Spatial analysis and GIS in the study of COVID-19. A review

Sci Total Environ. 2020 Oct 15;739:140033. doi: 10.1016/j.scitotenv.2020.140033. Epub 2020 Jun 8.

Abstract

This study entailed a review of 63 scientific articles on geospatial and spatial-statistical analysis of the geographical dimension of the 2019 coronavirus disease (COVID-19) pandemic. The diversity of themes identified in this paper can be grouped into the following categories of disease mapping: spatiotemporal analysis, health and social geography, environmental variables, data mining, and web-based mapping. Understanding the spatiotemporal dynamics of COVID-19 is essential for its mitigation, as it helps to clarify the extent and impact of the pandemic and can aid decision making, planning and community action. Health geography highlights the interaction of public health officials, affected actors and first responders to improve estimations of disease propagation and likelihoods of new outbreaks. Attempts at interdisciplinary correlation examine health policy interventions for the siting of health/sanitary services and controls, mapping/tracking of human movement, formulation of appropriate scientific and political responses and projection of spatial diffusion and temporal trends. This review concludes that, to fight COVID-19, it is important to face the challenges from an interdisciplinary perspective, with proactive planning, international solidarity and a global perspective. This review provides useful information and insight that can support future bibliographic queries, and also serves as a resource for understanding the evolution of tools used in the management of this major global pandemic of the 21 Century. It is hoped that its findings will inspire new reflections on the COVID-19 pandemic by readers.

Keywords: COVID-19; Data mining and web-base; Geographical dimensions; Health geography; Interdisciplinary correlation; Spatiotemporal analyst.

Publication types

  • Review

MeSH terms

  • Betacoronavirus
  • COVID-19
  • Coronavirus Infections*
  • Coronavirus*
  • Geographic Information Systems
  • Humans
  • Pandemics*
  • Pneumonia, Viral*
  • SARS-CoV-2