Markovian And Non-Markovian (Discrete) Spatio-Temporal Processes with Active Decision Making Strategies For Addressing The COVID-19 Pandemic
- Funded by University of Michigan
- Total publications:0 publications
Grant number: unknown
Grant search
Key facts
Disease
COVID-19Funder
University of MichiganPrincipal Investigator
Unspecified Unspecified UnspecifiedResearch Location
United States of AmericaLead Research Institution
N/AResearch Priority Alignment
N/A
Research Category
Epidemiological studies
Research Subcategory
Impact/ effectiveness of control measures
Special Interest Tags
N/A
Study Type
Non-Clinical
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
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.