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.

中文翻译：

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在印度五个受影响最严重的州中，用于建模和预测COVID-19的深度学习算法

在本文中，深度学习用于提出一种基于人工神经网络（ANN）的在线增量学习技术，用于开发Covid-19大流行的适应性和非介入性分析模型，以分析疾病传播的时间动态。该模型能够实时智能地适应新的地面现实，而无需每次从不断发展的训练数据中收到新数据集时都从头开始重新训练模型。该模型已通过历史数据进行了验证，并给出了印度五个受影响最严重州的30天疾病传播预测。