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Who Were Where When?: On the Use of Social Collective Intelligence in Computational Epidemiology
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0001-7949-1815
2014 (English)In: Social Collective Intelligence / [ed] Daniele Miorandi, Vincenzo Maltese, Michael Rovatsos, Anton Nijholt and James Stewart, Switzerland: Springer , 2014, 203-225 p.Chapter in book (Refereed)
Abstract [en]

A triangular (case, theoretical, and literature) study approach is used to investigate if and how social collective intelligence is useful to computational epidemiology. The hypothesis is that the former can be employed for assisting in converting data into useful information through intelligent analyses by deploying new methods from data analytics that render previously unintelligible data intelligible. A conceptual bridge is built between the two concepts of crowd signals and syndromic surveillance. A concise list of empirical observations supporting the hypothesis is presented. The key observation is that new social collective intelligence methods and algorithms allow for massive data analytics to stay with the individual, in micro. It is thus possible to provide the analyst with advice tailored to the individual and with relevant policies, without resorting to macro (statistical) analyses of homogeneous populations.

Place, publisher, year, edition, pages
Switzerland: Springer , 2014. 203-225 p.
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:kth:diva-159146DOI: 10.1007/978-3-319-08681-1_10ISBN: 978-3-319-08680-4 (print)ISBN: 978-3-319-08681-1 (print)OAI: oai:DiVA.org:kth-159146DiVA: diva2:782664
Note

QC 20150216

Available from: 2015-01-22 Created: 2015-01-22 Last updated: 2015-02-16Bibliographically approved

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Boman, Magnus

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CiteExportLink to record
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Citation style
  • apa
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  • Other locale
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Output format
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