May 13, 2020: The Michigan Institute for Data Science (MIDAS) announced today the awardees of its COVID-19 data science grants. 7 interdisciplinary teams, chosen from 49 submissions, will receive funding support for COVID-19 research. With data science methods at the core, these teams combat the pandemic in varied and creative ways, including better clinical decisions for in-patients, strategies to improve testing, data-driven guidance for social distancing and reopening, detection of high-risk communities; and addressing inequality in healthcare and in the society’s recovery. These projects, along with many other excellent proposals that we wish we had funding for, demonstrate the resolve, expertise and creativity of U-M data scientists facing a public health catastrophe. They lend us hope that science will help us prevail.
Each of the 7 teams will receive funding of up to $30,000, and the projects will start right away and finish by the end of 2020. This round of funding is part of the MIDAS Propelling Original Data Science (PODS) pilot funding program.
The awarded PODS grants and the (co) Principal Investigators are:
- Improved real-time surveillance of COVID-19 patients’ electronic health records using transfer learning and ordinal regression, Andrew Admon (Internal Medicine), Christopher Gillies (Emergency Medicine)
- Markovian And Non-Markovian (Discrete) Spatio-Temporal Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic, Moulinath Banerjee and Ya’acov Ritov (Statistics)
- Estimating county-level age group contact rates from time use data, Jacob Fisher and Yajuan Si (Institute for Social Research)
- From data to knowledge: Applying the principles of data science to understand preventable inequities in care and support among COVID-19 cases in Michigan, Nancy Fleischer (Epidemiology)
- To learn about how their work provides insight to the impacts of “Long Covid,” read more
- Handling Selective and Imperfect Testing in Design and Inference of COVID-19 Studies, Bhramar Mukherjee (Biostatistics)
- Students’ mobility patterns on campus and the implications for the recovery of campus activities post-pandemic, Quan Nguyen, Christopher Brooks, Daniel Romero (School of Information)
- High-resolution spatial analysis to inform the response to COVID-19 in Michigan, Jon Zelner (Epidemiology)