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. 2020 Jul 20:727:138394.
doi: 10.1016/j.scitotenv.2020.138394. Epub 2020 Apr 8.

Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm

Lishi Wang  1 , Jing Li  2 , Sumin Guo  3 , Ning Xie  4 , Lan Yao  5 , Yanhong Cao  6 , Sara W Day  7 , Scott C Howard  7 , J Carolyn Graff  7 , Tianshu Gu  8 , Jiafu Ji  9 , Weikuan Gu  10 , Dianjun Sun  11
Affiliations

Real-time estimation and prediction of mortality caused by COVID-19 with patient information based algorithm

Lishi Wang et al. Sci Total Environ. .

Abstract

The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6 days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries.

Keywords: COVID-19; Coronavirus; Death rate; Inpatient; Normal distribution; Prediction.

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Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
Duration distribution of 33 death cases in Wuhan. A. Distribution of days between disease symptoms and death and between time of ICU admission and death. Vertical axis: days, Horizontal axis: cases. B. Estimated days from first symptoms to death and days from ICU admission to death. C. Lagging days (days from first symptoms to the day of death), μ, μ ± σ and μ ± 2σ and their weight (in percentages) used for the estimation of death rate in the broader patient population. Note: Among these values above, the lagging day μ − 2σ from symptom confirmation to death in panel B that equals to −3 has been set to 0.
Fig. 2
Fig. 2
The estimated death rate in Mainland China and Hubei Province. The blue curve represents the mortality calculated by the actual increase in deaths per lagging day divided by the increase in actual patients on the previous corresponding day. The gray curve represents the total number of deaths per lagging day, divided by the total number of identified actual patients on the corresponding previous day. The orange curve shows the number of deaths per day divided by the total number of patients the same day. The number on the vertical bar represents the death rate; the number on the horizontal bar shows the date. A: Overall death rate in Mainland China. B: Death rate in Hubei province. C. The death rate in the rest of the country except Hubei province. D: Death rate in Wuhan city.
Fig. 3
Fig. 3
Death rate estimations of four places. The blue curve represents the mortality calculated by the actual increase in deaths per lagging day divided by the increase in actual patients on the previous corresponding day. The gray curve represents the total number of deaths per lagging day, divided by the total number of identified actual patients on the corresponding previous day. The orange curve shows the number of deaths per day divided by the total number of patients the same day. Numbers on the vertical axis represent the death rate; on the horizontal axis is the date. A. The death rate of Xiaogan city in Hubei province B. Death rate of Huanggang city in Hubei province. C. The death rate in Heilongjiang province. D. The death rate in Harbin city.
Fig. 4
Fig. 4
Comparison between the predicted number of deaths based on PIBA and the actual number of deaths. The blue color represents the estimated minimum number of deaths line. The orange color represents the estimated maximum number of deaths line. The gray line represents the actual number of deaths. Panels A, B, C, and D showed these death numbers in the country, Hubei, Wuhan and the rest of country except Hubei.
Fig. 5
Fig. 5
Test PIBA model using COVID-19 population from South Korea. A. Estimation of death rate in the Korean population using the PIBA method. The blue curve represents the mortality calculated by the actual increase in deaths per lagging day divided by the increase in actual patients on the previous corresponding day. The gray curve represents the total number of deaths per lagging day, divided by the total number of identified actual patients on the corresponding previous day. The orange curve shows the number of deaths per day divided by the total number of patients the same day. The number on the vertical bar represents the death rate, number on the horizontal bar shows the date. B. Comparison between the predicted number of deaths based on PIBA and the actual number of deaths. The blue color represents the estimated minimum number of deaths line. The orange color represents the estimated maximum number of deaths line. The gray line represents the actual deaths.
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