Epidemic Computational Model using Machine Learning for COVID-19

Abstract

The world is under tremendous pressure and lock down in major giant countries due to ongoing coronavirus COVID-19 originated from Wuhan city of China. Corona is the name of a virus group that is found in humans and animals. They range from simple cold coughs to coronary viruses to serious illnesses such as SARS or MERS. The corona virus found in Wuhan city , China (2019), is different from the six to seven viruses previously reported in humans. So it's called the Novel Corona virus (COVID-19).

The Module which is popular for pandemic prediction diseases Susceptible, Exposed, Infectious, Recovered i.e. SEIR is the modified model of SEI which is the simplest epidemiologic model presents the reality of epidemic growth with reference to the populated cases.  In this paper, the SEIR computational model analyzed by considering the total recovered cases again get 10 % susceptible. Also the interventions of authority get flatten the susceptible population by 40 % due to remedial intervention of concern authority. The results achieved are predicting that the peak of Infectious population will be in median of June in India.