2014.ISDS.Abstracts.Final.pdf


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ISDS 2014 Conference Abstracts

Real-time Laboratory-Based Influenza Surveillance with 
Xpert Flu
Andrea Dugas*1, Howard Burkom2, Richard Rothman1 and on behalf of the National 
Emergency Department Influenza Consortium1

1Emergency Medicine, Johns Hopkins University, Baltimore, MD, USA; 2Johns Hopkins Applied Physics Lab, Baltimore, MD, USA

Objective
To demonstrate the feasibility and validity of a novel electronic 

surveillance system utilizing a cloud-based interface that consolidates 
laboratory test results and geographical information in real-time.

Introduction
Previous studies have demonstrated the benefit of laboratory 

surveillance and its capability to accurately detect influenza outbreaks 
earlier than syndromic surveillance 1,2. Current laboratory surveillance 
has an approximate 2-week lag due to laboratory test turn–around 
time and data collection 3. In order to provide real-time access to 
aggregated test results, we utilized direct cloud connectivity with a 
rapid PCR-based influenza test, Xpert Flu, to centrally consolidate 
test results along with GIS data. On-site, type-specific results were 
available to physicians and uploaded for public health awareness 
within 100 minutes of patient nasopharyngeal swab.

Methods
We implemented a real-time laboratory-based influenza 

surveillance system at two academic emergency medical departments. 
From November 2013 – April 2014 we systematically enrolled and 
tested patients with an acute respiratory illness using Cepheid Xpert 
Flu, a rapid highly sensitive PCR-based assay that provides significant 
improvement over traditional rapid antigen tests 4. Test results were 
instantaneously uploaded to a cloud-based data aggregation system 
including the test result, time and date, and geolocation.

Results
From November 2013 – April 2014, the cloud-based laboratory 

surveillance system automatically uploaded test results and associated 
data which were immediately available to affiliated clinicians and 
local, state, and national public health end users. A total of 1032 
subjects were tested and 82 cases of influenza were identified as 
displayed in Figure 1. Results from these two EDs differentiated in 
real time the regional epidemic curves and showed the type-A virus 
wave followed by the smaller type-B virus wave.

Conclusions
This novel, real-time laboratory-based surveillance system 

automatically uploaded and aggregated flu test results and the 
associated times and locations. Expansion of this real-time capability 
to sentinel facilities could improve both local and national surveillance 
and response, reducing the need for syndromic influenza surveillance. 
Patient demographic detail will be added to real-time flu results in the 
upcoming season for enriched public health utility.

Figure 1: Influenza test results by Week throughout the 2013-2014 
Influenza season

Keywords
Influenza; Clinical Decision Support; Surveillance

Acknowledgments

This work was supported by the Cooperative Agreement IDSEP130014-
01-00 from The Assistant Secretary for Preparedness and Response. Its 
contents are solely the responsibility of the authors and do not necessarily 
represent the official views of The Assistant Secretary for Preparedness 
and Response (within the U.S. Department of Health and Human 
Services).

References

1. Baumbach J, Mueller M, Smelser C, Albanese B, Sewell CM.
Enhancement of influenza surveillance with aggregate rapid
influenza test results: New Mexico, 2003-2007. Am J Public Health.
2009;99(Suppl 2):S372-377.

2. Ghosh TS, Vogt RL. Active influenza surveillance at the local level: a
model for local health agencies. Am J Public Health. 2008;98(2):13-
15.

3. Centers for Disease Control and Prevention. The National Respiratory
and Enteric Virus Surveillance System (NREVSS). 2011; http://www.
cdc.gov/surveillance/nrevss/. Accessed Nov 25, 2011.

4. DiMaio MA, Sahoo MK, Waggoner J, Pinsky BA. Comparison of
Xpert Flu rapid nucleic acid testing with rapid antigen testing for the
diagnosis of influenza A and B. J Virol Methods. 2012;186(1-2):137-
140.

*Andrea Dugas
E-mail: adugas1@jhmi.edu    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 7(1):e20, 2015