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

TL;DR

This study develops a non-Markovian model to quantitatively assess how ICU capacity expansion and prioritizing younger COVID-19 patients can reduce mortality, providing scenario-based predictions validated with data from Wuhan and Lombardy.

Contribution

It introduces a comprehensive model that predicts COVID-19 death tolls under different resource and policy scenarios, validated with real-world data from multiple epicenters.

Findings

Prioritizing younger patients can reduce deaths by up to 10.4%.

Expanding ICU capacity and early intervention are more effective in countries with younger populations.

The model accurately predicts daily death tolls in Wuhan and Lombardy.

Abstract

It is evident that increasing the intensive-care-unit (ICU) capacity and giving priority to admitting and treating younger patients will reduce the number of COVID-19 deaths, but a quantitative assessment of these measures has remained inadequate. We develop a comprehensive, non-Markovian state transition model, which is validated through 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 treatment priorities had been given to younger patients, the death toll in Wuhan and Lombardy would have been reduced by 10.4\% and 6.7\%, 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.