Identifying New Factors in COVID - 19 AI Case Predictions

EasyChair Preprint no. 5056

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.

Keyphrases: Artificial Intelligence, correlation, COVID-19, infectious diseases, Support Vector Machine

@Booklet{EasyChair:5056,
  author = {Lynn Pickering and Javier Viaña and Xin Li and Anirudh Chhabra and Dhruv Patel and Kelly Cohen},
  title = {Identifying New Factors in COVID - 19 AI Case Predictions},
  howpublished = {EasyChair Preprint no. 5056},

  year = {EasyChair, 2021}}