Published on in Vol 6, No 2 (2020): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19509, first published .
Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Tim Mackey 1, 2, 3, 4, MAS, PhD;  Vidya Purushothaman 2, 5, MBBS, MAS;  Jiawei Li 1, 2, 3, 4, MS;  Neal Shah 1, 4, BS;  Matthew Nali 1, 3, BA;  Cortni Bardier 6, BA;  Bryan Liang 2, 3, MD, JD, PhD;  Mingxiang Cai 2, 3, 7, MS;  Raphael Cuomo 1, 2, MPH, PhD

1 Department of Anesthesiology and Division of Global Public Health and Infectious Diseases, School of Medicine, University of California San Diego, La Jolla, CA, US

2 Global Health Policy Institute , San Diego, CA, US

3 S-3 Research LLC , San Diego, CA, US

4 Department of Healthcare Research and Policy, University of California San Diego , San Diego, CA, US

5 Department of Family Medicine and Public Health, School of Medicine, University of California San Diego, La Jolla, CA, US

6 Masters Program in Global Health, Department of Anthropology, University of California San Diego, La Jolla, CA, US

7 Masters Program in Computer Science, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, US

Corresponding Author:

  • Tim Mackey, MAS, PhD
  • Department of Anesthesiology and Division of Global Public Health and Infectious Diseases
  • School of Medicine
  • University of California San Diego
  • 8950 Villa La Jolla Drive
  • A124
  • La Jolla, CA
  • US
  • Phone: 1 9514914161
  • Email: tmackey@ucsd.edu