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Sterl, S., Almalla, N., & Gerhold, L. (2023). Conceptualizing a Pandemic Early Warning System Using Various Data: An Integrative Approach. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 284–294). Omaha, USA: University of Nebraska at Omaha.
Abstract: Covid-19 demonstrated the vulnerability of various systems and showed, however, that digital tools and data can serve not only to stop infections but also to detect viruses before or immediately after a zoonosis has occurred, thus preventing a potential pandemic. Although several pandemic early warning systems (P-EWS) and German pandemic-related projects (G-PRP) exist, they often use a limited data range or rely on third-party data. Here, we present a concept of an integrative pandemic early warning system (IS-PAN) applied to Germany using various data such as health data (e.g., clinical/syndromic) or internet data (e.g., social media/apps). Based on a systematic literature research of P-EWS and G-PRP on scientific and public health platforms, we derived indicators that help to detect virus threats with a system consisting of modules monitored in parallel. By integrating various pre collected digital data, this approach can help to identify a potential health threat efficiently and effectively.