基于网络SEIR模型的印度新冠肺炎疫情防控措施实施与阶段性放松分析

Piklu Mallick, Sourav K. Bhowmick, S. Panja
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引用次数: 1

摘要

本文进行了一项调查,分析了周期性封锁和解锁如何帮助印度抗击第一波COVID-19。为此,考虑建立一个网络化的SEIR模型,以捕捉印度16个受影响最严重的邦的疾病传播动态。为此,构造了一个基于距离的接触矩阵来反映状态之间的连通性。估计了模型的各种速率参数,并找出了每个阶段16个状态中每个状态的基本再现数$(\mathscr{R}_{0})$。最后,将估计参数模拟的累计感染病例数与印度截至2020年12月31日的实际COVID-19数据进行了比较,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of Imposition and Periodic Relaxation of Lockdown on the Spread of COVID-19 in India through Networked SEIR Model
In this paper, an investigation is carried out to analyse how periodic lockdown and unlocking have helped India to combat the first wave of COVID-19. To that end, a networked SEIR model is considered that captures the spreading dynamics of the disease in sixteen of the worst affected states of India. In this regard, a distance based contact matrix is constructed to reflect the connectivity between states. Various rate parameters of the model are estimated as well as the basic reproduction number $(\mathscr{R}_{0})$ of each of the sixteen states for each phase of lockdown is found out. Finally, a comparison is drawn between the simulated results of cumulative infected caseload using the estimated parameters and that with the real COVID-19 data of India till December 31, 2020, which establishes the effectiveness of the method.
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