The Role of Informal Digital Surveillance Systems Before, During and After Infectious Disease Outbreaks: A Critical Analysis
Background One of the main limitations of traditional surveillance systems for disease detection is their inability to detect epidemics in real-time. In addition to syndromic surveillance, a number of informal digital resources have been developed. These systems are based on data collected through media sources such as news reports on the Internet, mailing lists, and RSS (Really Simple Syndication) feeds. The role of such systems at all stages of the epidemic remains unclear.
Methods A literature review was carried out on informal digital resources for infectious disease surveillance. We examined the source of information, the manner in which they process and disseminate the information, their role in each phase of disease outbreaks, and whether and to what extent these systems are capable of early detection and management of infectious disease epidemics.
Results Informal digital resources use similar sources of data for surveillance. However, they use different algorithms to create their output, and cover different geographic areas. In this regard, they complement each other with respect to information completeness. There is evidence in the literature on the systems’ usefulness in communicating information to public health professionals, as well as to the general public during and after previous epidemics. Retrospective studies of some systems have shown a theoretical decrease in the time of epidemic detection compared to conventional surveillance. However, there is no evidence of the ability for real-time detection.
Conclusions Currently, there is little prospective evidence that existing informal systems are capable of real-time early detection of disease outbreaks. Most systems accumulate large amounts of information on a wide variety of diseases, making it difficult to extract critical information. Presenting critical information clearly and precisely remains a challenge.
KeywordsInfectious disease Outbreak Digital systems Formal Informal
- 2.Avian Influenza Daily Digest website: http://aidailydigest.blogspot.co.il/2008/10/dni-avian-influenza-daily-digest_09.html
- 3.Barboza P, Vaillant L, Le Strat Y, Hartley DM, Nelson NP, Mawudeku A, Madoff LC, Linge JP, Collier N, Brownstein JS, Astagneau P (2014) Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks. PLoS One 9(3):e90536CrossRefPubMedPubMedCentralGoogle Scholar
- 7.Carneiro HA, Mylonakis E (2013) Google trends: a web-based tool for real-time surveillance of disease outbreaks. Surfing Web 2009(49):1557Google Scholar
- 13.Herman Tolentino M, Raoul Kamadjeu MD, Michael Matters PhD MPH, Marjorie Pollack MD, Larry Madoff MD (2007) Scanning the emerging infectious diseases horizon-visualizing ProMED emails using EpiSPIDER. Adv Dis Surveil 2:169Google Scholar
- 18.Mykhalovskiy E, Weir L (2006) The global public health intelligence network and early warning outbreak detection: a Canadian contribution to global public health. Can J Public Health:42–44Google Scholar
- 19.Nature website: www.nature.com/avianflu/google-earth
- 20.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? EuroSurveillance 15(30)Google Scholar
- 21.ProMED-mail website: http://www.promedmail.org/
- 22.Scarpino SV, Dimitrov NB, Meyers LA (2012) Optimizing provider recruitment for influenza surveillance networks. PLoS 8(4)Google Scholar
- 23.Wagner MM, Moore AW, Aryel RM (2006) Book: handbook of biosurveillance 301Google Scholar
- 25.World Health Organization (2008) International health regulations (2005), 2nd edn. World Health Organization, GenevaGoogle Scholar