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

One issue regarding our analyses is that some COVID-19 deaths might be misreported such that the reported numbers of deaths represent a lower limit. For instance, 134 new deaths were suddenly counted from >13,000 clinically diagnosed patients in Hubei Province on February 12, 2020 (as clearly indicated by a sudden increase in deaths) (Fig. 1B) . Another uncertainty might result from those unidentified COVID-19 deaths that occurred in the early phase of the outbreak. We applied the Monte Carlo method (see the Methods section in the Supplementary Information online) to estimate such uncertainty assuming that the relative uncertainty of the reported numbers of deaths follows a single-sided normal distribution with a mean of 1.0 and a standard deviation of 2.5%. The potential total numbers of COVID-19 deaths were estimated to be 3 Supplementary Fig. 1 (online) . (F). Prediction of COVID deaths in Wuhan City by Boltzmann function-based analyses. The real data from January 21 to different closing dates were arbitrarily analyzed (colored lines), and the potential total numbers of deaths under these analyses are shown in insets. Real data ( â—‹ ) from March 1 to 19 agree well with the predicted data (dotted red lines) that were derived from the real data (â–ª) from January 21 to February 29.