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)

Abstract

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.

L. Pickering, J. Viana, X. Li, A. Chhabra, D. Patel and K. Cohen, "Identifying Factors in COVID - 19 AI Case Predictions," 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), Stockholm, Sweden, 2020, pp. 192-196