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Quantifying the Impact of Data Sharing on Outbreak Dynamics

Jundong Li, School of Engineering and Applied Science, Electrical and Computer Engineering, Computer Science & School of Data Science
Daniel Mietchen, School of Data Science
This project was awarded Undergraduate Research Award Supplement

In this project, Jundong Li and Daniel Mietchen will explore the range of data-related decisions made during COVID-19 and analyze the flow of information, data, and metadata. Data sharing is now considered a key component of addressing present, future, and even past public health emergencies, from local to global levels. Researchers, research institutions, journals and others have taken steps towards increasing the sharing of data around ongoing COVID-19 and in preparation for future pandemics.

Their team will quantify the effects of data flow modifications to identify parameter sets under which specific modes of sharing or withholding information have the largest effects. For these high-impact parameter sets, they will then assess the current and past availability of corresponding data, metadata, and misinformation, and estimate the effects on outbreak mitigation and preparedness efforts.

Grant Date/Year: 
May, 2020