Excel spreadsheet containing, in separate sheets, the underlying numerical data for Figs 2, 3, 4, 5, 6, 7, 8, 9, and 11.

The numerical data for Fig 10 is stored in a shapefile, which can be accessed through this link: https://www.kaggle.com/datasets/keminzhu/basemap-shenzhen-subzones. (XLSX)</p

An example of a household motif weight matrix.

Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. 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’s 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.</div

Framework of the agent-based epidemic model.

(a) The compartmental model used to describe the natural history of the infectious disease between the states. (b) Schematic illustration of the weighted multilayer contact network. Details of the epidemic model and the transitions between compartments are provided in the S1 Text.</p

Information on the process of generating the synthetic population network and the epidemic model.

This passage provides a detailed description of the process involved in generating the multi-layered contact network G based on synthetic population in the model. It includes information about the age groups of agents targeted by each layer and the configuration of weight. Fig A. Parameters used in the infectious disease model. Encompasses all parameter values used in our S-E-I-R model, along with their descriptions. (DOCX)</p

Comparison of the joint distribution of age-gender obtained from the survey dataset and synthetic population.

Comparison of the joint distribution of age-gender obtained from the survey dataset and synthetic population.</p

Comparison of interdependency distributions in simulated populations using different methods with those in the survey data, where the value of each cell represents the average frequency of the corresponding cross-age relationship in households.

Comparison of interdependency distributions in simulated populations using different methods with those in the survey data, where the value of each cell represents the average frequency of the corresponding cross-age relationship in households.</p

Comparison of the motif distribution in the synthetic populations generated by different methods, with motifs ordered according to the survey data.

Comparison of the motif distribution in the synthetic populations generated by different methods, with motifs ordered according to the survey data.</p

Sample of household survey data.

Agent-based models have gained traction in exploring the intricate processes governing the spread of infectious diseases, particularly due to their proficiency in capturing nonlinear interaction dynamics. 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’s 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.</div

Distribution of household structure in survey data.

(a) Probability/Cumulative Density Function. (b) Frequency-rank and Liner Regression.</p

Comparison of marginal distributions obtained from the demographic data and synthetic population for household size, age, and gender.

Comparison of marginal distributions obtained from the demographic data and synthetic population for household size, age, and gender.</p