Prediction of Epidemic Disease Dynamics on the Infection Risk Using Machine Learning Algorithms

Abstract

Accurate forecast for the public is more important to many organisations especially health organisations on infectious disease dynamics that prevails in prevention or decrease in disease transmission. With multiple data availability in healthcare and medical sectors, precise analysis of such data helps in disease detection and better health care of all individuals. With the existing computational power and big data, there are more chances in predicting an epidemic outbreak. The basic idea of this paper is to analyse and predict the spread of epidemic diseases mainly on the focus on infection risk. A machine learning model using Multivariate Logistic Regression on Modified SEIR has to be built to predict the epidemic disease dynamics on the infection risk.