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Who Were Where When? On the Use of Social Collective Intelligence in Computational Epidemiology

  • Magnus BomanEmail author
Chapter
Part of the Computational Social Sciences book series (CSS)

Abstract

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.

Keywords

Health Data Personal Health Record Syndromic Surveillance Network Visualisation Passive Surveillance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This chapter emerged from experiences gathered over a period of almost 10 years of working as a computational epidemiologist. The author spent time in 2011 and 2012 as a research fellow at the Etisalat BT Innovation Center (EBTIC) in Abu Dhabi, serving the local health authorities (HAAD), and some of the work reported on in this chapter was carried out at EBTIC, where Benjamin Hirsch led the work. Two keynotes at specialist conferences and a short presentation at a Social-IST meeting in 2013 generated a lot of comments and questions, some of which have been included here. The section on MRSA benefited considerably from illustrations and text from the co-authors of an earlier short paper: Asim Ghaffar, Fredrik Liljeros, and Mikael Stenhem. Some of the ideas in this chapter have also been used for research applications over the years, and some of the formulations here were in connection with this improved by SICS colleagues Anders Holst, Björn Bjurling, Markus Bylund, Pedro Sanches, Baki Cakici, and Daniel Gillblad. Baki Cakici provided the author with important comments on earlier sketches of this chapter. Last but not least, the author wishes to express his sincere thanks to Daniele Miorandi for generously sharing his insights on social collective intelligence.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.SICSKistaSweden
  2. 2.KTH/ICT/SCSKistaSweden

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