COVID-19—Extending Surveillance and the Panopticon

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Abstract

Surveillance is a core function of all public health systems. Responses to the COVID-19 pandemic have deployed traditional public health surveillance responses, such as contact tracing and quarantine, and extended these responses with the use of varied technologies, such as the use of smartphone location data, data networks, ankle bracelets, drones, and big data analysis. Applying Foucault’s (1979) notion of the panopticon, with its twin focus on surveillance and self-regulation, as the preeminent form of social control in modern societies, we examine the increasing levels of surveillance enacted during this pandemic and how people have participated in, and extended, this surveillance, self-regulation, and social control through the use of digital media. Consideration is given to how such surveillance may serve public health needs and/or political interests and whether the rapid deployment of these extensive surveillance mechanisms risks normalizing these measures so that they become more acceptable and then entrenched post-COVID-19.

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Most cited references 18

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Real-time tracking of self-reported symptoms to predict potential COVID-19

Cristina Menni, Ana M. Valdes, Maxim Freidin … (2020)

A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.

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Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data

Milena Gianfrancesco, Suzanne Tamang, Jinoos Yazdany … (2018)

A promise of machine learning in health care is the avoidance of biases in diagnosis and treatment; a computer algorithm could objectively synthesize and interpret the data in the medical record. Integration of machine learning with clinical decision support tools, such as computerized alerts or diagnostic support, may offer physicians and others who provide health care targeted and timely information that can improve clinical decisions. Machine learning algorithms, however, may also be subject to biases. The biases include those related to missing data and patients not identified by algorithms, sample size and underestimation, and misclassification and measurement error. There is concern that biases and deficiencies in the data used by machine learning algorithms may contribute to socioeconomic disparities in health care. This Special Communication outlines the potential biases that may be introduced into machine learning–based clinical decision support tools that use electronic health record data and proposes potential solutions to the problems of overreliance on automation, algorithms based on biased data, and algorithms that do not provide information that is clinically meaningful. Existing health care disparities should not be amplified by thoughtless or excessive reliance on machines.

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Discipline and Punish : The Birth of the Prison

Michel Foucault (1979)

In this brilliant study, one of the most influential philosophers alive sweeps aside centuries of sterile debate about prison reform and gives a highly provocative account of how penal institutions and the power to punish became a part of our lives. Foucault explains the alleged failures of the modern prison by showing how the very concern with rehabilitation encourages and refines criminal activity.