Open Data-Driven Decision Making: Observations from COVID-19 Minimal Essential Data, Canton Zürich, Switzerland.

Abstract

This research delves into the relationship between open data and decision making, drawing insights from the utilization of Minimal Essential Data (MED) during the COVID-19 pandemic in Canton Zürich, Switzerland. It examines observations and lessons learned, emphasizing the pivotal role of open data in response to an emergency. Through a case study approach, this article sheds light on the practical implications and observed strategies in publishing open data from digital public health surveillance software. While it is arguable that open data has the potential to enhance data quality in the context of a public health emergency, our findings indicate that it is firstly essential that a Minimal Essential Dataset (MED) is defined and publicly published, an open data publication platform and procedure are secondly in place (ideally following FAIR data principles), and thirdly, that regular data quality reviews are conducted.