Quantifying the Impact of Data Sharing on Outbreak Dynamics (QIDSOD)

Quantifying the Impact of Data Sharing on Outbreak Dynamics (QIDSOD)

Daniel Mietchen, Jundong Li

In this project, we will explore the range of data-related decisions made during public health emergencies like the ongoing COVID-19 pandemic and analyze the flow of information, data, and metadata within networks of such decisions.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 the ongoing COVID-19 pandemic and in preparation for future pandemics.We 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 on outbreak dynamics. For these high-impact parameter sets, we will then assess the current and past availability of corresponding data, metadata, and misinformation, and estimate the effects on outbreak mitigation and preparedness efforts.

2020

data-sharing, Public health emergencies, epidemiol

DOI: 10.3897/rio.6.e54770

Research Ideas and Outcomes

Pensoft Publishers

Science