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

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.

Automated summary

This paper introduces a deep learning-based online incremental learning technique using Artificial Neural Network (ANN) to develop an adaptive and non-intrusive analytical model of Covid-19 pandemic, which can analyze the temporal dynamics of disease spread in real-time. The model has been validated with historical data and provides a 30-day forecast of disease spread in the five worst affected states in India.