Identifying Factors in COVID - 19 AI Case Predictions
Lynn Pickering, Javier Viana, Xin Li, Anirudh Chhabra, Dhruv Patel, Kelly Cohen
7th International Conference on Soft Computing & Machine Intelligence (ISCMI 2020)
Many machine learning methods are being developed to predict the spread of COVID – 19. This paper focuses on the expansion of inputs that may be considered in these models. A correlation matrix is used to identify those variables with the highest correlation to COVID – 19 cases. These variables are then used and compared in three methods that predict future cases: a Support Vector Machine Regression (SVR), Multidimensional Regression with Interactions, and the Stepwise Regression method. All three methods predict a rise in cases similar to the actual rise in cases, and importantly, are all able to predict to a certain degree the unexpected dip in cases on the 10th and 11th day of prediction.