Cell Phones ≠ Self and Other Problems with Big Data Detection and Containment during Epidemics

This is a fascinating paper that discusses the follies of using “big data” for the “containment” of the epidemics. I have not included the document in the link, but the complete text follows in the link below.

Primarily, the fundamental flaw to contain the epidemic was to assume that each individual was carrying a cell phone, and they could map location data with the areas identified in the outbreak. This was bankrolled by western funding agencies (there’s an implicit assumption), but the key takeaways could never be extrapolated in the other regions.

It emphasises the importance of socio-cultural contexts in implementation of AI with healthcare. (emphasis mine)

Evidence from Sierra Leone reveals the significant limitations of big data in disease detection and containment efforts.

Early in the 2014–2016 Ebola epidemic in West Africa, media heralded HealthMap’s ability to detect the outbreak from newsfeeds. Later, big data—specifically, call detail record data collected from millions of cell phones—was hyped as useful for stopping the disease by tracking contagious people. It did not work. In this article, I trace the causes of big data’s containment failures. During epidemics, big data experiments can have opportunity costs: namely, forestalling urgent response.

This again brings forth key takeaways- media press releases drum up support with “weaving the narrative” around “human stories”. Previously, they used to term “embedded journalism”. These are marketing spins and spiels, which unfortunately accompany science. The fact that it was published (as an account of failures nearly four years after the study was conducted, is “long enough” for the hype to “fade away from the public mind”. These are regressive practises and could be done away with.

However, the study itself has pertinent issues that merit a close look.

via Cell Phones ≠ Self and Other Problems with Big Data Detection and Containment during Epidemics – Erikson – 2018 – Medical Anthropology Quarterly – Wiley Online Library