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The pandemic of 2019 novel coronavirus (SARS-CoV-2019), reminiscent of the 2002-SARS-CoV outbreak, has completely isolated countries, disrupted health systems and partially paralyzed international trade and travel. In order to be better equipped to anticipate transmission of this virus to new regions, it is imperative to track the progress of the virus over time. This review analyses information on progression of the pandemic in the past 3 months and systematically discusses the characteristics of SARS-CoV-2019 virus including its epidemiologic, pathophysiologic, and clinical manifestations. Furthermore, the review also encompasses some recently proposed conceptual models that estimate the spread of this disease based on the basic reproductive number for better prevention and control procedures. Finally, we shed light on how the virus has endangered the global economy, impacting it both from the supply and demand side.

Highlights Deep learning data-driven analytical model of Covid-19 pandemic to study disease transmission and prevention mechanism. Artificial Neural Network (ANN) based adaptive incremental learning technique used for model parameter learning and model updating with evolving training data. Simulation and stability analysis of different epidemic control strategies. An effective strategy to minimize the number of deaths through controlled natural immunization in absence of availability of vaccination at mass level.

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.