Event-based internet biosurveillance: Relation to epidemiological observation

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Abstract

Background: The World Health Organization (WHO) collects and publishes surveillance data and statistics for select diseases, but traditional methods of gathering such data are time and labor intensive. Event-based biosurveillance, which utilizes a variety of Internet sources, complements traditional surveillance. In this study we assess the reliability of Internet biosurveillance and evaluate disease-specific alert criteria against epidemiological data. Methods. We reviewed and compared WHO epidemiological data and Argus biosurveillance system data for pandemic (H1N1) 2009 (April 2009 - January 2010) from 8 regions and 122 countries to: identify reliable alert criteria among 15 Argus-defined categories; determine the degree of data correlation for disease progression; and assess timeliness of Internet information. Results: Argus generated a total of 1,580 unique alerts; 5 alert categories generated statistically significant (p<0.05) correlations with WHO case count data; the sum of these 5 categories was highly correlated with WHO case data (r=0.81, p<0.0001), with expected differences observed among the 8 regions. Argus reported first confirmed cases on the same day as WHO for 21 of the first 64 countries reporting cases, and 1 to 16days (average 1.5days) ahead of WHO for 42 of those countries. Conclusion: Confirmed pandemic (H1N1) 2009 cases collected by Argus and WHO methods returned consistent results and confirmed the reliability and timeliness of Internet information. Disease-specific alert criteria provide situational awareness and may serve as proxy indicators to event progression and escalation in lieu of traditional surveillance data; alerts may identify early-warning indicators to another pandemic, preparing the public health community for disease events. © 2012 Nelson et al.; licensee BioMed Central Ltd.

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Nelson, N. P., Yang, L., Reilly, A. R., Hardin, J. E., & Hartley, D. M. (2012). Event-based internet biosurveillance: Relation to epidemiological observation. Emerging Themes in Epidemiology. https://doi.org/10.1186/1742-7622-9-4

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