Published on 07.04.17 in Vol 3, No 2 (2017): Apr-Jun
Works citing "Determinants of Participants’ Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System"
According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.7304):
(note that this is only a small subset of citations)
- Fraundorf P. Task-Layer Multiplicity as a Measure of Community Level Health. Complexity 2019;2019:1CrossRef
- Kalimeri K, Delfino M, Cattuto C, Perrotta D, Colizza V, Guerrisi C, Turbelin C, Duggan J, Edmunds J, Obi C, Pebody R, Franco AO, Moreno Y, Meloni S, Koppeschaar C, Kjelsø C, Mexia R, Paolotti D, Scarpino S. Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms. PLOS Computational Biology 2019;15(4):e1006173CrossRef
- Baltrusaitis K, Vespignani A, Rosenfeld R, Gray J, Raymond D, Santillana M. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation. JMIR Public Health and Surveillance 2019;5(4):e13403CrossRef
- Moss R, Zarebski A, Carlson S, McCaw J. Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts. Tropical Medicine and Infectious Disease 2019;4(1):12CrossRef
- Leal Neto O, Cruz O, Albuquerque J, Sousa M, Smolinski M, Libel M, Cesse E, Vieira Souza W. Participatory surveillance based on crowdsourcing during Olympic games Rio 2016: the case of Guardians of Health (Preprint). JMIR Public Health and Surveillance 2019;CrossRef
- Lu FS, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, Hawkins J, Brownstein J, Conidi G, Gunn J, Gray J, Zink A, Santillana M. Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis. JMIR Public Health and Surveillance 2018;4(1):e4CrossRef
- Baltrusaitis K, Brownstein JS, Scarpino SV, Bakota E, Crawley AW, Conidi G, Gunn J, Gray J, Zink A, Santillana M. Comparison of crowd-sourced, electronic health records based, and traditional health-care based influenza-tracking systems at multiple spatial resolutions in the United States of America. BMC Infectious Diseases 2018;18(1)CrossRef
- Chan T, Hu T, Hwang J. Estimating the Risk of Influenza-Like Illness Transmission Through Social Contacts: Web-Based Participatory Cohort Study. JMIR Public Health and Surveillance 2018;4(2):e40CrossRef
- Smolinski MS, Crawley AW, Olsen JM, Jayaraman T, Libel M. Participatory Disease Surveillance: Engaging Communities Directly in Reporting, Monitoring, and Responding to Health Threats. JMIR Public Health and Surveillance 2017;3(4):e62CrossRef
- Paul MJ, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1CrossRef
- Brownstein JS, Chu S, Marathe A, Marathe MV, Nguyen AT, Paolotti D, Perra N, Perrotta D, Santillana M, Swarup S, Tizzoni M, Vespignani A, Vullikanti AKS, Wilson ML, Zhang Q. Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches. JMIR Public Health and Surveillance 2017;3(4):e83CrossRef
- Welhausen CA. At your own risk. Communication Design Quarterly Review 2017;5(2):51CrossRef
- Prieto JT, Jara JH, Alvis JP, Furlan LR, Murray CT, Garcia J, Benghozi P, Kaydos-Daniels SC. Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala. JMIR Public Health and Surveillance 2017;3(4):e87CrossRef