The following is a project created by Tamara Velasquez Leiferman for the Global Pandemics in an Unequal World taught by Prof. Sakiko Fukuda-Parr.
Data has been central to the COVID19 pandemic. It has been a present factor in most of the policy responses to the virus, from predictive models used to drive policies to advanced contact-tracing programs. The rapid availability of datasets on COVID19 and its use in creating visualizations and dashboards, along with the 24 hour social media-driven news cycle have made COVID19 the first pandemic we have been able to follow and track in real time. What has this meant for policymaking to combat the pandemic? And, what are the implications of this data? On the one hand, there is great promise in how we can leverage open data to drive public policy responses to the pandemic, and successes in this regard can be seen in places like South Korea and Vietnam, where real-time data leads to highly targeted interventions that are effective at controlling the virus. However, this “real-time” pandemic big data can also raise questions relating to surveillance and the degree to which an over-reliance on data-driven decision-making can ignore social determinants of health. This paper seeks to explore the both potential and the drawbacks of real-time COVID19 data, as well as the real dangers of data opacity and lack of access to information.
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