The goal of this COVID-19 mapping project is to put COVID in its spatial and social context, and to allow better exploration and understanding of geographic patterns in detected cases and deaths. Additionally, this project will explore how the distribution of COVID-19 cases change with respect to social risk factors, such as urban/rural environment, age and race
With support from the Gordon and Betty Moore Foundation, the Society for Medical Decision Making (SMDM) has partnered with researchers from the University of Michigan, Johns Hopkins School of Nursing (JHSON), and Duke University to catalyze innovative COVID-19 decision models for rapid uptake and impact.
A team of University of Michigan researchers is analyzing epidemiologic data from India to make predictions and recommendations as it relates to the continued spread of COVID-19. With a population of 1.34 billion, this data-driven modeling can inform policymakers and stakeholders India as they address this global pandemic.
We have developed a spatio-temporal epidemiological prediction model to inform county-level COVID-19 risk in the USA. Through this new health information system we hope to provide timely risk evaluation and prediction of the COVID-19 infection in communities. This is the first modeling paradigm to use self-immunization from antibody tests to predict community level risk.
Developed a health informatics tool enabling analysis and evaluation of a range of control measures on the covid-19 pandemics. As a case study, we examine the COVID-19 pandemic using publicly available data from China’s CDC and the USA. The tool is built on an extended epidemiological model to incorporate various time-varying intervention protocols, including large-scale, government-level isolation policies and local community-level social distancing measures.
This project uses electronic health records data from Michigan Medicine to build models for who gets tested, who gets diagnosed positive, who needs hospitalization and what features predict severe outcomes for COVID-19. This is joint work between many researchers at the University of Michigan School of Public Health and Michigan Medicine.
A team of researchers is developing mathematical models to predict the true infection rate and detect spikes, taking into account the limited and imperfect testing. Their model will also provide guidance to policymakers on how to allocate testing resources.
Increased social distance measures over the Thanksgiving and Christmas seasons in 2020 might have prevented 109,000 coronavirus cases in Michigan, potentially avoiding 2,800 deaths during the holiday season, according to University of Michigan School of Public Health models.