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

Erikson, Susan L.

doi:10.1111/maq.12440

Cited by 44 publications

(34 citation statements)

References 30 publications

(30 reference statements)

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“…The HealthMap, which covers several sources of data to monitor outbreaks of infectious diseases worldwide, has the limitation of being configured for the English language, losing initial notifications of diseases reported in other languages (Erikson 2018 ). Thus, only later the outbreak can be detected which can become a larger problem than it could really be if detected in its early stages.…”

Section: Resultsmentioning

confidence: 99%

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Big data analytics as a tool for fighting pandemics: a systematic review of literature

Corsi

Souza

Pagani

et al. 2020

J Ambient Intell Human Comput

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Infectious and contagious diseases represent a major challenge for health systems worldwide, either in private or public sectors. More recently, with the increase in cases related to these problems, combined with the recent global pandemic of COVID-19, the need to study strategies to treat these health disturbs is even more latent. Big Data, as well as Big Data Analytics techniques, have been addressed in this context with the possibility of predicting, mapping, tracking, monitoring, and raising awareness about these epidemics and pandemics. Thus, the purpose of this study is to identify how BDA can help in cases of pandemics and epidemics. To achieve this purpose, a systematic review of literature was carried out using the methodology Methodi Ordinatio. The rigorous search resulted in a portfolio of 45 articles, retrived from scientific databases. For the collection and analysis of data, the softwares NVivo 12 and VOSviewer were used. The content analysis sought to identify how Big Data and Big Data Analytics can help fighting epidemics and pandemics. The types and sources of data used in cases of previous epidemics and pandemics were identified, as well as techniques for treating these data. The results showed that the main sources of data come from social media and Internet search engines. The most common techniques for analyzing these data involve the use of statistics, such as correlation and regression, combined with other techniques. Results shows that there is a fruitiful field of study to be explored by both areas, Big Data and Health.

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Section: Resultsmentioning

confidence: 99%

“…In this scenario, the Digital Humanitarians (Meier 2015 ) aim to apply Big Data Analytics to humanitarian aid through a participatory surveillance system, a form of crowdsourcing that creates real-time communication on social media (Erikson 2018 ). Participatory surveillance systems allow people to report via Internet, as in crowdsourcing.…”

Section: Resultsmentioning

confidence: 99%

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Corsi

Souza

Pagani

et al. 2020

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…2018; Wiener 2000). Data work has become a fertile ground for ethnographic studies exploring what data do to people and what people do with data (Adams 2016a; Biruk 2017, 2018; Carruth 2018; Erikson 2018; Hogle 2019; Hunt et al. 2017; Merry 2016).…”

Section: In Need Of Measurement? the Datafication Of Home Carementioning

confidence: 99%

Weak Data: The Social Biography of a Measurement Instrument and How It Failed to Ensure Accountability in Home Care

Hoeyer

Bødker

2020

Med Anthropol Q

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Contemporary health and social care is saturated by processes of datafication. In many cases, these processes are nested within an ostensibly simple logic of accountability: Define a politically and morally desirable goal, then measure the level of achievement. This logic has come to permeate public health initiatives globally and today it operates in most health care systems in various ways. We explore here a particular instantiation of the logic associated with the introduction of a measurement instrument used in Danish home care. Building on ethnographic fieldwork, interviews, and analysis of policy documents, we show how the instigated processes of datafication—despite hopeful political claims—erode care levels and disempower older people. We believe that these findings can be of relevance for other settings that subscribe to the same accountability logic and to similar forms of measurement instruments.

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“…20. Susan L. Erikson (2018), “Cell Phones ≠ Self and Other Problems with Big Data Detection and Containment during Epidemics,” Medical Anthropology Quarterly 32, no. 3: 315–39, doi: 10.1111/maq.12440 [published Online First: 2018/03/10].…”

mentioning

confidence: 99%

The imaginary of precision public health

Kenney

Mamo

2019

Med Humanities

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In recent years, precision medicine has emerged as a charismatic name for a growing movement to revolutionise biomedicine by bringing genomic knowledge and sequencing to clinical care. Increasingly, the precision revolution has also included a new paradigm called precision public health—part genomics, part informatics, part public health and part biomedicine. Advocates of precision public health, such as Sue Desmond-Hellmann, argue that adopting cutting-edge big data approaches will allow public health actors to precisely target populations who experience the highest burden of disease and mortality, creating more equitable health futures. In this article we analyse precision public health as a sociotechnical imaginary, examining how calls for precision shape which public health efforts are seen as necessary and desirable. By comparing the rhetoric of precision public health to precision warfare, we find that precision prescribes technical solutions to complex problems and promises data-driven futures free of uncertainty, unnecessary suffering and inefficient use of resources. We look at how these imagined futures shape the present as they animate public health initiatives in the Global South funded by powerful philanthropic organisations, such as the Bill & Melinda Gates Foundation, as well as local efforts to address cancer disparities in San Francisco. Through our analysis of the imaginary of precision public health, we identify an emerging tension between health equity goals and precision’s technical solutions. Using large datasets to target interventions with greater precision, we argue, fails to address the upstream social determinants of health that give rise to health disparities worldwide. Therefore, we urge caution around investing in precision without a complementary commitment to addressing the social and economic conditions that are the root cause of health inequality.

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

Cited by 44 publications

References 30 publications

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Big data analytics as a tool for fighting pandemics: a systematic review of literature

Weak Data: The Social Biography of a Measurement Instrument and How It Failed to Ensure Accountability in Home Care

The imaginary of precision public health

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