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

Impact of geography on the importance of parameters in infectious disease models