights about the migration strategies.