The COVID-19 Epidemiological Investigation Support System (EISS) is a digital epidemiological tool, which utilizes location data from cellular base stations, credit card transactions records, and QR codes. It is a mass surveillance system that uses big data to track the entire infected population, featuring an extensive, automated, and speedy processing of data on personal location and the linkage of multiple databases from various governmental agencies. Based on interviews with people who have developed Korean digital epidemiology systems, this paper explores the technical, infrastructural, social, and institutional factors that have shaped Korean digital epidemiology since the 2014 avian flu crisis and examines the essential conditions of big data for digital epidemiology. The main findings are as follows: The feasibility of EISS goes beyond the matter of privacy; it is closely connected to technological infrastructures such as a high density of cellular base stations and private cloud systems; people’s behavior such as a high rate of smartphone and credit card usage; and new forms of governance and institutions for speedy data processing. Multiple database linkage would develop EISS into a big data surveillance system that enables the prediction of risk-prone groups in a more preemptive manner.
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On 10 April 2020, the Korean government held a briefing session on the COVID-19 Epidemiological Investigation Support System (EISS) for domestic and foreign media. Many foreign journalists attended the briefing, because South Korea’s COVID-19 response appeared to be successful. They also expressed considerable interest in the way the Korean government dealt with privacy while using personal information for EISS. EISS was one of the first government-run digital epidemiology systems in the world to use big data, dramatically reducing the time required to track the movements of infected people and analyzing large-scale outbreak areas. This system, along with drive-through triage clinics, has been effective in controlling COVID-19 in Korea by quickly identifying infected people and quarantining them while avoiding lockdowns (Kim and Chung Citation2021, Citation2022; Kim et al. Citation2020; Ministry of Science and ICT Citation2020; Park et al. Citation2020; The Government of the Republic of Korea Citation2020).
“Digital epidemiology” refers to “epidemiology that uses data that was generated outside the public health system, that is, with data that was not generated with the primary purpose of doing epidemiology” (Salathé Citation2018, 2). Digital epidemiology was fueled by the growth of big data, such as internet or mobile phone data, and computing power over the past decade (National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Global Health, Forum on Microbial Threats, and Joe Alper Citation2016; Salathé Citation2018). “Big data” generally indicates a dataset containing high-volume, high-variety, and/or high-velocity information (Mooney et al. Citation2015). The Internet of Things (IOT), next-generation communication networks (5G), Twitter, artificial intelligence, and block-chain technology are also useful for digital epidemiology (Ting et al. Citation2020). Digital epidemiology signifies the rapid processing of a wide variety of big data; however, that data’s specificity is poor (Mooney et al. Citation2015); digital epidemiology does not completely replace but instead complements traditional epidemiology to create a hybrid system (Bansal et al. Citation2016; Salathé et al. Citation2012; Simonsen et al. Citation2016; Tarkoma et al. Citation2020).
