Huang Sisi, Zhu Anding, Wang Yan, Xu Yancong, Li Lu, Kong Dexing
School of Mathematical Sciences, Zhejiang University, Hangzhou 310027, China.
SH and AZ contributed equally to this study.
Infect Microbes Dis. 2020 May 5. doi: 10.1097/IM9.0000000000000026.
Regarding to the actual situation of the new coronavirus disease 2019 epidemic, social factors should be taken into account and the increasing growth trend of confirmed populations needs to be explained. A proper model needs to be established, not only to simulate the epidemic, but also to evaluate the future epidemic situation and find a pilot indicator for the outbreak.
The original susceptible-infectious-recover model is modified into the susceptible-infectious-quarantine-confirm-recover combined with social factors (SIDCRL) model, which combines the natural transmission with social factors such as external interventions and isolation. The numerical simulation method is used to imitate the change curve of the cumulative number of the confirmed cases and the number of cured patients. Furthermore, we investigate the relationship between the suspected close contacts (SCC) and the final outcome of the growth trend of confirmed cases with a simulation approach.
This article selects four representative countries, that is, China, South Korea, Italy, and the United States, and gives separate numerical simulations. The simulation results of the model fit the actual situation of the epidemic development and reasonable predictions are made. In addition, it is analyzed that the increasing number of SCC contributes to the epidemic outbreak and the prediction of the United States based on the population of the SCC highlights the importance of external intervention and active prevention measures.
The simulation of the model verifies its reliability and stresses that observable variable SCC can be taken as a pilot indicator of the coronavirus disease 2019 pandemic.
针对2019年新型冠状病毒病疫情的实际情况，应考虑社会因素并解释确诊人群不断增长的趋势。需要建立一个合适的模型，不仅用于模拟疫情，还用于评估未来疫情形势并找到疫情爆发的先导指标。
将原始的易感-感染-康复模型修改为结合社会因素的易感-感染-隔离-确诊-康复（SIDCRL）模型，该模型将自然传播与外部干预和隔离等社会因素相结合。采用数值模拟方法来模拟确诊病例累计数和治愈患者数的变化曲线。此外，我们用模拟方法研究疑似密切接触者（SCC）与确诊病例增长趋势最终结果之间的关系。
本文选取了四个具有代表性的国家，即中国、韩国、意大利和美国，并分别进行了数值模拟。模型的模拟结果符合疫情发展的实际情况并做出了合理预测。此外，分析得出SCC数量的增加促成了疫情爆发，基于SCC人群对美国的预测突出了外部干预和积极预防措施的重要性。
模型的模拟验证了其可靠性，并强调可观察变量SCC可作为2019年冠状病毒病大流行的先导指标。
