EpiGeostats: An R Package to Facilitate Visualization of Geostatistical Disease Risk Maps

Abstract

AbstractWith the emergence of the coronavirus disease 2019 (COVID-19) pandemic in Portugal, a geostatistical tool was developed to model the spatial distribution of COVID-19 risk to support decision-making and policymakers. Based on a block direct sequential simulation algorithm, the model provides detailed disease risk estimates and associated spatial uncertainty. However, uncertainty is difficult to visualize with the estimated risk, and is usually overlooked as a tool to support decision-making. Ignoring uncertainty can be misleading in evaluating risk, since the amount of uncertainty varies throughout the spatial domain. The EpiGeostats R package was developed to solve this problem, since it integrates the geostatistical model and visualization tools to deliver a single map summarizing disease risk and spatial uncertainty. This paper briefly describes the methodology and package functions implemented for interfacing with the tools in question. The use of EpiGeostats is illustrated by applying it to real data from COVID-19 incidence rates on mainland Portugal. EpiGeostats is a powerful tool for supporting decision-making in the context of epidemics, since it combines a well-established geostatistical model for disease risk mapping with simple and intuitive ways of visualizing results, which prevent fine-scale inference in regions with high-risk uncertainty. The package may be used for similar problems such as mortality risk, or applied to other fields such as ecology or environmental epidemiology.