Synthetic population generation with public health characteristics for spatial agent-based models.
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....
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012439 |
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| _version_ | 1850261537832304640 |
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| author | Emma Von Hoene Amira Roess Hamdi Kavak Taylor Anderson |
| author_facet | Emma Von Hoene Amira Roess Hamdi Kavak Taylor Anderson |
| author_sort | Emma Von Hoene |
| collection | DOAJ |
| description | 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 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. |
| format | Article |
| id | doaj-art-0b3407713e9446ebb6439d15b256bbaf |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-0b3407713e9446ebb6439d15b256bbaf2025-08-20T01:55:22ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-03-01213e101243910.1371/journal.pcbi.1012439Synthetic population generation with public health characteristics for spatial agent-based models.Emma Von HoeneAmira RoessHamdi KavakTaylor AndersonAgent-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 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.https://doi.org/10.1371/journal.pcbi.1012439 |
| spellingShingle | Emma Von Hoene Amira Roess Hamdi Kavak Taylor Anderson Synthetic population generation with public health characteristics for spatial agent-based models. PLoS Computational Biology |
| title | Synthetic population generation with public health characteristics for spatial agent-based models. |
| title_full | Synthetic population generation with public health characteristics for spatial agent-based models. |
| title_fullStr | Synthetic population generation with public health characteristics for spatial agent-based models. |
| title_full_unstemmed | Synthetic population generation with public health characteristics for spatial agent-based models. |
| title_short | Synthetic population generation with public health characteristics for spatial agent-based models. |
| title_sort | synthetic population generation with public health characteristics for spatial agent based models |
| url | https://doi.org/10.1371/journal.pcbi.1012439 |
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