论文标题
使用SEIR模型对印度Covid19爆发的分析
Analysis of COVID19 Outbreak in India using SEIR model
论文作者
论文摘要
由于其多功能人群以及气象数据分布，印度CoVID19病毒的扩散模式的预测非常困难。全球的各种研究人员都试图将这些数据的相互依赖性与印度Covid19案件的扩散模式相关联。但是很难预测确切的模式，尤其是活动案例数量的峰值。在本文中，我们试图使用广义SEIR模型来预测印度有效，恢复，死亡和Covid19的总案例的数量。在我们的预测中，活动案例曲线的峰值发生与实际数据中的峰非常匹配（仅一周的差异）。尽管预测案件的数量与实际情况数的数量有所不同（由于从2020年6月开始逐渐解锁运动限制），但实际和预测时间（在活动案例曲线的峰值中）的相似之处相似，使该模型相对适合分析印度的COVID19爆发。
The prediction of spread patterns of COVID19 virus in India is very difficult due to its versatile demographic as well as meteorological data distribution. Various researchers across the globe have attempted to correlate the interdependency of these data with the spread pattern of COVID19 cases in India. But it is hard to predict the exact pattern, especially the peak in the number of active cases. In the present article we have tried to predict the number of active, recovered, death and total cases of COVID19 in India using generalized SEIR model. In our prediction, the occurrence of peak in the active cases curve has a very close match with the peak in the real data (difference of only one week). Although the number of predicted cases differs with the real number of cases (due to unlocking the movement restrictions gradually from June 2020 onwards), the close resemblance in the actual and predicted time (in the peak of active cases curve) makes this model relatively suitable for analysis of COVID19 outbreak in India.
