Compartmental Disaggregation: Bridging Simulation and Sampling Methods for Synthetic Population Data Generation

As agent-based models (ABMs) grow increasingly widespread in public health, their associated challenges have become all the more significant. Lauded for their ability to capture population heterogeneity, nonlinear dynamics, and emergent behaviors, disease ABMs are also computationally expensive and often require detailed inputs that describe each agent at the individual-level, known as synthetic population data. Current approaches for synthetic population data generation generally fall into one of two categories: sampling or simulation. These methods are both feasible only under restricted conditions and suffer from challenges surrounding data availability and computing power. This thesis proposes compartmental disaggregation, an intermediate method for producing synthetic, individual-level time series data. By combining population-level measurements with a compartmental model that imposes assumptions about how individuals transition between a set of states, we can recover synthetic data at the individual-level. These data obey the mechanics of a given dynamic process and individual-level data in aggregate match the population-level measurement data by construction. The synthetic population data generated by compartmental disaggregation were found to effectively recover unobserved states from various simulation studies using a classical susceptible-infected-recovered (SIR) model. Applications of compartmental disaggregation will be discussed and include the construction of epidemiological profiles and network inference.