ArticleMedical anthropology quarterly2018
Cell Phones ≠ Self and Other Problems with Big Data Detection and Containment during Epidemics.
Article in Medical anthropology quarterly, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- AI in variant analysis: fast track to genetic diagnoses.Human genetics · 2026Review
- Socioeconomic reorganization of communication and mobility networks in response to external shocks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Disparities in mobile phone ownership reflect inequities in access to healthcare.PLOS digital health · 2023Article
- Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health.Annual review of public health · 2022Review
- COVID-Apps: Misdirecting Public Health Attention in a Pandemic.Global policy · 2021Article
- Cell-phone traces reveal infection-associated behavioral change.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic.Information systems frontiers : a journal of research and innovation · 2021Article
- Weak Data: The Social Biography of a Measurement Instrument and How It Failed to Ensure Accountability in Home Care.Medical anthropology quarterly · 2020Article
- AI for social good: unlocking the opportunity for positive impact.Nature communications · 2020Review
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Authors and funding
1 author.
Funding
Abstract
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. Finally, what counts as data during epidemics must include that coming from anthropological technologies because they are so useful for detection and containment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.