Published on in Vol 6, No 12 (2022): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40825, first published .
State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data

State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data

State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data

Authors of this article:

Alex Turvy 1 Author Orcid Image

Alex Turvy   1 , BA, MS

1 City, Culture, and Community, Department of Sociology, Tulane University, New Orleans, LA, United States

Corresponding Author:

  • Alex Turvy, BA, MS
  • City, Culture, and Community
  • Department of Sociology
  • Tulane University
  • 6823 St Charles Ave
  • New Orleans, LA, 70118
  • United States
  • Phone: 1 504 865-5231
  • Email: aturvy@tulane.edu