从早期数据预测中国 COVID-19 疫情的累计病例数。

Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data.

机构信息

School of Mathematical Sciences, Beijing Normal University. Beijing 100875, China.

Université de Bordeaux, IMB, UMR 5251, F-33400 Talence, France.

出版信息

Math Biosci Eng. 2020 Apr 8;17(4):3040-3051. doi: 10.3934/mbe.2020172.

DOI:10.3934/mbe.2020172

PMID:32987515

Abstract

We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases.

摘要

我们对中国的 COVID-19 冠状病毒疫情进行建模。我们使用早期报告的病例数据来预测报告病例的累计数量达到最终规模。我们模型的关键特征是实施主要公共政策限制社会流动的时间、识别和隔离未报告的病例，以及无症状感染病例的影响。