IDEAS home Printed from https://ideas.repec.org/a/sae/envira/v40y2008i7p1615-1632.html
   My bibliography  Save this article

Signals Come and Go: Syndromic Surveillance and Styles of Biosecurity

Author

Listed:
  • Lyle Fearnley

    (Department of Medical Anthropology, University of California at Berkeley 3342A California Street, Berkeley, CA 94703, USA)

Abstract

This paper follows the development of a novel biosecurity technology known as ‘syndromic surveillance’. By monitoring new sources of nondiagnostic health information (911 calls, ER triage logs, pharmaceutical sales), syndromic surveillance produces new ‘territories of intelligibility’. But the implemention of syndromic systems—and the opening up of these new territories—poses a problem of interpretation. What is significant in nondiagnostic data flows? In fact, the development of a national syndromic system in the United States attracted criticism from local public health experts, who complained about the costs of ‘false positive’ or insignificant detections. This exposes a disjuncture between two interpretative frameworks, two styles of governing biosecurity: public health (a responsibility for maximal population health) and preparedness (a concern for disaster-scale events). At stake are new norms and forms of securing life.

Suggested Citation

  • Lyle Fearnley, 2008. "Signals Come and Go: Syndromic Surveillance and Styles of Biosecurity," Environment and Planning A, , vol. 40(7), pages 1615-1632, July.
    • Handle: RePEc:sae:envira:v:40:y:2008:i:7:p:1615-1632
    DOI: 10.1068/a4060
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1068/a4060
    Download Restriction: no
    File URL: https://libkey.io/10.1068/a4060?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    2. Savitz, D.A. & Poole, C. & Miller, W.C., 1999. "Reassessing the role of epidemiology in public health," American Journal of Public Health, American Public Health Association, vol. 89(8), pages 1158-1161.
    3. Fee, E. & Brown, T.M., 2001. "Preemptive biopreparedness: Can we learn anything from history?," American Journal of Public Health, American Public Health Association, vol. 91(5), pages 721-726.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yi-Da Chen & Susan A. Brown & Paul Jen-Hwa Hu & Chwan-Chuen King & Hsinchun Chen, 2011. "Managing Emerging Infectious Diseases with Information Systems: Reconceptualizing Outbreak Management Through the Lens of Loose Coupling," Information Systems Research, INFORMS, vol. 22(3), pages 447-468, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dong Ding & Axel Gandy & Georg Hahn, 2020. "A simple method for implementing Monte Carlo tests," Computational Statistics, Springer, vol. 35(3), pages 1373-1392, September.
    2. Sami Ullah & Hanita Daud & Sarat C. Dass & Hadi Fanaee-T & Husnul Kausarian & Alamgir, 2020. "Space-Time Clustering Characteristics of Tuberculosis in Khyber Pakhtunkhwa Province, Pakistan, 2015–2019," IJERPH, MDPI, vol. 17(4), pages 1-10, February.
    3. Costa, Marcelo Azevedo & Ruiz-Cárdenas, Ramiro & Mineti, Leandro Brioschi & Prates, Marcos Oliveira, 2021. "Dynamic time scan forecasting for multi-step wind speed prediction," Renewable Energy, Elsevier, vol. 177(C), pages 584-595.
    4. Yingqi Zhao & Donglin Zeng & Amy H. Herring & Amy Ising & Anna Waller & David Richardson & Michael R. Kosorok, 2011. "Detecting Disease Outbreaks Using Local Spatiotemporal Methods," Biometrics, The International Biometric Society, vol. 67(4), pages 1508-1517, December.
    5. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    6. Hadeel AlQadi & Majid Bani-Yaghoub & Sindhu Balakumar & Siqi Wu & Alex Francisco, 2021. "Assessment of Retrospective COVID-19 Spatial Clusters with Respect to Demographic Factors: Case Study of Kansas City, Missouri, United States," IJERPH, MDPI, vol. 18(21), pages 1-15, November.
    7. Ibrahim Musa & Hyun Woo Park & Lkhagvadorj Munkhdalai & Keun Ho Ryu, 2018. "Global Research on Syndromic Surveillance from 1993 to 2017: Bibliometric Analysis and Visualization," Sustainability, MDPI, vol. 10(10), pages 1-20, September.
    8. Frisén, Marianne, 2008. "Introduction to financial surveillance," Research Reports 2008:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    9. Jingnan Zhang & Yicheng Kang & Yang Yang & Peihua Qiu, 2015. "Statistical monitoring of the hand, foot and mouth disease in China," Biometrics, The International Biometric Society, vol. 71(3), pages 841-850, September.
    10. M. R. Martines & R. V. Ferreira & R. H. Toppa & L. M. Assunção & M. R. Desjardins & E. M. Delmelle, 2021. "Detecting space–time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities," Journal of Geographical Systems, Springer, vol. 23(1), pages 7-36, January.
    11. Sami Ullah & Hanita Daud & Sarat C Dass & Hadi Fanaee-T & Alamgir Khalil, 2018. "An Eigenspace approach for detecting multiple space-time disease clusters: Application to measles hotspots detection in Khyber-Pakhtunkhwa, Pakistan," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.
    12. Sevvandi Kandanaarachchi & Rob J Hyndman & Kate Smith-Miles, 2020. "Early classification of spatio-temporal events using partial information," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-39, August.
    13. Assuno, Renato & Correa, Thais, 2009. "Surveillance to detect emerging space-time clusters," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2817-2830, June.
    14. Lele Deng & Yajun Han & Jinlong Wang & Haican Liu & Guilian Li & Dayan Wang & Guangxue He, 2023. "Epidemiological Characteristics of Notifiable Respiratory Infectious Diseases in Mainland China from 2010 to 2018," IJERPH, MDPI, vol. 20(5), pages 1-16, February.
    15. Suparna Das & Jenevieve Opoku & Adam Allston & Michael Kharfen, 2018. "Detecting spatial clusters of HIV and hepatitis coinfections," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-13, September.
    16. de Lima, Max Sousa & Duczmal, Luiz Henrique, 2014. "Adaptive likelihood ratio approaches for the detection of space–time disease clusters," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 352-370.
    17. Schiöler, Linus, 2009. "Explorative analysis of spatial patterns of influenza incidences in Sweden 1999—2008," Research Reports 2008:5, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    18. Thais Paiva & Renato Assunção & Taynãna Simões, 2015. "Prospective space–time surveillance with cumulative surfaces for geographical identification of the emerging cluster," Computational Statistics, Springer, vol. 30(2), pages 419-440, June.
    19. William H. Woodall & J Brooke Marshall & Michael D. Joner Jr & Shannon E Fraker & Abdel‐Salam G Abdel‐Salam, 2008. "On the use and evaluation of prospective scan methods for health‐related surveillance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 223-237, January.
    20. Marianne Frisén, 2014. "Spatial outbreak detection based on inference principles for multivariate surveillance," IISE Transactions, Taylor & Francis Journals, vol. 46(8), pages 759-769, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envira:v:40:y:2008:i:7:p:1615-1632. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.