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Identifying New Factors in COVID - 19 AI Case Predictions

EasyChair Preprint no. 5056

5 pagesDate: February 26, 2021


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

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  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}}
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