Etiology management and COVID-19 death forecast using artificial intelligent based Bayesian learning approach

Mohd Saqib, Saleem M. Khan, Danish Suhail


In 2020, coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 (severe acute respiratory syndrome corona virus 2) coronavirus, has become a worldwide natural disaster and has taken form of pandemic. Due to this unforeseen pandemic, humanity had faced many challenges and realized the capability of a human being is not enough to survive in such circumstances. Here, artificial intelligence (AI) comes into the picture which can perform beyond human abilities and enhance the capabilities of a human especially the doctors and health care system. The proposed work is a survey of the areas of COVID-19 where AI can play a big role. There are some areas: death cases prediction, virus progression prediction, disease detection, and drug discovery. In this study we have discussed above mentioned issues and presented a Bayesian learning model to forecast death cases in India. The proposed model predicted with good accuracy and also takes care of uncertainty of predictions. We have fitted Bayesian learning on n-polynomial. This is a completely mathematical model in which we have successfully incorporated with prior knowledge and posterior distribution enables us to incorporate more upcoming data without storing previous data. Our forecast in this study is based on the public datasets provided by John Hopkins University. We are concluding with further evolution and scope of the proposed model.


COVID-19 pandemic, Bayesian leaning, Prediction, Etiology management

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