Forecasting the cumulative number of COVID-19 deaths in China: a Boltzmann function-based modeling study

Gao, Yuanyuan; Zhang, Zuqin; Yao, Wei; Ying, Qi; Long, Cheng; Fu, Xinmiao

Because the crude fatality ratio in the epicenter of the outbreak is much higher than that in other provinces of mainland China, 1,2 there is a great potential for government to optimize preparedness, therapeutic treatments, and medical resource supplies. In this way, hundreds of lives of COVID-19 patients, particularly severe and critically ill patients, might be saved. 2, 9 In addition, our estimates on the course of COVID-19 deaths (see Supplementary Table S2 online) may aid in providing mental health services to family members of those COVID-19 patients who pass away. 10 Supplementary material. To view supplementary material for this article, please visit https://doi.org/10.1017/ice.2020.101 Note. SD, standard deviation; CI, confidence interval. a Boltzmann function-based regression analysis results assuming the reported cumulative number of deaths (from January 21 to February 29) are precise and have uncertainty (SD, 2.5%), respectively (see the Methods section in the Supplementary Information online and Fig. 1E and Supplementary Fig. 1 online) . The same uncertainty was set for Richards function-based regression analysis (see the Methods section and Supplementary Fig. 2 online) . b Officially reported cumulative numbers of COVID-19 deaths as of March 19, 2020.