Kavitha C, Gowrisankar A, Banerjee Santo
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu India.
Department of Mathematical Sciences, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
Eur Phys J Plus. 2021;136(5):596. doi: 10.1140/epjp/s13360-021-01586-7. Epub 2021 May 28.
An unprecedented upsurge of COVID-19-positive cases and deaths is currently being witnessed across India. According to WHO, India reported an average of 3.9 lakhs of new cases during the first week of May 2021 which equals 47% of new cases reported globally and 276 daily cases per million population. In this letter, the concept of SIR and fractal interpolation models is applied to predict the number of positive cases in India by approximating the epidemic curve, where the epidemic curve denotes the two-dimensional graphical representation of COVID-19-positive cases in which the abscissa denotes the time, while the ordinate provides the number of positive cases. In order to estimate the epidemic curve, the fractal interpolation method is implemented on the prescribed data set. In particular, the vertical scaling factors of the fractal function are selected from the SIR model. The proposed fractal and SIR model can also be explored for the assessment and modeling of other epidemics to predict the transmission rate. This letter investigates the duration of the second and third waves in India, since the positive cases and death cases of COVID-19 in India have been highly increasing for the past few weeks, and India is in a midst of a catastrophizing second wave. The nation is recording more than 120 million cases of COVID-19, but pandemics are still concentrated in most states. In order to predict the forthcoming trend of the outbreaks, this study implements the SIR and fractal models on daily positive cases of COVID-19 in India and its provinces, namely Delhi, Karnataka, Tamil Nadu, Kerala and Maharashtra.
目前,印度正经历着新冠病毒阳性病例和死亡人数前所未有的激增。据世界卫生组织称,印度在2021年5月的第一周平均报告了39万例新病例,这相当于全球报告的新病例的47%,即每百万人口中有276例每日病例。在这封信中,SIR模型和分形插值模型的概念被应用于通过逼近疫情曲线来预测印度的阳性病例数量,其中疫情曲线表示新冠病毒阳性病例的二维图形表示,横坐标表示时间,纵坐标表示阳性病例数量。为了估计疫情曲线,在规定的数据集中实施分形插值方法。具体而言,分形函数的垂直缩放因子从SIR模型中选择。所提出的分形模型和SIR模型也可用于评估和建模其他流行病,以预测传播率。这封信研究了印度第二波和第三波疫情的持续时间,因为在过去几周里,印度的新冠病毒阳性病例和死亡病例一直在大幅增加,印度正处于灾难性的第二波疫情之中。该国记录的新冠病毒病例超过1.2亿例,但疫情仍集中在大多数邦。为了预测疫情的未来趋势,本研究对印度及其德里、卡纳塔克邦、泰米尔纳德邦、喀拉拉邦和马哈拉施特拉邦等省份的新冠病毒每日阳性病例实施了SIR模型和分形模型。