Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China

Background.  The epidemic outbreak caused by coronavirus COVID-19 is of great interest to researches because of the high rate of the infection spread and the significant number of fatalities. A detailed scientific analysis of the phenomenon is yet to come, but the public is already interested in th...

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Published in:Innovative Biosystems and Bioengineering
Main Author: Igor Nesteruk
Format: Article
Language:English
Published: Igor Sikorsky Kyiv Polytechnic Institute 2020-02-01
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Online Access:https://ibb.kpi.ua/article/view/195074
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author Igor Nesteruk
author_facet Igor Nesteruk
author_sort Igor Nesteruk
collection DOAJ
container_title Innovative Biosystems and Bioengineering
description Background.  The epidemic outbreak caused by coronavirus COVID-19 is of great interest to researches because of the high rate of the infection spread and the significant number of fatalities. A detailed scientific analysis of the phenomenon is yet to come, but the public is already interested in the questions of the epidemic duration, the expected number of patients and deaths. Long-time predictions require complicated mathematical models that need a lot of effort to identify and calculate unknown parameters. This article will present some preliminary estimates. Objective. Since the long-time data are available only for mainland China, we will try to predict the epidemic characteristics only in this area. We will estimate some of the epidemic characteristics and present the dependen­cies for victim numbers, infected and removed persons versus time. Methods. In this study we use the known SIR model for the dynamics of an epidemic, the known exact solution of the linear differential equations and statistical approach developed before for investigation of the children disease, which occurred in Chernivtsi (Ukraine) in 1988–1989.  Results. The optimal values of the SIR model parameters were identified with the use of statistical approach. The numbers of infected, susceptible and removed persons versus time were predicted and compared with the new data obtained after February 10, 2020, when the calculations were completed. Conclusions. The simple mathematical model was used to predict the characteristics of the epidemic caused by coronavirus in mainland China. Unfortunately, the number of coronavirus victims is expected to be much higher than that predicted on February 10, 2020, since 12289 new cases (not previously included in official counts) have been added two days later. Further research should focus on updating the predictions with the use of up-to-date data and using more complicated mathematical models.
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spelling doaj-art-d0fedc6dffdf47dbbb6bf448fa4e31c22025-09-18T09:35:18ZengIgor Sikorsky Kyiv Polytechnic InstituteInnovative Biosystems and Bioengineering2616-177X2020-02-014110.20535/ibb.2020.4.1.195074195074Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland ChinaIgor Nesteruk0Institute of Hydromechanics, National Academy of Sciences of Ukraine; Igor Sikorsky Kyiv Polytechnic Institute Background.  The epidemic outbreak caused by coronavirus COVID-19 is of great interest to researches because of the high rate of the infection spread and the significant number of fatalities. A detailed scientific analysis of the phenomenon is yet to come, but the public is already interested in the questions of the epidemic duration, the expected number of patients and deaths. Long-time predictions require complicated mathematical models that need a lot of effort to identify and calculate unknown parameters. This article will present some preliminary estimates. Objective. Since the long-time data are available only for mainland China, we will try to predict the epidemic characteristics only in this area. We will estimate some of the epidemic characteristics and present the dependen­cies for victim numbers, infected and removed persons versus time. Methods. In this study we use the known SIR model for the dynamics of an epidemic, the known exact solution of the linear differential equations and statistical approach developed before for investigation of the children disease, which occurred in Chernivtsi (Ukraine) in 1988–1989.  Results. The optimal values of the SIR model parameters were identified with the use of statistical approach. The numbers of infected, susceptible and removed persons versus time were predicted and compared with the new data obtained after February 10, 2020, when the calculations were completed. Conclusions. The simple mathematical model was used to predict the characteristics of the epidemic caused by coronavirus in mainland China. Unfortunately, the number of coronavirus victims is expected to be much higher than that predicted on February 10, 2020, since 12289 new cases (not previously included in official counts) have been added two days later. Further research should focus on updating the predictions with the use of up-to-date data and using more complicated mathematical models. https://ibb.kpi.ua/article/view/195074Coronavirus epidemic in ChinaCoronavirus COVID-19Coronavirus 2019-nCoVMathematical modeling of infection diseasesSIR modelParameter identification
spellingShingle Igor Nesteruk
Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China
Coronavirus epidemic in China
Coronavirus COVID-19
Coronavirus 2019-nCoV
Mathematical modeling of infection diseases
SIR model
Parameter identification
title Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China
title_full Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China
title_fullStr Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China
title_full_unstemmed Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China
title_short Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China
title_sort statistics based predictions of coronavirus epidemic spreading in mainland china
topic Coronavirus epidemic in China
Coronavirus COVID-19
Coronavirus 2019-nCoV
Mathematical modeling of infection diseases
SIR model
Parameter identification
url https://ibb.kpi.ua/article/view/195074
work_keys_str_mv AT igornesteruk statisticsbasedpredictionsofcoronavirusepidemicspreadinginmainlandchina