A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India

被引：27

作者：

Farooq, Junaid ^{[1
]}

Bazaz, Mohammad Abid ^{[1
]}

机构：

[1] Natl Inst Technol Srinagar, Dept Elect Engn, Srinagar, Jammu & Kashmir, India

来源：

ALEXANDRIA ENGINEERING JOURNAL | 2021年 / 60卷 / 01期

关键词：

Covid-19; Incremental learning; ANN; Forecasting; TRANSMISSION DYNAMICS; INFLUENZA;

D O I：

10.1016/j.aej.2020.09.037

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, deep learning is employed to propose an Artificial Neural Network (ANN) based online incremental learning technique for developing an adaptive and non-intrusive analytical model of Covid-19 pandemic to analyze the temporal dynamics of the disease spread. The model is able to intelligently adapt to new ground realities in real-time eliminating the need to retrain the model from scratch every time a new data set is received from the continuously evolving training data. The model is validated with the historical data and a forecast of the disease spread for 30-days is given in the five worst affected states of India. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

引用

页码：587 / 596

页数：10

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