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High-throughput Detection Method for Influenza Virus

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Published on: February 4, 2012

Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons

Mark S Smolinski¹, Adam W Crawley¹, Kristin Baltrusaitis¹

¹Mark S. Smolinski, Adam W. Crawley, and Jennifer M. Olsen are with the Skoll Global Threats Fund, San Francisco, CA. At the time of study, Rumi Chunara was with and Kristin Baltrusaitis, Oktawia Wójcik, Mauricio Santillana and John S. Brownstein are currently with the Boston Children's Hospital Informatics Program, Boston, MA. Andre Nguyen is with the Harvard School of Engineering and Applied Sciences, Harvard University, Cambridge, MA.

American Journal of Public Health

|August 14, 2015

Abstract

Objectives:

We summarized Flu Near You (FNY) data from the 2012-2013 and 2013-2014 influenza seasons in the United States.

Methods:

FNY collects limited demographic characteristic information upon registration, and prompts users each Monday to report symptoms of influenza-like illness (ILI) experienced during the previous week. We calculated the descriptive statistics and rates of ILI for the 2012-2013 and 2013-2014 seasons. We compared raw and noise-filtered ILI rates with ILI rates from the Centers for Disease Control and Prevention ILINet surveillance system.

Results:

More than 61 000 participants submitted at least 1 report during the 2012-2013 season, totaling 327 773 reports. Nearly 40 000 participants submitted at least 1 report during the 2013-2014 season, totaling 336 933 reports. Rates of ILI as reported by FNY tracked closely with ILINet in both timing and magnitude.

Conclusions:

With increased participation, FNY has the potential to serve as a viable complement to existing outpatient, hospital-based, and laboratory surveillance systems. Although many established systems have the benefits of specificity and credibility, participatory systems offer advantages in the areas of speed, sensitivity, and scalability.

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