Synthetic Spatiotemporal Covid-19 Data Replicates Generated via the USPopulationSampler R Package

Abstract

Given that a disease case is known to have occurred in a specific county or state, can one reconstruct a precise geographic observation for that case? The answer is that one cannot. We instead sample multiple high-probability locations for that case under the assumption that cases are more likely to occur in places where there are more people. To do this, we develop a novel and user-friendly R package called USPopulationSampler that randomly samples geospatial locations within census block groups (BG; the smallest geographic unit for which population counts are available) within target counties, states, or across the entirety of the U.S., randomly selected according to population counts using Census Bureau reference data. We use the package to generate 28 realistic high-probability data replicates of 100M+ synthetic spatiotemporal Covid-19 cases observed between the dates of January 21st, 2020 to March 23rd, 2023 and openly publish these data replicates on Zenodo for easy access. Given the large scale nature of the data, the USPopulationSampler package also provides tools for fast download of these datasets and functions to generate further replicates at scale using multi-core parallelization.