Gupta Aakansha, Katarya Rahul
Big Data Analytics and Web Intelligence Laboratory, Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India.
Indian J Phys Proc Indian Assoc Cultiv Sci (2004). 2023;97(2):389-399. doi: 10.1007/s12648-022-02425-w. Epub 2022 Jul 14.
After a consistent drop in daily new coronavirus cases during the second wave of COVID-19 in India, there is speculation about the possibility of a future third wave of the virus. The pandemic is returning in different waves; therefore, it is necessary to determine the factors or conditions at the initial stage under which a severe third wave could occur. Therefore, first, we examine the effect of related multi-source data, including social mobility patterns, meteorological indicators, and air pollutants, on the COVID-19 cases during the initial phase of the second wave so as to predict the plausibility of the third wave. Next, based on the multi-source data, we proposed a simple short-term fixed-effect multiple regression model to predict daily confirmed cases. The study area findings suggest that the coronavirus dissemination can be well explained by social mobility. Furthermore, compared with benchmark models, the proposed model improves prediction by 33.6%, 10.8%, 27.4%, and 19.8% for Maharashtra, Kerala, Karnataka, and Tamil Nadu, respectively. Thus, the simplicity and interpretability of the model are a meaningful contribution to determining the possibility of upcoming waves and direct pandemic prevention and control decisions at a local level in India.
在印度第二波新冠疫情期间，每日新增新冠病毒病例持续下降之后，人们猜测未来可能会出现第三波疫情。这场大流行正以不同的波次卷土重来；因此，有必要确定在初始阶段可能引发严重第三波疫情的因素或条件。所以，首先，我们研究了包括社会流动模式、气象指标和空气污染物在内的相关多源数据在第二波疫情初始阶段对新冠病例的影响，以便预测第三波疫情的可能性。接下来，基于多源数据，我们提出了一个简单的短期固定效应多元回归模型来预测每日确诊病例。研究区域的结果表明，新冠病毒的传播可以通过社会流动得到很好的解释。此外，与基准模型相比，所提出的模型在马哈拉施特拉邦、喀拉拉邦、卡纳塔克邦和泰米尔纳德邦的预测分别提高了33.6%、10.8%、27.4%和19.8%。因此，该模型的简单性和可解释性对于确定印度未来疫情波次的可能性以及在地方层面直接进行疫情防控决策具有重要意义。
