Agent-based models (ABMs) simulate the behaviors, interactions, and disease transmission between individual agents within their environment, enabling the investigation of the underlying processes driving disease dynamics and how these processes may be influenced by policy interventions. Despite the critical role that characteristics such as health attitudes and vaccination status play in disease outcomes, the initialization of agent populations with these variables is often oversimplified, overlooking statistical relationships between attitudes and other characteristics or lacking spatial heterogeneity. Leveraging population synthesis methods to create populations with realistic health attitudes and protective behaviors for spatial ABMs has yet to be fully explored. Therefore, this study introduces a novel application for generating synthetic populations with protective behaviors and associated attitudes using public health surveys instead of traditional individual-level survey datasets from the census. We test our approach using two different public health surveys (one national and the other representative of the study area, Virginia, U.S.) to create two synthetic populations representing individuals aged 18 and over in Virginia, U.S., and their COVID-19 vaccine attitudes and uptake as of December 2021. Results show that integrating public health surveys into synthetic population generation processes preserves the statistical relationships between vaccine uptake and attitudes in different demographic groups while capturing spatial heterogeneity at fine scales. This approach can support disease simulations that aim to explore how real populations might respond to interventions and how these responses may lead to demographic or geographic health disparities. Our study also demonstrates the potential for initializing agents with variables relevant to public health domains that extend beyond infectious diseases, ultimately advancing data-driven ABMs for geographically targeted decision-making.

The authors have declared no competing interest.

This research was funded by National Science Foundation (Award #230970 and #2109647).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

(IRB 1684418-3) IRB of George Mason University gave ethical approval for this work.

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The data including the validation dataset and code for generating the synthetic population based on the Household Pulse Survey is available on a GitHub repository at https://github.com/evonhoene/Population-Generation-for-Public-Health-ABMs. The spatially aggregate data from the American Community Survey and the individual-level Household Pulse Survey are publicly available and the links are provided on the GitHub page. De-identified data from the local Virginia survey data is available upon request.

https://github.com/evonhoene/Population-Generation-for-Public-Health-ABMs