基于SIR模型的COVID-19统计分析

Proceedings of the 2020 4th International Conference on Computational Biology and Bioinformatics Pub Date : 2020-12-27 DOI:10.1145/3449258.3449261

Feng Liang, Ziyuan Li

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引用次数: 1

摘要

本文从统计角度对全球新冠肺炎疫情进行了分析。基于SIR模型，计算每个国家的感染率和恢复率，并将其作为聚类基础。因此，20个选定的国家被分成3组。本文还研究了在参数中加入噪声时动力学的变化，并针对两种情况给出了相应的策略和解决方法。敏感性分析表明，感染率对预后的影响更为显著。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Statistical Analysis on COVID-19 Based on SIR Model

This article analyzes global COVID-19 from a statistical perspective. Based on SIR Model, the rate of infectivity and recovery in each country are calculated, which are used to be a clustering basis. Thus, 20 selected countries are divided into 3 clusters. The article also studies how the dynamics would change when adding noise to parameters and give some policies and solutions based on two situations. Sensitivity analysis indicates that the infection rate plays a more significant role in outcomes.

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Proceedings of the 2020 4th International Conference on Computational Biology and Bioinformatics

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