Abstract
The Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) is a European registry for studying the epidemiology and clinical course of COVID-19. To support evidence-generation at the rapid pace required in a pandemic, LEOSS follows an Open Science approach, making data available to the public in real-time. To protect patient privacy, quantitative anonymization procedures are used to protect the continuously published data stream consisting of 16 variables on the course and therapy of COVID-19 from singling out, inference and linkage attacks. We investigated the bias introduced by this process and found that it has very little impact on the quality of output data. Current laws do not specify requirements for the application of formal anonymization methods, there is a lack of guidelines with clear recommendations and few real-world applications of quantitative anonymization procedures have been described in the literature. We therefore believe that our work can help others with developing urgently needed anonymization pipelines for their projects.
Highlights
IntroductionThe novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus II (SARS-CoV-2), and its associated respiratory illness COVID-19 can spread from person to person[1]
The LEOSS Public Use File (PUF) is generated from applying the anonymization pipeline on the primary data of LEOSS
In addition to the LEOSS PUF, we believe that the design of the anonymization pipeline as well as its development process are the most important contributions of our work
Summary
The novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus II (SARS-CoV-2), and its associated respiratory illness COVID-19 can spread from person to person[1]. Until SARS-CoV-2 and COVID-19 have been sufficiently well understood and effective medication or even a vaccine has been developed, non-pharmaceutical interventions, such as social distancing, travel restrictions, closing of public institutions and businesses, quarantines and curfews are alternative options[3,4]. Many of these measures have drastic socio-economic consequences[5]. There is a significant need for rapid access to research data on the novel coronavirus
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