Harvard Catalyst Profiles

Contact, publication, and social network information about Harvard faculty and fellows.

John Samuel Brownstein, Ph.D.

Co-Author

This page shows the publications co-authored by John Brownstein and Mauricio Santillana.
Connection Strength

4.763
  1. Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance. Sci Rep. 2016 05 11; 6:25732.
    View in: PubMed
    Score: 0.662
  2. Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. PLoS Comput Biol. 2015 Oct; 11(10):e1004513.
    View in: PubMed
    Score: 0.638
  3. Using clinicians' search query data to monitor influenza epidemics. Clin Infect Dis. 2014 Nov 15; 59(10):1446-50.
    View in: PubMed
    Score: 0.586
  4. Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach. PLoS One. 2021; 16(5):e0250890.
    View in: PubMed
    Score: 0.234
  5. Estimation of Pneumonic Plague Transmission in Madagascar, August-November 2017. PLoS Curr. 2018 Nov 01; 10.
    View in: PubMed
    Score: 0.196
  6. 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 Infect Dis. 2018 08 15; 18(1):403.
    View in: PubMed
    Score: 0.194
  7. Antibiotic Resistance Increases with Local Temperature. Nat Clim Chang. 2018 Jun; 8(6):510-514.
    View in: PubMed
    Score: 0.190
  8. Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis. JMIR Public Health Surveill. 2018 Jan 09; 4(1):e4.
    View in: PubMed
    Score: 0.186
  9. Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches. JMIR Public Health Surveill. 2017 Nov 01; 3(4):e83.
    View in: PubMed
    Score: 0.183
  10. County-level assessment of United States kindergarten vaccination rates for measles mumps rubella (MMR) for the 2014-2015 school year. Vaccine. 2017 11 07; 35(47):6444-6450.
    View in: PubMed
    Score: 0.183
  11. Advances in using Internet searches to track dengue. PLoS Comput Biol. 2017 Jul; 13(7):e1005607.
    View in: PubMed
    Score: 0.180
  12. Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System. JMIR Public Health Surveill. 2017 Apr 07; 3(2):e18.
    View in: PubMed
    Score: 0.176
  13. Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. PLoS Negl Trop Dis. 2017 01; 11(1):e0005295.
    View in: PubMed
    Score: 0.173
  14. Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Sci Rep. 2016 Sep 26; 6:33707.
    View in: PubMed
    Score: 0.170
  15. Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak. JMIR Public Health Surveill. 2016 Jun 01; 2(1):e30.
    View in: PubMed
    Score: 0.166
  16. Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. Am J Public Health. 2015 Oct; 105(10):2124-30.
    View in: PubMed
    Score: 0.157
  17. 2014 ebola outbreak: media events track changes in observed reproductive number. PLoS Curr. 2015 Apr 28; 7.
    View in: PubMed
    Score: 0.154
  18. A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. J Med Internet Res. 2014 Oct 20; 16(10):e236.
    View in: PubMed
    Score: 0.149
  19. Evaluation of Internet-based dengue query data: Google Dengue Trends. PLoS Negl Trop Dis. 2014 Feb; 8(2):e2713.
    View in: PubMed
    Score: 0.142
  20. Using electronic health records and Internet search information for accurate influenza forecasting. BMC Infect Dis. 2017 05 08; 17(1):332.
    View in: PubMed
    Score: 0.044
Connection Strength
The connection strength for co-authors is the sum of the scores for each of their shared publications.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.