Sensitivity Analysis for COVID-19 Epidemiological Models within a Geographic Framework

Spatial sciences and geography have been integral to the modeling of and communicating information pertaining to the COVID-19 pandemic. Epidemiological models are being used within a geographic context to map the spread of the novel SARS-CoV-2 virus and to make decisions regarding state-wide interventions and allocating hospital resources. Data required for epidemiological models are often incomplete, biased, and available for a spatial unit more extensive than the one needed for decision-making. In this paper, we present results on a global sensitivity analysis of epidemiological model parameters on an important design variable, time to peak number of cases, within a geographic context. We design experiments for quantifying the impact of uncertainty of epidemiological model parameters on distribution of peak times for the state of California. We conduct our analysis at the county-level and perform a non-parametric, global sensitivity analysis to quantify interplay between the uncertainty of epidemiological parameters and design variables.