Markovian And Non-Markovian (Discrete) Spatio-Temporal Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic

Research Overview

Drs. Moulinath Banerjee and Ya’acov Ritov, Professors of Statistics, are developing cutting-edge statistical models that incorporate many complex features of the pandemic, including people’s mobility patterns, testing capacity and pressure on the healthcare system, and last but not least, active decision making strategies as the epidemic evolves. Their model will help policymakers understand both the short-term and long-term impact of the pandemic on human health and on the economy.

The MIDAS COVID-19 Propelling Original Data Science (PODS) grants were awarded on May 13th, 2020, each of the 7 teams received funding of up to $30,000 with projects starting immediately and expected to finish by the end of 2020. These projects demonstrate the resolve, expertise, and creativity of U-M data scientists facing a public health crisis. To learn more about the other projects please visit our COVID-19 PODS awards page.

Research Team

Moulinath Banerjee, Professor of Statistics, University of Michigan

Ya’acov Ritov, Professor Statistics, University of Michigan

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