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Dynamics of COVID-19 outbreak in India are different from other countries for her huge population and different administrative approaches. This paper presents a six compartments (SHIQRD) model with different time delays between them, to analyse and forecast the COVID-19 pandemic in India. The model introduces separate compartments for 'home-quarantined', 'quarantined infectious', and 'undetected infectious' pertaining to the Indian scenario. It also incorporates incubation time, sample testing time, and recovery window as time delays between blocks. With the proposed model, reproduction number of different phases during lock-down are evaluated. Besides, dynamics of pandemic for the next one year are also explored. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]