Generating synthetic population for simulating the spatiotemporal dynamics of epidemics

Abstract

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.

Fig 1. The framework of population synthesis.

The basemap shapefile can be accessed at https://www.kaggle.com/datasets/keminzhu/basemap-shenzhen-subzones.

Fig 2. Distribution of household structure in survey data.

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

Fig 3. Changes in the objective function value with the iteration number.

Fig 4. Spatial distribution for full population by age group across subregions.

The basemap shapefile can be accessed at https://www.kaggle.com/datasets/keminzhu/basemap-shenzhen-subzones.

Fig 5. Age distributions obtained from the demographic data and synthetic population at the subzone level, where each point represents the number of people in a certain age group within a subzone.

Fig 6. Comparison of marginal distributions obtained from the demographic data and synthetic population for household size, age, and gender.

Fig 7. Comparison of the joint distribution of age-gender obtained from the survey dataset and synthetic population.

Fig 8. Comparison of the motif distribution in the synthetic populations generated by different methods, with motifs ordered according to the survey data.

Fig 9. 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.

Fig 10. 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.

Fig 11. Epidemic curve simulated with a different synthetic population.