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Citing this Article

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Published on 28.05.20 in Vol 22, No 5 (2020): May

This paper is in the following e-collection/theme issue:

Works citing "Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study"

According to Crossref, the following articles are citing this article (DOI 10.2196/19421):

(note that this is only a small subset of citations)

  1. Picone M, Inoue S, DeFelice C, Naujokas MF, Sinrod J, Cruz VA, Stapleton J, Sinrod E, Diebel SE, Wassman ER. Social Listening as a Rapid Approach to Collecting and Analyzing COVID-19 Symptoms and Disease Natural Histories Reported by Large Numbers of Individuals. Population Health Management 2020;
    CrossRef
  2. De Santis E, Martino A, Rizzi A. An Infoveillance System for Detecting and Tracking Relevant Topics From Italian Tweets During the COVID-19 Event. IEEE Access 2020;8:132527
    CrossRef
  3. Adly AS, Adly AS, Adly MS. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. Journal of Medical Internet Research 2020;22(8):e19104
    CrossRef
  4. Nan S, Tang T, Feng H, Wang Y, Li M, Lu X, Duan H. A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination. JMIR Medical Informatics 2020;8(10):e21628
    CrossRef
  5. Eltoukhy AEE, Shaban IA, Chan FTS, Abdel-Aal MAM. Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations. International Journal of Environmental Research and Public Health 2020;17(19):7080
    CrossRef