Quantitative assessment of the effects of resource optimization and ICU admission policy on COVID-19 mortalities

Lang Zeng, Ying Qi Zeng, Ming Tang, Ying Liu, Zonghua Liu, Ying Cheng Lai

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


It is evident that increasing the intensive-care-unit (ICU) capacity and giving priority to admitting and treating patients will reduce the number of COVID-19 deaths, but the quantitative assessment of these measures has remained inadequate. We develop a comprehensive, non-Markovian state transition model, which is validated through the accurate prediction of the daily death toll for two epicenters: Wuhan, China and Lombardy, Italy. The model enables prediction of COVID-19 deaths in various scenarios. For example, if appropriate treatment priorities had been used, the death toll in Wuhan and Lombardy would have been reduced by about 10% and 7%, respectively. The strategy depends on the epidemic scale and is more effective in countries with a younger population structure. Analyses of data from China, South Korea, Italy, and Spain suggest that countries with less per capita ICU medical resources should implement this strategy in the early stage of the pandemic to reduce mortalities. We emphasize that the results of this paper should be interpreted purely from a scientific and a quantitative-analysis point of view. No ethical implications are intended and meaningful.

Original languageEnglish (US)
Article number033209
JournalPhysical Review Research
Issue number3
StatePublished - Jul 2022

ASJC Scopus subject areas

  • Physics and Astronomy(all)


Dive into the research topics of 'Quantitative assessment of the effects of resource optimization and ICU admission policy on COVID-19 mortalities'. Together they form a unique fingerprint.

Cite this