Spatiotemporal analysis of medical resource deficiencies in the U.S. under COVID-19 pandemic

PLoS One. 2020 Oct 14;15(10):e0240348. doi: 10.1371/journal.pone.0240348. eCollection 2020.


Coronavirus disease 2019 (COVID-19) was first identified in December 2019 in Wuhan, China as an infectious disease, and has quickly resulted in an ongoing pandemic. A data-driven approach was developed to estimate medical resource deficiencies due to medical burdens at county level during the COVID-19 pandemic. The study duration was mainly from February 15, 2020 to May 1, 2020 in the U.S. Multiple data sources were used to extract local population, hospital beds, critical care staff, COVID-19 confirmed case numbers, and hospitalization data at county level. We estimated the average length of stay from hospitalization data at state level, and calculated the hospitalized rate at both state and county level. Then, we developed two medical resource deficiency indices that measured the local medical burden based on the number of accumulated active confirmed cases normalized by local maximum potential medical resources, and the number of hospitalized patients that can be supported per ICU bed per critical care staff, respectively. Data on medical resources, and the two medical resource deficiency indices are illustrated in a dynamic spatiotemporal visualization platform based on ArcGIS Pro Dashboards. Our results provided new insights into the U.S. pandemic preparedness and local dynamics relating to medical burdens in response to the COVID-19 pandemic.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • COVID-19
  • Coronavirus Infections / economics
  • Coronavirus Infections / epidemiology*
  • Cost of Illness
  • Health Care Rationing / statistics & numerical data*
  • Health Resources / statistics & numerical data*
  • Humans
  • Pandemics / economics
  • Pneumonia, Viral / economics
  • Pneumonia, Viral / epidemiology*
  • Spatio-Temporal Analysis*
  • United States

Grant support

X.M. acknowledges support from NSF CSSI-1835512. C.Y. is supported by NSF CNS-1841520 and CSSI-1835507. ( The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.