Published on in Vol 6, No 4 (2020): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20588, first published .
Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends

Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends

Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends

Journals

  1. Pilz A, Tizek L, Rüth M, Seiringer P, Biedermann T, Zink A. Interest in Sexually Transmitted Infections: Analysis of Web Search Data Terms in Eleven Large German Cities from 2015 to 2019. International Journal of Environmental Research and Public Health 2021;18(5):2771 View
  2. Wang D, Guerra A, Wittke F, Lang J, Bakker K, Lee A, Finelli L, Chen Y. Real-Time Monitoring of Infectious Disease Outbreaks with a Combination of Google Trends Search Results and the Moving Epidemic Method: A Respiratory Syncytial Virus Case Study. Tropical Medicine and Infectious Disease 2023;8(2):75 View
  3. Okunoye B, Ning S, Jemielniak D. Searching for HIV and AIDS Health Information in South Africa, 2004-2019: Analysis of Google and Wikipedia Search Trends. JMIR Formative Research 2022;6(3):e29819 View
  4. Yuan K, Huang G, Wang L, Wang T, Liu W, Jiang H, Yang A. Predicting Norovirus in the United States Using Google Trends: Infodemiology Study. Journal of Medical Internet Research 2021;23(9):e24554 View
  5. Dias-Karunaratne N, Whop L, Ward J, Vujovich-Dunn C, Amin J, Dakiniewich A, Dyda A. Representation of marginalised populations in digital surveillance for notifiable conditions in Australia: a systematic review. Perspectives in Public Health 2024;144(3):162 View