Generating synthetic population for simulating the spatiotemporal dynamics of epidemics

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. Exploring the intricacies of infectious disease transmission becomes profoundly insightful with tools that can model complex, nonlinear interactions. Among these, agent-based models stand out, primarily due to their ability to mirror not just expansive populations but also detailed individual-level interactions. While synthetic populations act as a vital surrogate for the real-world demographic when comprehensive datasets are elusive, their creation isn't without challenges. Present synthesizers, though acknowledging intra-household relationships, still have nuances left unexplored. Addressing this, our research presents a pioneering method tailored for crafting synthetic populations, especially for disease spread simulations. This methodology, grounded in data from genuine household structures, utilizes an optimization strategy to calibrate these relationships effectively. Applying this, we meticulously constructed a synthetic population for Shenzhen, China, encompassing over 17 million agents. Our results underscored the technique's prowess in accurately emulating population structures, adhering commendably to demographic metrics on both city and subzone scales. Crucially, our findings also illuminated that the specific choice of synthesizer can have a profound bearing on epidemic simulations, affecting crucial attributes like the peak and timing of disease incidence.