Predicting COVID-19 Case Status from Self-Reported Symptoms and Behaviors Using Data from a Massive Online Survey
Abstract
With the varying availability of RT-PCR testing for COVID-19 across time and location, there is a need for alternative methods of predicting COVID-19 case status. In this study, multiple machine learning (ML) models were trained and assessed for their ability to accurately predict the COVID-19 case status using US COVID-19 Trends and Impact Survey (CTIS) data. The CTIS includes information on testing, symptoms, demographics, behaviors, and vaccination status. The best performing model was XGBoost, which achieved an F1 score of ~94% in predicting whether an individual was COVID-19 positive or negative. This is a notable improvement on existing models for predicting COVID-19 case status and demonstrates the potential for ML methods to provide policy-relevant estimates.
Cite
Text
Srivastava et al. "Predicting COVID-19 Case Status from Self-Reported Symptoms and Behaviors Using Data from a Massive Online Survey." ICLR 2023 Workshops: MLGH, 2023.Markdown
[Srivastava et al. "Predicting COVID-19 Case Status from Self-Reported Symptoms and Behaviors Using Data from a Massive Online Survey." ICLR 2023 Workshops: MLGH, 2023.](https://mlanthology.org/iclrw/2023/srivastava2023iclrw-predicting/)BibTeX
@inproceedings{srivastava2023iclrw-predicting,
title = {{Predicting COVID-19 Case Status from Self-Reported Symptoms and Behaviors Using Data from a Massive Online Survey}},
author = {Srivastava, Mashrin and Reinhart, Alex and Mejia, Robin},
booktitle = {ICLR 2023 Workshops: MLGH},
year = {2023},
url = {https://mlanthology.org/iclrw/2023/srivastava2023iclrw-predicting/}
}