Current Infectious Disease Reports

, Volume 15, Issue 4, pp 316–319 | Cite as

Why We Need Crowdsourced Data in Infectious Disease Surveillance

  • Rumi Chunara
  • Mark S. Smolinski
  • John S. Brownstein
Hot Topic

Abstract

In infectious disease surveillance, public health data such as environmental, hospital, or census data have been extensively explored to create robust models of disease dynamics. However, this information is also subject to its own biases, including latency, high cost, contributor biases, and imprecise resolution. Simultaneously, new technologies including Internet and mobile phone based tools, now enable information to be garnered directly from individuals at the point of care. Here, we consider how these crowdsourced data offer the opportunity to fill gaps in and augment current epidemiological models. Challenges and methods for overcoming limitations of the data are also reviewed. As more new information sources become mature, incorporating these novel data into epidemiological frameworks will enable us to learn more about infectious disease dynamics.

Keywords

Crowdsourcing Surveillance Technology Bias 

Notes

Acknowledgements

Research reported in this publication was supported by grants from the National Library of Medicine of the National Institutes of Health under Award Numbers G08 LM009776, and R01 LM010812 and Google.org.

Compliance with Ethics Guidelines

Conflict of Interest

Rumi Chunara, Mark S. Smolinski, and John S. Brownstein declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

  1. 1.
    Morse SS, Mazet JA, Woolhouse M, Parrish CR, Carroll D, Karesh WB, et al. Prediction and prevention of the next pandemic zoonosis. Lancet. 2012;380(9857):1956–65.PubMedCrossRefGoogle Scholar
  2. 2.
    Bogich TL, Chunara R, Scales D, Chan E, Pinheiro LC, Chmura AA, et al. Preventing pandemics via international development: a systems approach. PLoS Med. 2012;9(12):e1001354.PubMedCrossRefGoogle Scholar
  3. 3.
    Hay SI, Tatem AJ, Graham AJ, Goetz SJ, Rogers DJ. Global environmental data for mapping infectious disease distribution. Adv Parasitol. 2006;62:37–77.PubMedCrossRefGoogle Scholar
  4. 4.
    Reis BY, Mandl KD. Time series modeling for syndromic surveillance. BMC Med Inform Decis Mak. 2003;3.Google Scholar
  5. 5.
    Tatem AJ, Riley S. Effect of poor census data on population maps. Science. 2007;318(5847):43. author reply.PubMedCrossRefGoogle Scholar
  6. 6.
    Tuite AR, Tien J, Eisenberg M, Earn DJ, Ma J, Fisman DN. Cholera epidemic in Haiti, 2010: using a transmission model to explain spatial spread of disease and identify optimal control interventions. Ann Intern Med. 2011;154(9):593–601.PubMedCrossRefGoogle Scholar
  7. 7.
    Basu S, Andrews J, Kishore S, Panjabi R, Stuckler D. Comparative performance of private and public healthcare systems in low- and middle-Income countries: a systematic review. PLoS Med. 2012;9(6):e1001244.PubMedCrossRefGoogle Scholar
  8. 8.
    Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012–4.PubMedCrossRefGoogle Scholar
  9. 9.
    Chunara R, Andrews J, Brownstein J. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian Cholera outbreak American. J Trop Med Hyg. 2011;86:39–45.CrossRefGoogle Scholar
  10. 10.
    Chunara R, Chhaya V, Bane S, Mekaru S, Chan E, Freifeld C, et al. Online reporting for malaria surveillance using micro-monetary incentives, in urban India 2010–2011. Mala J. 2012;11(43).Google Scholar
  11. 11.
    Lakhani KR, Boudreau KJ, Loh P-R, Backstrom L, Baldwin C, Lonstein E, et al. Prize-based contests can provide solutions to computational biology problems. Nat Biotechnol. 2013;31(2):108–11.PubMedCrossRefGoogle Scholar
  12. 12.
    Anderson DP, Cobb J, Korpela E, Lebofsky M, Werthimer D. SETI@ home: an experiment in public-resource computing. Commun ACM. 2002;45(11):56–61.CrossRefGoogle Scholar
  13. 13.
    Meymaris K, Henderson S, Alaback P, Havens K, editors. Project BudBurst: Citizen Science for All Seasons. AGU Fall Meeting Abstracts; 2008.Google Scholar
  14. 14.
    Bengtsson L, Lu X, Thorson A, Garfield R, von Schreeb J. Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS Med. 2011;8(8):e1001083.PubMedCrossRefGoogle Scholar
  15. 15.
    Chunara R, Freifeld CC, Brownstein JS. New technologies for reporting real-time emergent infections. Parasitology. 2012;1(1):1–9.Google Scholar
  16. 16.
    The Centers for Disease Control and Prevention. FluView. Available from: gis.cdc.gov/grasp/fluview/fluportaldashboard.html. Accessed March 13, 2012.
  17. 17.
    Copeland KR, Allen AE, editors. Basic Models for Mapping Prescription Drug Data. Proceedings of the Survey Research Methods Section, American Statistical Association; 2005.Google Scholar
  18. 18.
    The World Health Organization. Global Outbreak Alert & Response Network. Available from: http://www.who.int/csr/outbreaknetwork/en/%5D. Accessed March 6, 2013.
  19. 19.
    Cauchemez S, Epperson S, Biggerstaff M, Swerdlow D, Finelli L, Ferguson NM. Using routine surveillance data to estimate the epidemic potential of emerging zoonoses: application to the emergence of US Swine Origin Influenza A H3N2v Virus. PLoS Med. 2013;10(3):e1001399.PubMedCrossRefGoogle Scholar
  20. 20.
    Chan EH, Brewer TF, Madoff LC, Pollack MP, Sonricker AL, Keller M, et al. Global capacity for emerging infectious disease detection. Proc Natl Acad Sci USA. 2010;107(50):21701–6. Epub 2010 Nov 29.PubMedCrossRefGoogle Scholar
  21. 21.
    The World Health Organization. Global Alert and Response: Epidemic intelligence - systematic event detection. Available from: http://www.who.int/csr/alertresponse/epidemicintelligence/en/index.html. Accessed March 6, 2013.
  22. 22.
    Freifeld CC, Mandl KD, Reis BY, Brownstein JS. HealthMap: global infectious disease monitoring through automated classification and visualization of Internet media reports. J Am Med Inform Assoc. 2008;15(2):150–7.PubMedCrossRefGoogle Scholar
  23. 23.
    Wesolowski A, Eagle N, Tatem AJ, Smith DL, Noor AM, Snow RW, et al. Quantifying the impact of human mobility on malaria. Science. 2012;338(6104):267–70.PubMedCrossRefGoogle Scholar
  24. 24.
    Tilston NL, Eames KT, Paolotti D, Ealden T, Edmunds WJ. Internet-based surveillance of Influenza-like-illness in the UK during the 2009 H1N1 influenza pandemic. BMC Public Health. 2010;10(1):650.PubMedCrossRefGoogle Scholar
  25. 25.
    Hufnagel L, Brockmann D, Geisel T. Forecast and control of epidemics in a globalized world. Proc Natl Acad Sci USA. 2004;101(42):15124–9.PubMedCrossRefGoogle Scholar
  26. 26.
    Wolfe ND, Heneine W, Carr JK, Garcia AD, Shanmugam V, Tamoufe U, et al. Emergence of unique primate T-lymphotropic viruses among central African bushmeat hunters. Proc Natl Acad Sci. 2005;102(22):7994–9.PubMedCrossRefGoogle Scholar
  27. 27.
    Read JM, Edmunds WJ, Riley S, Lessler J, Cummings DA. Close encounters of the infectious kind: methods to measure social mixing behaviour. Epidemiol Infect. 2012;140(12):2117–30. doi:10.1017/S0950268812000842. Epub 2012 Jun 12.PubMedCrossRefGoogle Scholar
  28. 28.
    Chunara R, Bouton L, Ayers JW, Brownstein JS. Assessing the online social environment for surveillance of obesity prevalence. PloS One. 2013;8(4):e61373.Google Scholar
  29. 29.
    Dugas AF, Hsieh Y-H, Levin SR, Pines JM, Mareiniss DP, Mohareb A, et al. Google Flu Trends: correlation with emergency department influenza rates and crowding metrics. Clin Infect Dis. 2012;54(4):463–9.PubMedCrossRefGoogle Scholar
  30. 30.
    Chan EH, Sahai V, Conrad C, Brownstein JS. Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance. PLoS Negl Trop Dis. 2011;5(5):e1206.PubMedCrossRefGoogle Scholar
  31. 31.
    Chunara R, Aman S, Smolinski M, Brownstein JS. Flu near you: an online self-reported influenza surveillance system in the USA. Online J Public Health Inform. 2013;5(1).Google Scholar
  32. 32.
    Wesolowski A, Eagle N, Noor AM, Snow RW, Buckee CO. Heterogeneous mobile phone ownership and usage patterns in Kenya. PLoS One. 2012;7(4):e35319.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Rumi Chunara
    • 1
    • 2
    • 4
  • Mark S. Smolinski
    • 3
  • John S. Brownstein
    • 1
    • 2
  1. 1.Department of PediatricsHarvard Medical SchoolBostonUSA
  2. 2.Children’s Hospital Informatics Program, Division of Emergency MedicineBoston Children’s HospitalBostonUSA
  3. 3.Skoll Global Threats FoundationSan FranciscoUSA
  4. 4.BostonUSA

Personalised recommendations