License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.76
URN: urn:nbn:de:0030-drops-189712
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18971/
Go to the corresponding LIPIcs Volume Portal

Von Hoene, Emma ; Roess, Amira ; Anderson, Taylor

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

pdf-format:
LIPIcs-GIScience-2023-76.pdf (1 MB)

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.

BibTeX - Entry

@InProceedings{vonhoene_et_al:LIPIcs.GIScience.2023.76,
  author =	{Von Hoene, Emma and Roess, Amira and Anderson, Taylor},
  title =	{{A Data-Driven Decision-Making Framework for Spatial Agent-Based Models of Infectious Disease Spread}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{76:1--76:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18971},
  URN =		{urn:nbn:de:0030-drops-189712},
  doi =		{10.4230/LIPIcs.GIScience.2023.76},
  annote =	{Keywords: Agent-based model, geographic information science, disease simulation, COVID-19, agent behavior, mask use}
}

Keywords: Agent-based model, geographic information science, disease simulation, COVID-19, agent behavior, mask use
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

DROPS-Home | Fulltext Search | Imprint | Privacy Published by LZI