Publication:
Improving the Representation of Human Health Behavior in Spatial Agent-Based Models of Disease Spread

Date

2023-08-03

Authors

Jose, Roberto Siasoco

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Abstract

Given the significant threat of infectious respiratory diseases to global health, epidemiological models are essential tools for better understanding disease transmission. Of the many modeling approaches, Agent-Based Models (ABMs) are ideal for simulating disease spread because of their ability to examine and predict disease outcomes based on individual-level behaviors and interactions, which are key drivers of disease spread. However, many existing ABMs either ignore or generalize the behavioral component due to several challenges, such as the lack of data for informing agent behaviors, difficulties with implementing behavioral computational frameworks, or limited interdisciplinary collaboration between the broader ABM community and domain experts. The objective of the thesis is to advance the representation of human health behaviors in ABMs of infectious respiratory disease spread. To achieve this, a systematic literature review is conducted to assess the extent to which health behavior is modeled in existing ABMs of infectious respiratory disease spread. Building upon the findings from the literature review, a data-driven agent decision framework of health behaviors for spatial ABMs of disease spread is developed. The framework is then integrated into a geospatial ABM that simulates the spread of COVID-19 and mask-use behavior among the student population at George Mason University (GMU) during the Fall 2021 semester. The advancements made in this thesis will ultimately provide the public and decision-makers with greater insight into disease transmission, accurate predictions on disease outcomes, and preparation for future infectious respiratory disease outbreaks.

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Keywords

geographic information science, infectious respiratory disease, health behavior, agent-based model, COVID-19, disease simulation

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