An Artificial Intelligence Enabled Model to Minimize Corona Virus Variant Infection Spreading

Many nations including India are being very badly affected by the second wave of the COVID-19 infections. The critical situation prevails in some states and cities of India. The mortality rate varies state to state depending on the health care facilities, immunological response of the individuals & comorbidities and vaccination status of that particular state. The multiclass prediction model is developed based on the status of data available from the different states of India considering their level of population density, intensity economic activities, education level, vaccination status and timing of lockdown or shut down. Based on this prediction model we can develop an application to motivate the internet of health things (IoHT), which can monitor the state and help in governing. This paper uses a multi class prediction model using Deep Neural Network (DNN) and validates the data set up to the year 2022, with accuracy level 98%. In this architecture, we have used 4 hidden layers between input and output layer. We have collected data from JHU CSSE Covid-19 and also follow our own algorithm to create our own dataset. We have taken 80% of data for training purposes and 20% of the dataset as validation purposes.