Factors Influencing Performance of Internet-Based Biosurveillance Systems Used in Epidemic Intelligence for Early Detection of Infectious Diseases Outbreaks
Background Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives. Method and Findings Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems’ performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p = 0001) whereas other indicators were similar (C-DR: p = 020; C-Se, p = 013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables. Conclusion Although differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p = 00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance.
References
[1]
Heymann DL, Rodier GR (2001) Hot spots in a wired world: WHO surveillance of emerging and re-emerging infectious diseases. Lancet Infect Dis. 1(5): 345–353. doi: 10.1016/s1473-3099(01)00148-7
[2]
Formenty P, Roth C, Gonzalez-Martin F, Grein T, Ryan M, et al. (2006) Emergent pathogens, international surveillance and international health regulations (2005). Med Mal Infect 36: 9–15.
[3]
Morse SS (2012) Public health surveillance and infectious disease detection. . Biosecur Bioterror. 10(1): 6–16.
[4]
Paquet C, Coulombier D, Kaiser R, Ciotti M (2012) Epidemic intelligence a new framework for strengthening disease surveillance in Europe. Euro Surveill. 12: 212–214.
[5]
Hartley DM, Nelson NP, Walters R, Arthur RR, Yangarber R, et al. (2010) The Landscape of International Event-based Biosurveillance. . Emerg Health Threats J. 3: e3 doi:10.3134/ehtj.10.003.
[6]
Hartley DM, Nelson NP, Arthur RR, Barboza P, Collier N, et al. (2013) An overview of Internet biosurveillance. Clin Microbiol Infect 19(11): 1006–1013 doi:10.1111/1469-0691.1227.
[7]
Barboza P, Vaillant L, Mawudeku A, Nelson NP, Hartley DM, et al. (2013) Evaluation of Epidemic Intelligence Systems Integrated in the Early Alerting and Reporting Project for the Detection of A/H5N1 Influenza Events. PLoS One 8: 1–9 doi:10.1371/journal.pone.0057252.
[8]
Bitar D, Tarantola A, Capek I, Barboza P, Che D (2009) Risk of importation of tropical diseases in metropolitan France: detection, alert, response. . Bull Acad Natl Med. 193(8): 1847–59 discussion 1859–60.
[9]
Josseran L, Fouillet A, Caillère N, Brun-Ney D, Ilef D, et al. (2010) Assessment of a syndromic surveillance system based on morbidity data: results from the Oscour network during a heat wave. PLoS One 5: e11984. doi: 10.1371/journal.pone.0011984
[10]
Rotureau B, Barboza P, Tarantola A, Paquet C (2007) International epidemic intelligence at the Institut de Veille Sanitaire, France. Emerg Infect Dis13: 1590–2. doi: 10.3201/eid1310.070522
[11]
Institut de Veille Sanitaire website. Epidemic intelligence at the Institut de veille Sanitaire (InVS). http://www.invs.sante.fr/Dossiers-themat？iques/Veille-et-alerte/Veille-sanitaire-？internationale Accessed 15/02/2013.
Zhang Z, Chen D, Chen Y, Liu W, Wang L, et al. (2010) Spatio-temporal data comparisons for global highly pathogenic avian influenza (HPAI) H5N1 outbreaks. PLoS One 5: e15314 Doi:10.1371/journal.pone.0015314.
[14]
Global Health Security Initiative (GHSI) website. http://www.ghsi.ca/english/index.asp; accessed 15/02/2013.
[15]
Zou G (2004) A Modified Poisson Regression Approach to Prospective Studies with Binary Data. American Journal of Epidemiology. 159(7: ), 702–706. doi:10.1093/aje/kwh090.
[16]
Lyon A, Nunn M, Grossel G, Burgman M (2012) Comparison of Web-Based Biosecurity Intelligence Systems: BioCaster, EpiSPIDER and HealthMap. . Transbound Emerg Dis. 59(3): 223–32. doi: 10.1111/j.1865-1682.2011.01258.x
[17]
Cowen P, Garland T, Hugh-Jones ME, Shimshony A, Handysides S, et al. (2006) Evaluation of ProMED-mail as an electronic early warning system for emerging animal diseases: 1996 to 2004. J Am Vet Med Assoc 229: 1090–1099. doi: 10.2460/javma.229.7.1090
[18]
Woodall JP (2001) Global surveillance of emerging diseases: the ProMED-mail perspective. . Cad Saude Publica. 17 Suppl: 147–154 doi: 10.1590/s0102-311x2001000700024
[19]
Barboza P, Quatresous I (2007) Globalization, emergence and importation of infectious diseases. . Rev Prat. 30 57(8): 867–3.
[20]
Ledrans M, Quatresous I, Renault P, Pierre V (2007) Outbreak of chikungunya in the French Territories, 2006: lessons learned. . Euro Surveill. 12(9): E070906.3.
[21]
EpiSouth Website (2010) West Nile virus circulation in the EpiSouth countries and neighbouring areas (Hungary and Russia), Update 6th of October 2010. http://www.episouth.org/download.php?&id？=12182; accessed 18/02/2013;
[22]
Nelson NP, Brownstein JS, Hartley DM (2010) Event-based biosurveillance of respiratory disease in Mexico, 2007-2009: connection to the 2009 influenza A(H1N1) pandemic? Euro Surveill. 15(30): 2007–9.
[23]
Collier N, Doan S, Kawazoe A, Goodwin RM, Conway M, et al. (2008) BioCaster: detecting public health rumors with a Web-based text mining system. Bioinformatics 24: 2940–2941. doi: 10.1093/bioinformatics/btn534
[24]
Mawudeku A, Blench M. Global Public Health Intelligence Network (GPHIN) (2008) Proceedings of the 7th Conference of the Association for Machine Translation in the Americas 2006. Available from http://www.mt-archive.info/MTS-2005-Mawu？deku.pdf
[25]
Brownstein JS, Freifeld CC (2007) HealthMap: the development of automated real-time internet surveillance for epidemic intelligence. Euro Surveill 12: E071129.
[26]
Linge JP, Steinberger R, Weber TP, Yangarber R, van der Gott E, et al.. (2009) Internet surveillance systems for early alerting of health threats. Euro Surveill 14 . Available: http://www.eurosurveillance.org/ViewArti？cle.aspx?ArticleId=19162.
[27]
Madoff LC (2004) ProMED-mail: an early warning system for emerging diseases. . Clin Infect Dis. 39: 227–232. doi: 10.1086/422003
