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A Data-Driven Decision-Making Framework for Spatial Agent-Based Models of Infectious Disease Spread (Short Paper)

Authors Emma Von Hoene , Amira Roess , Taylor Anderson

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Author Details

Emma Von Hoene
  • Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA
Amira Roess
  • Department of Global and Community Health, George Mason University, Fairfax, VA, USA
Taylor Anderson
  • Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA

Funding

National Science Foundation (Award #2030685 and #2109647).

Acknowledgements

The survey used in this project was considered exempt by the George Mason University Institutional Review Board (IRB 1684418-3).

Cite As Get BibTex

Emma Von Hoene, Amira Roess, and Taylor Anderson. A Data-Driven Decision-Making Framework for Spatial Agent-Based Models of Infectious Disease Spread (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 76:1-76:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.GIScience.2023.76

Abstract

Agent-based models (ABMs) are powerful tools used for better understanding, predicting, and responding to diseases. ABMs are well-suited to represent human health behaviors, a key driver of disease spread. However, many existing ABMs of infectious respiratory disease spread oversimplify or ignore behavioral aspects due to limited data and the variety of behavioral theories available. Therefore, this study aims to develop and implement a data-driven framework for agent decision-making related to health behaviors in geospatial ABMs of infectious disease spread. The agent decision-making framework uses a logistic regression model expressed in the form of odds ratios to calculate the probability of adopting a behavior. The framework is integrated into a geospatial ABM that simulates the spread of COVID-19 and mask usage among the student population at George Mason University in Fall 2021. The framework leverages odds ratios, which can be derived from surveys or open data, and can be modified to incorporate variables identified by behavioral theories. This advancement will offer the public and decision-makers greater insight into disease transmission, accurate predictions on disease outcomes, and preparation for future infectious disease outbreaks.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Modeling methodologies
Keywords
  • Agent-based model
  • geographic information science
  • disease simulation
  • COVID-19
  • agent behavior
  • mask use

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