Abstract
Coronavirus disease 2019 (COVID-19) is a respiratory ailment that can spread from individual to individual, first recognized during an outbreak in Wuhan, China. Possibility of getting COVID-19 is greater for people who are in contact of someone known to have COVID-19, for example clinical professional, or family member. Peoples at higher peril for sickness are the those who live in or have starting late been in a zone with advancing spread of COVID-19. A few patients have pneumonia in the two lungs, multi-organ failure and sometimes death. In this work we are predicting the impact of COVID-19 cases in India based on time series analysis, correlation analysis, Granger Test, and Group Method of Data Handling (GMDH). We have compared the prediction of four algorithms namely combinatorial (quick), stepwise forward, stepwise mixed, and GMDH neural network (GMDH-NN) for predicting the future of India. Out of that stepwise mixed method gives good prediction for confirmed cases but GMDH-NN gives better prediction in case of death and recovered cases. As this disease is declared as an epidemic, the present study will help researchers to understand the impact of this outbreak. We have used combinatorial (quick), stepwise forward selection, stepwise mixed selection, and GMDH neural network to predict the spread of disease in India.
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Sujatha, R., Chatterjee, J.M. (2021). Role of Artificial Intelligence in COVID-19 Prediction Based on Statistical Methods. In: Nandan Mohanty, S., Saxena, S.K., Satpathy, S., Chatterjee, J.M. (eds) Applications of Artificial Intelligence in COVID-19 . Medical Virology: From Pathogenesis to Disease Control. Springer, Singapore. https://doi.org/10.1007/978-981-15-7317-0_5
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