Skip to main content

Syndromic Surveillance of Infectious Diseases

  • Reference work entry
  • First Online:
Infectious Diseases
  • Originally published in
  • R. A. Meyers (ed.), Encyclopedia of Sustainability Science and Technology, © Springer Science+Business Media, LLC, part of Springer Nature 2021

Glossary Terms

Syndromic surveillance: A surveillance approach that uses medical data from different sources to monitor disease trends in real-time and to detect disease outbreaks.

Definition of the Subject

Infectious diseases continue to present major challenges throughout the world. Since the spread of infectious diseases occurs irrespective of geographic boundaries, they place at risk both individuals and populations [1]. Optimal surveillance is paramount for the detection and the combat against infectious diseases. Several types of surveillance can be applied to follow infectious diseases. They include the traditional form of surveillance which rely on the report of notifiable diseases by clinicians and clinical laboratories and syndromic surveillance [2]. Syndromic surveillance is an exploratory method in which disease indicators can be retrieved through automated data acquisition, analysis, and the generation of statistical signals [2]. These signals are monitored in real-time...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Bibliography

  1. Global health and infectious diseases (2003) Informing the future: critical issues in health. Institute of Medicine and National Academies Press, Washington, DC

    Google Scholar 

  2. Buehler JW, Hopkins RS, Overhage JM, Sosin DM, Tong V (2004) Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group. MMWR Recomm Rep: Morb Mortal Wkly Rep Recomm Rep 53(Rr-5):1–11

    Google Scholar 

  3. Paterson BJ, Durrheim DN (2013) The remarkable adaptability of syndromic surveillance to meet public health needs. J Epidemiol Glob Health 3(1):41–47

    Article  Google Scholar 

  4. Triple S Project (2011) Assessment of syndromic surveillance in Europe. Lancet (London) 378(9806):1833–1834

    Article  Google Scholar 

  5. Musa I, Park H, Munkhdalai L, Ryu K (2018) Global research on syndromic surveillance from 1993 to 2017: bibliometric analysis and visualization. Sustainability 10(10):3414

    Article  Google Scholar 

  6. Abat C, Chaudet H, Rolain JM, Colson P, Raoult D (2016) Traditional and syndromic surveillance of infectious diseases and pathogens. Int J Infect Dis IJID: Off Publ Int Soc Infect Dis 48:22–28

    Article  Google Scholar 

  7. Boktor SW, Waller K, Blanton L, Kniss K (2018) Validation of syndromic ILI data for use in CDC’s ILINet surveillance, Pennsylvania. Online J Public Health Inform 10(1):e67

    Google Scholar 

  8. Public Health England (2019) Syndromic surveillance summary: field service, national infection service, real-time syndromic surveillance. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/839865/PHESyndromicSurveillanceSummary2019Week41.pdf

  9. Sugawara T, Ohkusa Y, Kawanohara H, Kamei M (2018) Prescription surveillance for early detection system of emerging and reemerging infectious disease outbreaks. Biosci Trends 12(5):523–525

    Article  Google Scholar 

  10. Andersson T, Bjelkmar P, Hulth A, Lindh J, Stenmark S, Widerstrom M (2014) Syndromic surveillance for local outbreak detection and awareness: evaluating outbreak signals of acute gastroenteritis in telephone triage, web-based queries and over-the-counter pharmacy sales. Epidemiol Infect 142(2):303–313

    Article  CAS  Google Scholar 

  11. Noufaily A, Morbey RA, Colon-Gonzalez FJ, Elliot AJ, Smith GE, Lake IR et al (2019) Comparison of statistical algorithms for daily syndromic surveillance aberration detection. Bioinformatics (Oxford, England) 35(17):3110–3118

    Article  CAS  Google Scholar 

  12. Ziemann A, Fouillet A, Brand H, Krafft T (2016) Success factors of European syndromic surveillance systems: a worked example of applying qualitative comparative analysis. PLoS One 11(5):e0155535

    Article  Google Scholar 

  13. May L, Chretien JP, Pavlin JA (2009) Beyond traditional surveillance: applying syndromic surveillance to developing settings–opportunities and challenges. BMC Public Health 9:242

    Article  Google Scholar 

  14. Smith GE, Elliot AJ, Lake I, Edeghere O, Morbey R, Catchpole M et al (2019) Syndromic surveillance: two decades experience of sustainable systems – its people not just data! Epidemiol Infect 147:e101

    Article  Google Scholar 

  15. Centers for Disease Control and Prevention (2020) COVIDView; Coronavirus Disease 2019 (COVID-19). https://www.cdc.gov/coronavirus/2019-ncov/covid-data/covidview/index.html

  16. Public Health England (2020) Weekly Coronavirus Disease 2019 (COVID-19) surveillance report. https://www.gov.uk/government/publications/national-covid-19-surveillance-reports

  17. Israel Center for Disease Control (2020) Surveillance of COVID-19 un Israel. https://www.health.gov.il/coronaweekly/corona_04072020e.pdf

  18. Paterson BJ, Kool JL, Durrheim DN, Pavlin B (2012) Sustaining surveillance: evaluating syndromic surveillance in the Pacific. Glob Public Health 7(7):682–694

    Article  Google Scholar 

  19. Viboud C, Charu V, Olson D, Ballesteros S, Gog J, Khan F et al (2014) Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US. PLoS One 9(7):e102429

    Article  Google Scholar 

  20. Hripcsak G, Soulakis ND, Li L, Morrison FP, Lai AM, Friedman C et al (2009) Syndromic surveillance using ambulatory electronic health records. J Am Med Inform Assoc: JAMIA 16(3):354–361

    Article  Google Scholar 

  21. New York City Department of Health (2019) Syndromic surveillance data. https://www1.nyc.gov/site/doh/data/data-sets/epi-syndromic-surveillance-data.page

  22. Riviere M, Baroux N, Bousquet V, Ambert-Balay K, Beaudeau P, Jourdan-Da Silva N et al (2017) Secular trends in incidence of acute gastroenteritis in general practice, France, 1991–2015. Euro Surveill: Bulletin Europeen sur les maladies transmissibles = Euro Commun Dis Bull 22(50):17-00121

    Article  Google Scholar 

  23. Public Health England (2019) GP in-hours: weekly bulletins for 2019. Available from: https://www.gov.uk/government/publications/gp-in-hours-weekly-bulletins-for-2019

  24. Israel center for Disease Control (2019) Surveillance of infectious enteric illness. Updated weekly report for week 35 ending 31 August, 2019

    Google Scholar 

  25. Atrubin DHJ, Culpepper A, Mulay PR (2016) Utilizing Florida’s syndromic surveillance system for active case finding to support the Zika Virus response. Counsel of State and Territorial Epidemiologists Annual Conference; 19–23 June, 2016; Anchorage, Alaska, USA

    Google Scholar 

  26. Katelaris AL, Glasgow K, Lawrence K, Corben P, Zheng A, Sumithra S et al (2019) Investigation and response to an outbreak of leptospirosis among raspberry workers in Australia, 2018. Zoonoses Public Health 67:35

    Article  Google Scholar 

  27. Lall R, Abdelnabi J, Ngai S, Parton HB, Saunders K, Sell J et al (2017) Advancing the use of emergency department syndromic surveillance data, New York City, 2012–2016. Public Health Rep (Washington, DC: 1974) 132(1_suppl):23s–30s

    Article  Google Scholar 

  28. Jia K, Mohamed K (2015) Evaluating the use of cell phone messaging for community Ebola syndromic surveillance in high risked settings in Southern Sierra Leone. Afr Health Sci 15(3):797–802

    Article  Google Scholar 

  29. Katz R, May L, Baker J, Test E (2011) Redefining syndromic surveillance. J Epidemiol Glob Health 1(1):21–31

    Article  Google Scholar 

  30. Elliot A (2009) Syndromic surveillance: the next phase of public health monitoring during the H1N1 influenza pandemic? Euro Surveill: Bulletin Europeen sur les maladies transmissibles = Euro Commun Dis Bull 14(44):19391

    Article  Google Scholar 

  31. Todkill D, Hughes HE, Elliot AJ, Morbey RA, Edeghere O, Harcourt S et al (2016) An observational study using English syndromic surveillance data collected during the 2012 London Olympics – what did syndromic surveillance show and what can we learn for future mass-gathering events? Prehosp Disaster Med 31(6):628–634

    Article  Google Scholar 

  32. Kajita E, Luarca MZ, Wu H, Hwang B, Mascola L (2017) Harnessing syndromic surveillance emergency department data to monitor health impacts during the 2015 Special Olympics World Games. Public Health Rep (Washington, DC: 1974) 132(1_suppl):99s–105s

    Article  Google Scholar 

  33. Razavi SM, Sabouri-Kashani A, Ziaee-Ardakani H, Tabatabaei A, Karbakhsh M, Sadeghipour H et al (2013) Trend of diseases among Iranian pilgrims during five consecutive years based on a Syndromic Surveillance System in Hajj. Med J Islam Repub Iran 27(4):179–185

    Google Scholar 

  34. Lami F, Asi W, Khistawi A, Jawad I (2019) Syndromic surveillance of communicable diseases in mobile clinics during the Arbaeenia Mass Gathering in Wassit Governorate, Iraq, in 2014: cross-sectional study. JMIR Public Health Surveill 5(4):e10920

    Article  Google Scholar 

  35. Riccardo F, Napoli C, Bella A, Rizzo C, Rota MC, Dente MG et al (2011) Syndromic surveillance of epidemic-prone diseases in response to an influx of migrants from North Africa to Italy, May to October 2011. Euro Surveill: Bulletin Europeen sur les maladies transmissibles = Euro Commun Dis Bull 16(46):20016

    Google Scholar 

  36. Sarma N, Ullrich A, Wilking H, Ghozzi S, Lindner AK, Weber C et al (2018) Surveillance on speed: being aware of infectious diseases in migrants mass accommodations – an easy and flexible toolkit for field application of syndromic surveillance, Germany, 2016 to 2017. Euro Surveill: Bulletin Europeen sur les maladies transmissibles = Euro Commun Dis Bull 23(40):1700430

    Article  Google Scholar 

  37. Perrotta D, Bella A, Rizzo C, Paolotti D (2017) Participatory online surveillance as a supplementary tool to sentinel doctors for Influenza-like illness surveillance in Italy. PLoS One 12(1):e0169801

    Article  Google Scholar 

  38. Nuti SV, Wayda B, Ranasinghe I, Wang S, Dreyer RP, Chen SI et al (2014) The use of google trends in health care research: a systematic review. PLoS One 9(10):e109583

    Article  Google Scholar 

  39. Google. Google flu trends. Available from: https://www.google.org/flutrends/about/

  40. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature 457(7232):1012–1014

    Article  CAS  Google Scholar 

  41. Valdivia A, Lopez-Alcalde J, Vicente M, Pichiule M, Ruiz M, Ordobas M (2010) Monitoring influenza activity in Europe with Google Flu Trends: comparison with the findings of sentinel physician networks – results for 2009–10. Euro Surveill: Bulletin Europeen sur les maladies transmissibles = Euro Commun Dis Bull 15(29):19621

    Article  Google Scholar 

  42. Malik MT, Gumel A, Thompson LH, Strome T, Mahmud SM (2011) “Google flu trends” and emergency department triage data predicted the 2009 pandemic H1N1 waves in Manitoba. Can J Public Health = Revue canadienne de sante publique 102(4):294–297

    Article  Google Scholar 

  43. Patwardhan A, Bilkovski R (2012) Comparison: flu prescription sales data from a retail pharmacy in the US with Google flu trends and US ILINet (CDC) data as flu activity indicator. PLoS One 7(8):e43611

    Article  CAS  Google Scholar 

  44. Husnayain A, Fuad A, Lazuardi L (2019) Correlation between Google Trends on dengue fever and national surveillance report in Indonesia. Glob Health Action 12(1):1552652

    Article  Google Scholar 

  45. Gluskin RT, Johansson MA, Santillana M, Brownstein JS (2014) Evaluation of internet-based dengue query data: Google dengue trends. PLoS Negl Trop Dis 8(2):e2713

    Article  Google Scholar 

  46. Sulyok M, Richter H, Sulyok Z, Kapitany-Foveny M, Walker MD (2019) Predicting tick-borne encephalitis using Google Trends. Ticks Tick-borne Dis 11:101306

    Article  Google Scholar 

  47. Pollett S, Wood N, Boscardin WJ, Bengtsson H, Schwarcz S, Harriman K et al (2015) Validating the use of Google Trends to enhance pertussis surveillance in California. PLoS Curr 7:ecurrents.outbreaks.7119696b3e7523faa4543faac87c56c2

    Google Scholar 

  48. Verma M, Kishore K, Kumar M, Sondh AR, Aggarwal G, Kathirvel S (2018) Google search trends predicting disease outbreaks: an analysis from India. Healthc Inform Res 24(4):300–308

    Article  Google Scholar 

  49. Gesualdo F, Stilo G, Agricola E, Gonfiantini MV, Pandolfi E, Velardi P et al (2013) Influenza-like illness surveillance on Twitter through automated learning of naive language. PLoS One 8(12):e82489

    Article  Google Scholar 

  50. Marques-Toledo CA, Degener CM, Vinhal L, Coelho G, Meira W, Codeco CT et al (2017) Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level. PLoS Negl Trop Dis 11(7):e0005729

    Article  Google Scholar 

  51. Masri S, Jia J, Li C, Zhou G, Lee MC, Yan G et al (2019) Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic. BMC Public Health 19(1):761

    Article  Google Scholar 

  52. Odlum M, Yoon S (2015) What can we learn about the Ebola outbreak from tweets? Am J Infect Control 43(6):563–571

    Article  Google Scholar 

  53. Kalimeri K, Delfino M, Cattuto C, Perrotta D, Colizza V, Guerrisi C et al (2019) Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms. PLoS Comput Biol 15(4):e1006173

    Article  CAS  Google Scholar 

  54. van Noort SP, Codeco CT, Koppeschaar CE, van Ranst M, Paolotti D, Gomes MG (2015) Ten-year performance of Influenzanet: ILI time series, risks, vaccine effects, and care-seeking behaviour. Epidemics 13:28–36

    Article  Google Scholar 

  55. Yeng PK, Woldaregay AZ, Solvoll T, Hartvigsen G (2020) Cluster detection mechanisms for syndromic surveillance systems: systematic review and framework development. JMIR Public Health Surveill 6:e11512

    Article  Google Scholar 

  56. Gupta A, Katarya R (2020) Social media based surveillance systems for healthcare using machine learning: a systematic review. J Biomed Inform 108:103500

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aharona Glatman-Freedman .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Glatman-Freedman, A., Kaufman, Z. (2023). Syndromic Surveillance of Infectious Diseases. In: Shulman, L.M. (eds) Infectious Diseases. Encyclopedia of Sustainability Science and Technology Series. Springer, New York, NY. https://doi.org/10.1007/978-1-0716-2463-0_1088

Download citation

Publish with us

Policies and ethics