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The fidelity of agent-based models in replicating real-world epidemic scenarios hinges on the accurate portrayal of both population-wide and individual-level interactions. In situations where comprehensive population data are lacking, synthetic populations serve as a vital input to agent-based models, approximating real-world demographic structures. While some current population synthesizers consider the structural relationships among agents from the same household, there remains room for refinement in this domain, which could potentially introduce biases in subsequent disease transmission simulations. In response, this study unveils a novel methodology for generating synthetic populations tailored for infectious disease transmission simulations. By integrating insights from microsample-derived household structures, we employ a heuristic combinatorial optimizer to recalibrate these structures, subsequently yielding synthetic populations that faithfully represent agent structural relationships. Implementing this technique, we successfully generated a spatially-explicit synthetic population encompassing over 17 million agents for Shenzhen, China. The findings affirm the method\u2019s efficacy in delineating the inherent statistical structural relationship patterns, aligning well with demographic benchmarks at both city and subzone tiers. Moreover, when assessed against a stochastic agent-based Susceptible-Exposed-Infectious-Recovered model, our results pinpointed that variations in population synthesizers can notably alter epidemic projections, influencing both the peak incidence rate and its onset.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1011810","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T18:35:32Z","timestamp":1707762932000},"page":"e1011810","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":13,"title":["Generating synthetic population for simulating the spatiotemporal dynamics of epidemics"],"prefix":"10.1371","volume":"20","author":[{"given":"Kemin","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0262-0655","authenticated-orcid":true,"given":"Ling","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7466-4123","authenticated-orcid":true,"given":"Kang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junli","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yepeng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyang","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huibin","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"pcbi.1011810.ref001","article-title":"A simulation study of coronavirus as an epidemic disease using agent-based modeling.","volume":"18","author":"AA Alzu\u2019bi","year":"2021","journal-title":"Perspectives in health information management."},{"issue":"12","key":"pcbi.1011810.ref002","doi-asserted-by":"crossref","first-page":"e0208775","DOI":"10.1371\/journal.pone.0208775","article-title":"An open-data-driven agent-based model to simulate infectious disease outbreaks.","volume":"13","author":"E Hunter","year":"2018","journal-title":"PloS one."},{"issue":"1","key":"pcbi.1011810.ref003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1476-072X-8-50","article-title":"An agent-based approach for modeling dynamics of contagious disease spread","volume":"8","author":"L Perez","year":"2009","journal-title":"International journal of health geographics"},{"key":"pcbi.1011810.ref004","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.epidem.2017.02.010","article-title":"Using data-driven agent-based models for forecasting emerging infectious diseases.","volume":"22","author":"S Venkatramanan","year":"2018","journal-title":"Epidemics."},{"issue":"3","key":"pcbi.1011810.ref005","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s41745-021-00260-2","article-title":"Agent-Based Computational Epidemiological Modeling","volume":"101","author":"KR Bissett","year":"2021","journal-title":"Journal of the Indian Institute of Science"},{"issue":"7","key":"pcbi.1011810.ref006","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1080\/03081060.2018.1504182","article-title":"Investigating scalability in population synthesis: a comparative approach","volume":"41","author":"I Saadi","year":"2018","journal-title":"Transportation Planning and Technology"},{"issue":"2","key":"pcbi.1011810.ref007","doi-asserted-by":"crossref","first-page":"148","DOI":"10.3390\/ijgi11020148","article-title":"Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data.","volume":"11","author":"S Agriesti","year":"2022","journal-title":"ISPRS International Journal of Geo-Information"},{"issue":"1","key":"pcbi.1011810.ref008","doi-asserted-by":"crossref","first-page":"92","DOI":"10.3141\/2014-12","article-title":"Population synthesis for microsimulating travel behavior","volume":"2014","author":"JY Guo","year":"2007","journal-title":"Transportation Research Record"},{"key":"pcbi.1011810.ref009","doi-asserted-by":"crossref","first-page":"103291","DOI":"10.1016\/j.trc.2021.103291","article-title":"Synthetic population and travel demand for Paris and \u00cele-de-France based on open and publicly available data","volume":"130","author":"S H\u00f6rl","year":"2021","journal-title":"Transportation Research Part C: Emerging Technologies"},{"issue":"1","key":"pcbi.1011810.ref010","first-page":"371","article-title":"An open data-driven approach for travel demand synthesis: an application to S\u00e3o Paulo. 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