title: Novel Use of Flu Surveillance Data: Evaluating Potential of Sentinel Populations for Early Detection of Influenza Outbreaks.
abstract: Influenza causes significant morbidity and mortality each year, with 2-8% of weekly outpatient visits around the United States for influenza-like-illness (ILI) during the peak of the season. Effective use of existing flu surveillance data allows officials to understand and predict current flu outbreaks and can contribute to reductions in influenza morbidity and mortality. Previous work used the 2009-2010 influenza season to investigate the possibility of using existing military and civilian surveillance systems to improve early detection of flu outbreaks. Results suggested that civilian surveillance could help predict outbreak trajectory in local military installations. To further test that hypothesis, we compare pairs of civilian and military outbreaks in seven locations between 2000 and 2013. We find no predictive relationship between outbreak peaks or time series of paired outbreaks. This larger study does not find evidence to support the hypothesis that civilian data can be used as sentinel surveillance for military installations. We additionally investigate the effect of modifying the ILI case definition between the standard Department of Defense definition, a more specific definition proposed in literature, and confirmed Influenza A. We find that case definition heavily impacts results. This study thus highlights the importance of careful selection of case definition, and appropriate consideration of case definition in the interpretation of results.
authors.affiliation: Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America.
authors.forename: Ashlynn R
authors.identifier: http://orcid.org/0000-0003-1694-3268
authors.initials: AR
authors.lastname: Daughton
authors.affiliation: Bioscience Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America.
authors.forename: Nileena
authors.initials: N
authors.lastname: Velappan
authors.affiliation: Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America.
authors.forename: Esteban
authors.initials: E
authors.lastname: Abeyta
authors.affiliation: High Performance Computing Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America.
authors.forename: Reid
authors.initials: R
authors.lastname: Priedhorsky
authors.affiliation: Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States of America.
authors.forename: Alina
authors.initials: A
authors.lastname: Deshpande
mesh_terms: D016208:Databases, Factual; D004196:Disease Outbreaks; D005260:Female; D006801:Humans; D007251:Influenza, Human / Q000401:mortality*; D008297:Male; D008954:Models, Biological; D014481:United States / Q000453:epidemiology