Michigan Institute for Data Science

The shift in COVID-19 caseloads around the U.S. in recent weeks has scrambled supply and demand in the nation’s testing infrastructure. Even though testing capacity has increased dramatically since spring, the hardest-hit geographical areas are seeing demand outstripping supply, while a few areas are actually reporting excess capacity.

Now, University of Michigan industrial operations and engineering associate professor Siqian Shen, with support from Microsoft’s AI for Health program, is stepping in to help, designing a cloud-based system that will analyze infection and testing supply chain data to better organize the logistics of testing. It will provide policymakers with data that can help them determine how to distribute tests and site mobile testing centers to make testing as efficient and accessible as possible.

“Without a centralized source of data, it’s very difficult to optimize testing and minimize the spread of COVID.” Shen said. “By pulling in daily updates on infection rates and anonymous demographic data, along with data from the testing supply chain, we’re building a mathematical computer model that will crunch that data and provide visualizations that could help policymakers optimize testing and reduce the spread of the disease.”

Read more: https://news.engin.umich.edu/2020/07/how-big-data-could-optimize-covid-19-testing/