论文标题
基于SEIR和回归模型的COVID-19爆发预测
SEIR and Regression Model based COVID-19 outbreak predictions in India
论文作者
论文摘要
Covid-19-大流行已成为对该国的主要威胁。到目前为止，无法治愈该疾病经过良好测试的药物或解毒剂。根据WHO报告，Covid-19是一种严重的急性呼吸道综合征，通过呼吸液滴和接触路线传播。对这种疾病的分析需要政府的主要关注，以减少这一全球大流行的影响，采取必要的步骤。在这项研究中，直到2020年3月30日，已经对印度进行了分析，并对未来两周的病例数进行了预测。 SEIR模型和回归模型已根据2020年1月30日至2020年3月30日在约翰·霍普金斯大学存储库中收集的数据进行预测。使用RMSLE评估了模型的性能，并为SEIR模型实现了1.52，而回归模型的性能为1.75。发现SEIR模型和回归模型之间的RMSLE错误率为2.01。同样，R0的值是该疾病的扩散为2.02。在接下来的两周内，预期病例可能会增加5000-6000。这项研究将帮助政府和医生在接下来的两周内准备他们的计划。根据短期间隔的预测，可以在长期间隔内调整这些模型以进行预测。
COVID-19 pandemic has become a major threat to the country. Till date, well tested medication or antidote is not available to cure this disease. According to WHO reports, COVID-19 is a severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Analysis of this disease requires major attention by the Government to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks. SEIR model and Regression model have been used for predictions based on the data collected from John Hopkins University repository in the time period of 30th January 2020 to 30th March 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0 which is the spread of the disease was calculated to be 2.02. Expected cases may rise between 5000-6000 in the next two weeks of time. This study will help the Government and doctors in preparing their plans for the next two weeks. Based on the predictions for short-term interval, these models can be tuned for forecasting in long-term intervals.
