Liu Zhihua, Magal Pierre, Seydi Ousmane, Webb Glenn
School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China.
Université de Bordeaux, IMB, UMR 5251, F-33400 Talence, France.
Biology (Basel). 2020 Mar 8;9(3):50. doi: 10.3390/biology9030050.
We develop a mathematical model to provide epidemic predictions for the COVID-19 epidemic in Wuhan, China. We use reported case data up to 31 January 2020 from the Chinese Center for Disease Control and Prevention and the Wuhan Municipal Health Commission to parameterize the model. From the parameterized model, we identify the number of unreported cases. We then use the model to project the epidemic forward with varying levels of public health interventions. The model predictions emphasize the importance of major public health interventions in controlling COVID-19 epidemics.
我们建立了一个数学模型，用于对中国武汉的新冠疫情进行流行预测。我们使用了截至2020年1月31日来自中国疾病预防控制中心和武汉市卫生健康委员会报告的病例数据来对模型进行参数化。从参数化模型中，我们确定未报告病例的数量。然后，我们使用该模型在不同程度的公共卫生干预措施下对疫情进行预测。模型预测强调了重大公共卫生干预措施在控制新冠疫情中的重要性。
