Data-Powered Positive Deviance during the SARS-CoV-2 Pandemic—An Ecological Pilot Study of German Districts

Abstract

We introduced the mixed-methods Data-Powered Positive Deviance (DPPD) framework as a potential addition to the set of tools used to search for effective response strategies against the SARS-CoV-2 pandemic. For this purpose, we conducted a DPPD study in the context of the early stages of the German SARS-CoV-2 pandemic. We used a framework of scalable quantitative methods to identify positively deviant German districts that is novel in the scientific literature on DPPD, and subsequently employed qualitative methods to identify factors that might have contributed to their comparatively successful reduction of the forward transmission rate. Our qualitative analysis suggests that quick, proactive, decisive, and flexible/pragmatic actions, the willingness to take risks and deviate from standard procedures, good information flows both in terms of data collection and public communication, alongside the utilization of social network effects were deemed highly important by the interviewed districts. Our study design with its small qualitative sample constitutes an exploratory and illustrative effort and hence does not allow for a clear causal link to be established. Thus, the results cannot necessarily be extrapolated to other districts as is. However, the findings indicate areas for further research to assess these strategies’ effectiveness in a broader study setting. We conclude by stressing DPPD’s strengths regarding replicability, scalability, adaptability, as well as its focus on local solutions, which make it a promising framework to be applied in various contexts, e.g., in the context of the Global South.

Positive Deviance Underlies Successful Science: Normative Methodologies Risk Throwing out the Baby With the Bathwater

“…Moreover, unlike smaller datasets in traditional positive deviance research, yielding only a static, cross-sectional performance record, big data can paint a more dynamic picture via longitudinal coverage. Driesen and collaborators (2021), for instance, identified positive deviants from Germany’s 401 administrative districts in their ability to control SARS-CoV-2 transmission. They based the identification not only on daily cases per district but also on weather reports, weekly mobility data, and structural data on ruralness and socio-economic status of the districts.…”