Ann Infect Dis Epidemiol | Volume 5, Issue 1 | Research Article | Open Access

Mathematical Modeling and Epidemic Prediction of COVID-19 and Its Significance to Epidemic Prevention and Control Measures

Yichi Li1, Bowen Wang2, Ruiyang Peng3, Chen Zhou4, Yonglong Zhan5, Zhuoxun Liu6, Xia Jiang7 and Bin Zhao1*

1School of Science, Hubei University of Technology, China 2School of Electrical and Electronic Engineering, Hubei University of Technology, China 3School of Computer, Hubei Polytechnic University, China 4School of Economics and Management, Hubei University of Technology, China 5School of Computer Science, Hubei University of Technology, China 6Normal School of Vocational and Technical Education, HuBei University of Technology, China 7Hospital, Hubei University of Technology, China

*Correspondance to: Bin Zhao 

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Abstract

Background: Since receiving unexplained pneumonia patients at the Jinyintan Hospital in Wuhan, China in December 2019, the new coronavirus (COVID-19) has rapidly spread in Wuhan, China and spread to the entire China and some neighboring countries. We establish the dynamics model of infectious diseases and time series model to predict the trend and short-term prediction of the transmission of COVID-19, which will be conducive to the intervention and prevention of COVID-19 by departments at all levels in mainland China and buy more time for clinical trials. Methods: Based on the transmission mechanism of COVID-19 in the population and the implemented prevention and control measures, we establish the dynamic models of the six chambers, and establish the time series models based on different mathematical formulas according to the variation law of the original data. Findings: The results based on time series analysis and kinetic model analysis show that the cumulative diagnosis of pneumonia of COVID-19 in mainland China can reach 36,343 after one week (February 8, 2020) and the number of basic regeneration can reach 4.01. The cumulative number of confirmed diagnoses will reach a peak of 87,701 on March 15, 2020; the number of basic regeneration in Wuhan will reach 4.3, and the cumulative number of confirmed cases in Wuhan will reach peak at 76,982 on March 20. Whether in Mainland China or Wuhan, both the infection rate and the basic regeneration number of COVID-19 continue to decline, and the results of the sensitivity analysis show that the time it takes for a suspected population to be diagnosed as a confirmed population can have a significant impact on the peak size and duration of the cumulative number of diagnoses. Increased mortality leads to additional cases of pneumonia, while increased cure rates are not sensitive to the cumulative number of confirmed cases. Interpretation: Chinese governments at various levels have intervened in many ways to control the epidemic. According to the results of the model analysis, we believe that the emergency intervention measures adopted in the early stage of the epidemic, such as blocking Wuhan, restricting the flow of people in Hubei province, and increasing the support to Wuhan, had a crucial restraining effect on the original spread of the epidemic. It is a very effective prevention and treatment method to continue to increase investment in various medical resources to ensure that suspected patients can be diagnosed and treated in a timely manner. Based on the results of the sensitivity analysis, we believe that enhanced treatment of the bodies of deceased patients can be effective in ensuring that the bodies themselves and the process do not result in additional viral infections, and once the pneumonia patients with the COVID-19 are cured, the antibodies left in their bodies may prevent them from reinfecting COVID-19 for a longer period of time.

Keywords:

New coronavirus; Infection prediction; Infection prevention and control; ARIMAX Model; SEIR Model

Citation:

Li Y, Wang B, Peng R, Zhou C, Zhan Y, Liu Z, et al. Mathematical Modeling and Epidemic Prediction of COVID-19 and Its Significance to Epidemic Prevention and Control Measures. Ann Infect Dis Epidemiol. 2020; 5(1): 1052.

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