Graduation Semester and Year
Spring 2026
Language
English
Document Type
Thesis
Degree Name
Master of Science in Earth and Environmental Science
Department
Earth and Environmental Sciences
First Advisor
Yike Shen
Second Advisor
Yunyao Li
Third Advisor
Yue Liao
Abstract
The Texas Department of State Health Services monitors numerous notifiable conditions statewide, including Campylobacter, Salmonella, Shiga toxin-producing Escherichia coli (STEC), Rabies, and West Nile virus (WNV). Given the substantial health, economic, and public health burden associated with these conditions, improving prediction is an important step toward reducing their overall impact. This study evaluated whether external demographic, social, climate, and environmental data could improve prediction of county-year disease activity across Texas. County level data was analyzed using supervised machine learning models, including linear regression, ridge regression, multilayer perceptron, random forest, XGBoost, as well as K-means clustering to identify broader spatial-temporal patterns. Tree-based models performed best overall, with XGBoost emerging as the top-performing model for all five retained conditions. Predictive performance was strongest for Campylobacteriosis (R² = 0.774 ± 0.018), Salmonella (R² = 0.822 ± 0.018), and STEC (R² = 0.690 ± 0.030), while Rabies (R² = 0.803 ± 0.012) and West Nile Virus (R² = 0.677 ± 0.064) remained moderately to strongly predictable at the county-year level. Population was the strongest predictor across conditions, while environmental and climate-related features provided additional explanatory value, especially for West Nile Virus. This result extended to unsupervised clustering of these conditions, with a single cluster consistently dedicated to highly populated counties and additional clusters grouping geographically similar regions. These findings support integrating environmental and demographic data into infectious disease surveillance to inform more targeted public health approaches across Texas.
Keywords
Epidemiology, Machine Learning, Public Health, Salmonella, Campylobacter, Rabies, Shiga Toxin-producing E. Coli, Rabies, West Nile Virus
Disciplines
Data Science | Environmental Health | Environmental Sciences
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Lashbrook, Robert E., "Spatial Temporal Modeling of Infectious Disease Patterns in Texas" (2026). Earth & Environmental Sciences Theses. 2.
https://mavmatrix.uta.edu/ees_theses2/2