Impact of Hospital Bed Shortages on the Containment of COVID-19 in Wuhan

Int J Environ Res Public Health. 2020 Nov 18;17(22):8560. doi: 10.3390/ijerph17228560.

Abstract

The global outbreak of COVID-19 has caused worrying concern amongst the public and health authorities. The first and foremost problem that many countries face during the outbreak is a shortage of medical resources. In order to investigate the impact of a shortage of hospital beds on the COVID-19 outbreak, we formulated a piecewise smooth model for describing the limitation of hospital beds. We parameterized the model while using data on the cumulative numbers of confirmed cases, recovered cases, and deaths in Wuhan city from 10 January to 12 April 2020. The results showed that, even with strong prevention and control measures in Wuhan, slowing down the supply rate, reducing the maximum capacity, and delaying the supply time of hospital beds all aggravated the outbreak severity by magnifying the cumulative numbers of confirmed cases and deaths, lengthening the end time of the pandemic, enlarging the value of the effective reproduction number during the outbreak, and postponing the time when the threshold value was reduced to 1. Our results demonstrated that establishment of the Huoshenshan, Leishenshan, and Fangcang shelter hospitals avoided 22,786 people from being infected and saved 6524 lives. Furthermore, the intervention of supplying hospital beds avoided infections in 362,360 people and saved the lives of 274,591 persons. This confirmed that the quick establishment of the Huoshenshan, Leishenshan Hospitals, and Fangcang shelter hospitals, and the designation of other hospitals for COVID-19 patients played important roles in containing the outbreak in Wuhan.

Keywords: COVID-19 outbreak; effective reproduction number; hospital beds; sensitivity analysis; transmission model.

MeSH terms

  • Beds / supply & distribution*
  • Betacoronavirus
  • COVID-19
  • China / epidemiology
  • Coronavirus Infections / epidemiology*
  • Hospital Bed Capacity / statistics & numerical data*
  • Humans
  • Pandemics
  • Pneumonia, Viral / epidemiology*
  • SARS-CoV-2