How do algorithms shape the imaginary and practice of security? Does their proliferation point to a shift in the political rationality of security? If so, what is the nature and extent of that shift? This article argues that efforts to strengthen global health security are major drivers in the development and proliferation of new algorithmic security technologies. In response to a seeming epidemic of potentially lethal infectious disease outbreaks -like HIV, SARS, pandemic flu, MERS, Ebola and Zika -governments and international organizations are now using several next-generation syndromic surveillance systems to rapidly detect new outbreaks globally. This article analyses the origins, design and function of three such internet-based surveillance systems: 1) the Program for Monitoring Emerging Diseases, 2) the Global Public Health Intelligence Network, and 3) HealthMap. The article shows how each newly-introduced system became progressively more reliant upon algorithms to mine an ever-growing volume of indirect data sources for the earliest signs of a possible new outbreak -gradually propelling algorithms into the heart of global outbreak detection. That turn to the algorithm marks a significant shift in the underlying problem, nature, and role of knowledge in contemporary security policy.
Since the start of the COVID-19 pandemic, a steady stream of propositions from tech giants and start-ups alike has furnished us with the idea that GPS-or Bluetooth-enabled contact tracing apps are a vital part of the pandemic response. This commentary considers these apps as 'corporate contact tracing', emphasizing the private-sector role that such developments imply. We first discuss corporate contact tracing's potential to decenter the power of public health authorities. Then, using the frames of surveillance capitalism and disaster capitalism, we suggest how corporate contact tracing might feed the rise of corporate power in the public sphere. We question its capacity to address structural inequalities and to foster a social justice vision of public health. And, we wonder whether corporate contact tracing might intensify the effects of discriminatory design and algorithmic oppression. We conclude by calling for a discussion of this technology beyond questions of privacy and efficacy.
In order to combat the COVID-19 pandemic, policymakers around the globe have increasingly invested in digital health technologies to support the 'test, track and trace' approach of containing the spread of the novel coronavirus. These technologies include mobile 'contact tracing' applications (apps), which can trace individuals likely to have come into contact with those who have reported symptoms or tested positive for the virus and request that they self-isolate. This paper takes a critical public health perspective that advocates for 'genuine participation' in public health interventions and emphasises the need to take citizen's knowledge into account during public health decision-making. In doing so, it presents and discusses the findings of a UK interview study that explored public views on the possibility of using a COVID-19 contact-tracing app public health intervention at the time the United Kingdom (UK) Government announced their decision to develop such a technology. Findings illustrated interviewees' range and degree of understandings, misconceptions, and concerns about the possibility of using an app. In particular, concerns about privacy and surveillance predominated. Interviewees associated these concerns much more broadly than health by identifying with pre-existent British national narratives associated with individual liberty and autonomy. In extending and contributing to ongoing sociological research with public health, we argue that understanding and responding to these matters is vital, and that our findings demonstrate the need for a forward-looking, anticipatory strategy for public engagement as part of the responsible innovation of the COVID-19 contacttracing app in the UK.
This article investigates the rise of algorithmic disease surveillance systems as novel technologies of risk analysis utilised to regulate pandemic outbreaks in an era of big data. Critically, the article demonstrates how intensified efforts towards harnessing big data and the application of algorithmic processing techniques to enhance the real-time surveillance and regulation infectious disease outbreaks significantly transform practices of global infectious disease surveillance; observed through the advent of novel risk rationalities which underpin the deployment of intensifying algorithmic practices to increasingly colonise and patrol emergent topographies of data in order to identify and govern the emergence of exceptional pathogenic risks. Conceptually, this article asserts further howthe rise of these novel risk regulating technologies within a context of big data transforms the government and forecasting of epidemics and pandemics: illustrated by the rise of emergent algorithmic governmentalties of risk within contemporary contexts of big data, disease surveillance and the regulation of pandemic.
Contemporary infectious disease surveillance systems aim to employ the speed and scope of big data in an attempt to provide global health security. Both shifts - the perception of health problems through the framework of global health security and the corresponding technological approaches – imply epistemological changes, methodological ambivalences as well as manifold societal effects. Bringing current findings from social sciences and public health praxis into a dialogue, this conversation style contribution points out several broader implications of changing disease surveillance. The conversation covers epidemiological issues such as the shift from expert knowledge to algorithmic knowledge, the securitization of global health, and the construction of new kinds of threats. Those developments are detailed and discussed in their impacts for health provision in a broader sense.
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