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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 AsGet 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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References

  1. L. Berger and et al. Rational policymaking during a pandemic. PNAS, 118(4), 2021. Google Scholar
  2. D.E. Bloom and D. Cadarette. Infectious disease threats in the twenty-first century: Strengthening the global response. Frontiers in Immunology, 10, 2019. Google Scholar
  3. CDC. Reinfection: Clinical considerations for care of children and adults with confirmed covid-19, 2020. URL: https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/clinical-considerations-reinfection.html.
  4. D. Durham and E. Casman. Incorporating individual health-protective decisions into disease transmission models: a mathematical framework. J R Soc Interface, 9(68):562-570, 2012. Google Scholar
  5. S. Funk, M. Salathé, and V. Jansen. Modelling the influence of human behaviour on the spread of infectious diseases: a review. J R Soc Interface, 7(50):1247-1256, 2010. Google Scholar
  6. P.T. Gressman and J.R. Peck. Simulating covid-19 in a university environment. Mathematical Biosciences, 328, 2020. Google Scholar
  7. X. He and et al. Temporal dynamics in viral shedding and transmissibility of covid-19. Nature Medicine, 26:672-675, 2020. Google Scholar
  8. E. Hunter, B. Mac Namee, and J. Kelleher. A taxonomy for agent-based models in human infectious disease epidemiology. JASS, 20(3), 2017. Google Scholar
  9. D.A. Levy and P.R. Nail. Contagion: a theoretical and empirical review and reconceptualization. Genetic, social, and general psychology monographs, 119(12):233-284, 1993. Google Scholar
  10. Z. Li, J.L. Swann, and P. Keskinocak. Value of inventory information in allocating a limited supply of influenza vaccine during a pandemic. PLOS ONE, 13(10), 2018. Google Scholar
  11. N. Malleson. Repastcity, 2012. URL: https://code.google.com/archive/p/repastcity/.
  12. S. Mei and et al. Individual Decision Making Can Drive Epidemics: A Fuzzy Cognitive Map Study. IEEE Transactions on Fuzzy Systems, 22(2):264-273, 2014. Google Scholar
  13. T. Ogata and et al. A low proportion of asymptomatic covid-19 patients with the delta variant infection by viral transmission through household contact at the time of confirmation in ibaraki, japan. ISPRS International Journal of Geo-Information, 4(3):192-196, 2022. Google Scholar
  14. L. Perez and S. Dragicevic. An agent-based approach for modeling dynamics of contagious disease spread. International journal of health geographics, 8(1):1-17, 2009. Google Scholar
  15. M. Reveil and Y.H. Chen. Predicting and preventing covid-19 outbreaks in indoor environments: an agent-based modeling study. Scientific Reports, 12, 2022. Google Scholar
  16. A. Roess and et al. Predictors of firearm purchasing during the coronavirus pandemic in the united states: a cross-sectional study. Public health, 219:159-164, 2023. Google Scholar
  17. J. Von Neumann and O. Morgenstern. Theory of games and economic behavior, 2nd rev. Princeton university press, 1947. Google Scholar
  18. Wu Y. and et al. Incubation period of covid-19 caused by unique sars-cov-2 strains: A systematic review and meta-analysis. JAMA, 5(8), 2022. Google Scholar
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