Development, Prediction and Control of the COVID-19 in 2021 China
1. School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai 201620, China
2. College of Finance, Lancaster University, Lancaster LA2 0PF, UK
| Abstract: | Since the discovery of COVID-19 at the end of 2019, COVID-19 has begun to circulate in various countries around the world, and the virus strains are constantly mutating. The policy of "dynamic zero clearing" implemented in China has achieved good epidemic prevention effect. In the spring and summer of 2021, the local recurrent strains in Chinese Mainland and the imported variants Delta strain and Delta+ strains formed another wave of epidemic. Objective: In order to further study the changes of important parameters such as the basic reproduction number and infection rate of mixed strains, the number risk of confirmed cases and asymptomatic cases in the future was predicted according to the SAIR mathematical model. The effects of various epidemic prevention measures are discussed. Methods: Formula calculation and data fitting were used. Results: The values of important parameters were calculated again, and the effect data of epidemic prevention measures were discussed. Conclusion: The number of confirmed cases, the number of asymptomatic infections, and the development of the number of cases simulated by our SAIR model are in good agreement with the actual data. The new infection parameters and other data show that the infectivity of the new mixed strains is much stronger than that of the original strains in the past. In particular, the number of asymptomatic infections has increased significantly. We need to draw everyone's attention. The simulation shows that all strict epidemic prevention measures are effective, and strict epidemic prevention should be adhered to. |
| Keywords: | COVID-19; SAIR Model; Delta Strain; Confirmed Case; Asymptomatic Case |
| DOI: | 10.57237/j.mrf.2022.01.005 |
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Received: 30 August 2022
Accepted: 11 October 2022
Published Online: 5 January 2023
References
| [1] | 崔恒建, 胡涛. 新型冠状病毒肺炎疫情预测预报的非线性回归方法 [J]. 中国科学: 数学, 2020, 50: 1–12. |
| [2] | 郑丽晖, 陈建宝, 陈晓平.新型冠状病毒传播的统计建模与风险预测 [J]. 应用数学学报, 2020, 43: 324-334. |
| [3] | 张仲华, 张靖茹, 刘叶玲. 一类具有两种控制策略的SIR非连续传染病模型动力学研究 [J]. 应用数学学报, 2020, 43: 897-914. |
| [4] | Tian B C, Yuan R. Traveling waves for a diffusive SEIR epidemic model with standard incidences [J]. Science China Mathematics, 2017, 60: 813-832. |
| [5] | Chen D X, Yang Y F, Zhang Y F, Yu W W. Prediction of COVID-19 spread by sliding mSEIR observer. Science China Information Sciences 2020; accepted.doi: 10.1007/s11432-020-3034-y. |
| [6] | Simone A D, Piangerelli M. A bayesian approach for monitoring epidemics in presence of undetected cases [J]. Chaos, Solitons and Fractals, 2020; accepted. https://doi.org/10.1016/j.chaos.2020.110167. |
| [7] | Altan A, Karasu S, Bekiros S. Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques [J]. Chaos, Solitons and Fractals, 2019, 126: 325-336. |
| [8] | Liu Y, Tang X Q. COVID-19: Is it safe now? Study of asymptomatic infection spread and quantity risk based on SAIR model [J]. Chaos, Solitons and Fractals X, 2021, 6: 1-6. |
| [9] | 中华人民共和国卫生健康委. 新冠病毒肺炎疫情最新情况[EB/OL]. [2021-08-21]. http://www.nhc.gov.cn/xcs/yqtb/202108/871a5f07abea4a81b21de629d4ce271e.shtml |
| [10] | World Health Organization. Coronavirus disease (COVID-2019) situation reports-28 [EB/OL]. [2020-02-18]. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports |
| [11] | Tang X Q, Liu N Z, Wang H X, Bai Y Q, A class of new two-variable chromatic polynomials [J]. J. Lanzhou University (Natural Sciences), 2012, 48: 106–112. |
| [12] | Tang X Q, Liu N Z, Wang H X, Bai Y Q, The two-variable chromatic polynomial of regular tree study [J]. Acta Mathematicae Applicatae Sinica, 2013, 36: 761-768. |
| [13] | Tang X Q. New expected value expansions of rooted graphs [J]. Acta Mathematicae Applicatae Sinica，English Series, 2015, 31: 81-88. |
| [14] | Tang X Q, Wang H X. Stepwise confidence interval method for finding drug's minimum effective dose in trial [J]. Drug Evaluation Research, 2010, 33: 58-62. |
| [15] | Tang X Q, Bai Y Q, Liu N Z, Liu Y. Markowitz portfolio model based on random matrix theory [J]. J. Shanghai University (Natural Science), 2013, 19: 293-297. |
| [16] | Liu N Z, Tang X Q, Wang H X. On reliability of two probability in regular q-tree rooted graph [J]. J. Southwest China Normal University (Natural Science Edition), 2013, 38: 24-27. |
| [17] | Liu Y, Tang X Q. Comparison of some classes of two-variable chromatic polynomials [J]. Journal of Natural Science of Hunan Normal University, 2014, 37: 67-72. |
| [18] | Liu Y, Tang X Q. Reliability study of regular rooted q-tree [J]. J. Northeast Normal University, 2015, 47: 17-21. |
| [19] | Tang X Q, Liu Y and Liu N Z. Reliability and stability study of rooted net [J]. Journal of Chongqing Normal University (Natural Science), 2017, 34: 65-71. |
| [20] | Tang X Q, Wang B. On stability of rooted graph and its optimality [J]. J. Southwest China Normal University (Natural Science Edition), 2017, 42: 14-19. |
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