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Detecting the Visible: The Discursive Construction of Health Threats in a Syndromic Surveillance System Design

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  • Baki Cakici

    (Swedish Institute of Computer Science (SICS), Kista 164 29, Sweden
    Department of Computer and Systems Sciences, Stockholm University, DSV, Kista 164 40, Sweden)

  • Pedro Sanches

    (Swedish Institute of Computer Science (SICS), Kista 164 29, Sweden
    Royal Institute of Technology (KTH/ICT/SCS), Kista 164 40, Sweden)

Abstract

Information and communication technologies are not value-neutral tools that reflect reality; they privilege some forms of action, and they limit others. We analyze reports describing the design, development, testing and evaluation of a European Commission co-funded syndromic surveillance project called SIDARTHa (System for Information on Detection and Analysis of Risks and Threats to Health). We show that the reports construct the concept of a health threat as a sudden, unexpected event with the potential to cause severe harm and one that requires a public health response aided by surveillance. Based on our analysis, we state that when creating surveillance technologies, design choices have consequences for what can be seen and for what remains invisible. Finally, we argue that syndromic surveillance discourse privileges expertise in developing, maintaining and using software within public health practice, and it prioritizes standardized and transportable knowledge over local and context-dependent knowledge. We conclude that syndromic surveillance contributes to a shift in broader public health practice, with consequences for fairness if design choices and prioritizations remain invisible and unchallenged.

Suggested Citation

  • Baki Cakici & Pedro Sanches, 2014. "Detecting the Visible: The Discursive Construction of Health Threats in a Syndromic Surveillance System Design," Societies, MDPI, vol. 4(3), pages 1-15, July.
  • Handle: RePEc:gam:jsoctx:v:4:y:2014:i:3:p:399-413:d:38599
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    References listed on IDEAS

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    1. Minna Ruckenstein, 2014. "Visualized and Interacted Life: Personal Analytics and Engagements with Data Doubles," Societies, MDPI, vol. 4(1), pages 1-17, February.
    2. Declan Butler, 2013. "When Google got flu wrong," Nature, Nature, vol. 494(7436), pages 155-156, February.
    3. Sophia Alice Johnson, 2014. "“Maternal Devices”, Social Media and the Self-Management of Pregnancy, Mothering and Child Health," Societies, MDPI, vol. 4(2), pages 1-21, June.
    4. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
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