用离散随机模型研究湖北新冠肺炎COVID-19流行病动力学特征

doi:10.7523/j.issn.2095-6134.2020.02.001

摘要/Abstract

摘要： 新冠肺炎COVID-19的爆发并在全国及世界范围内的扩散传播造成了巨大社会影响，研究流行病传播动力学特征有助于更好地对疫情进行掌控和防治。我们发展了一种离散变量随机概率方法，对湖北省疫情发展进行模拟和预测。首先根据排队论的Erlang概率分布对每日确诊人数进行处理，获得每日发病人数和感染人数。计算结果与中国疾病预防控制中心（CDC）已经整理公开的部分资料比较吻合，证明处理方法科学可信。进而依据每日发病人数，反演疫情发展不同阶段的有效传染率的变化，并据此预测未来疫情可能怎样发展。发现疫情初期基本传染数R₀从6.1减少到4.0，在武汉采取封城等有效措施后，有效R值减少到1之下，并逐步降低到0.13以下。发病高峰已经在2月初度过，目前虽然不排除疫情会有小的起伏，但只要坚持严格的隔离管控措施，总的趋势就不会变化。预期疫情在3月底前后结束，累计患病人数达到71000人左右。春节后回程的农民工和学生诱发大的疫情回弹可能性不大。但是世界上一些国家正处在疫情可能爆发的阶段，国家应该对入境人员做好检查和隔离管控工作。

关键词: 新冠肺炎COVID-19, 离散随机模型, 流行病动力学, Erlang概率分布

Abstract: The outbreak of New Coronary Pneumonia COVID-19 and its spread throughout the China and many foreign countries have produced a huge social impact. Studying the dynamic characteristics of epidemic transmission will help us better control and prevent the epidemic. We have developed a discrete stochastic method to simulate the evolvement of the epidemic in Hubei Province of China. Firstly, the daily confirmed number of patients was processed according to the Erlang probability distribution of the queuing theory, and the daily number of patients onset and infected were obtained. The results are compared with references recently published by Chinese CDC, validating the scientific credibility of the method. Then, the effective reproduction rates at different stages of the epidemic are inverted to fit the number of daily onsets, and predict the future trend of the epidemic. It was found that the basic reproduction number R₀ decreased from 6.1 to 4.0 at the initial stage of the epidemic. After taking dramatic measures to close the Wuhan city, the effective R value decreased below 1, and gradually decreased to below 0.13. The peak of the onset of patients has already passed in early February. Although there small fluctuations in the epidemic are not ruled out, the overall trend will not change as long as strict quarantine measures are adhered to. The epidemic is expected to end around the end of March and the cumulative number of patients will reach around 71,000. Migrant workers and students returning after the Spring Festival are unlikely to induce a large epidemic rebound. However, some countries in the world are at the stage of possible outbreaks, and China should pay attention to inspecting and quarantine international travelers.

Key words: COVID-19, discrete stochastic model, epidemic dynamics, Erlang probability distribution

中图分类号:

P541
O35

石耀霖, 程惠红, 黄禄渊, 任天翔. 用离散随机模型研究湖北新冠肺炎COVID-19流行病动力学特征[J]. 中国科学院大学学报, 2020, 37(2): 145-154.

SHI Yaolin, CHENG Huihong, HUANG Luyuan, REN Tianxiang. Using a discrete stochastic model to study the epidemic dynamics of COVID-19 in Hubei, China[J]. , 2020, 37(2): 145-154.

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