Demographic Drivers of Epidemic Outcomes: Sensitivity Analysis of Multidimensional Parameters in the Covasim Model

Sensitivity analysis is a key tool for identifying which model inputs most strongly influence model outputs thereby informing data collection priorities. In agent-based models, these inputs include demographic parameters used to construct synthetic populations. Such parameters — age distributions, household size distributions, and contact matrices — are critical in shaping transmission dynamics. However, most established sensitivity analysis methods are designed for scalar inputs and cannot readily accommodate multidimensional demographic data. We introduce a novel sampling approach to assess the influence of such parameters on model outputs, addressing a key gap in sensitivity analysis of epidemiological agent-based models. The Covasim model of COVID-19 transmission was used as the case study in this work. An autoencoder neural network was trained on country-level demographic datasets to generate realistic samples of high-dimensional inputs of the model. These sampled inputs were coupled with Sobol’ sensitivity indices to quantify their influence on cumulative infections and deaths. Non-scalar parameters were found to exert a major influence on model outputs. Household size distribution was the most important parameter for cumulative number of infectious cases, while age distribution had the largest effect on cumulative deaths. These findings were consistent across experimental settings, and parameter rankings were stable despite stochastic variation. Our autoencoder-based sampling approach extends methods of global sensitivity analysis to high-dimensional demographic parameters in agent-based epidemiological models. Our results highlight that only a subset of demographic inputs exert a dominant influence on Covasim outputs, and similar behavior can be expected in other epidemiological modeling platforms.