Statistics-Based Predictions of Coronavirus Epidemic Spreading in Mainland China
DOI:
https://doi.org/10.20535/ibb.2020.4.1.195074Keywords:
Coronavirus epidemic in China, Coronavirus COVID-19, Coronavirus 2019-nCoV, Mathematical modeling of infection diseases, SIR model, Parameter identification, Statistical methodsAbstract
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.
References
Timeline of the COVID-19 pandemic. En.wikipedia.org. Available from: https://en.wikipedia.org/wiki/Timeline_of_the_COVID-19_pandemic
Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet. 2020;395(10225):689-97. DOI: 10.1016/S0140-6736(20)30260-9
Zhao S, Lin Q, Ran J, Musa SS, Yang G, Wang W, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. International Journal of Infectious Diseases. 2020;92:214-7. DOI: 10.1016/j.ijid.2020.01.050
Nesteruk IG. Statistics Based Models for the Dynamics of Chernivtsi Children Disease. Research Bulletin of the National Technical University of Ukraine "Kyiv Politechnic Institute". 2017;(5):26-34. DOI: 10.20535/1810-0546.2017.5.108577
Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character. 1927;115(772):700-21. DOI: 10.1098/rspa.1927.0118
Murray JD (eds). Mathematical Biology: I. An Introduction, vol. 17. New York, NY: Springer New York; 2002. DOI: 10.1007/b98868
Bailey NTJ. The Mathematical Theory of Epidemics. London: Charles Griffin; 1957, 202 p.
Langemann D, Nesteruk I, Prestin J. Comparison of mathematical models for the dynamics of the Chernivtsi children disease. Mathematics and Computers in Simulation. 2016;123:68-79. DOI: 10.1016/j.matcom.2016.01.003
Nesteruk I. Maximal Speed of Underwater Locomotion. Innovative Biosystems and Bioengineering. 2019;3(3):152-67. DOI: 10.20535/ibb.2019.3.3.177976
Draper NR, Smith H. Applied Regression Analysis. 3rd ed. Wiley; 1998. DOI: 10.1002/9781118625590
Nesteruk I. Statistics based predictions of coronavirus 2019-nCoV spreading in mainland China. MedRxiv, 2020. Preprint. DOI: 10.1101/2020.02.12.20021931
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