Agent-based models are widely used to predict infectious disease spread. For these predictions, one needs to understand how each input parameter affects the result. Here, some parameters may affect the sensitivities of others, requiring the analysis of higher order coefficients through e.g. Sobol sensitivity analysis. The geographical structures of real-world regions are distinct in that they are difficult to reduce to single parameter values, making a unified sensitivity analysis intractable. Yet analyzing the importance of geographical structure on the sensitivity of other input parameters is important because a strong effect would justify the use of models with real-world geographical representations, as opposed to stylized ones. Here we perform a grouped Sobol's sensitivity analysis on COVID-19 spread simulations across a set of three diverse real-world geographical representations. We study the differences in both results and the sensitivity of non-geographical parameters across these geographies. By comparing Sobol indices of parameters across geographies, we find evidence that infection rate could have more sensitivity in regions where the population is segregated, while parameters like recovery period of mild cases are more sensitive in regions with mixed populations. We also show how geographical structure affects parameter sensitivity changes over time.

翻译：基于智能体的模型被广泛用于预测传染病传播。在进行此类预测时，需要理解每个输入参数如何影响结果。其中，某些参数可能影响其他参数的敏感性，因此需要通过索博尔灵敏度分析等方法分析高阶系数。现实地理区域的结构具有独特性，难以简化为单一参数值，这使得统一的灵敏度分析难以实现。然而，分析地理结构对其他输入参数敏感性的影响至关重要，因为若影响显著，则证明使用具有真实地理表征的模型是合理的，而非采用简化模型。本研究在三个不同现实地理表征上，对COVID-19传播模拟进行了分组索博尔灵敏度分析。我们探讨了不同地理环境下非地理参数结果的差异及其敏感性的变化。通过比较各地理区域的索博尔指数，发现感染者比例在人口隔离区域可能具有更高敏感性，而轻症康复期等参数则在混合人口区域更为敏感。此外，我们还揭示了地理结构如何影响参数敏感性随时间的变化规律。