Oregon State University is actively engaged in research around Covid-19 and the Research Office would like to encourage both internal and external collaboration.  We will provide updates on current activities on this page.
 

For insights on Covid-19: Register your interests & assets for Covid Research:  OSU Collaborators: External Funding Opportunities:
OSU COVID experts OSU Internal Interest Form OSU Research inventory External Funding

 

If you are not currently part of OSU’s research efforts and would like to connect with the OSU research community for collaboration please contact the OSU research office:

Susan Emerson  or Mark Peters

 

While many research areas will not need biosafety support, the Research Office will work with researchers when biosafety resources are needed and this may include facilitate collaboration with other institutions like OHSU.  Please be aware, work with the new coronavirus that causes COVID-19, SARS-CoV-2, involving isolation and/or inoculation for growth of the virus in cell culture or animals may only be done at biosafety level 3 (BSL-3). Such work at OSU is not possible at this time because existing BSL-3 laboratories are currently non-functional.  Other work with specimens such as blood, sputum, nasopharyngeal swabs, or fecal samples that may contain SARS-CoV-2 must be reviewed by the Institutional Biosafety Committee (IBC) and performed in a BSL-2 laboratory with a functioning Biological Safety Cabinet.

COVID-19 Research efforts at Oregon State University

Existing Projects:

Melissa Haendelmelissa.haendel@oregonstate.edu@ontowonka on Twitter

Haendel is OSU's director of translational data science, as well as the director of the Center for Data to Health at Oregon Health & Science University. She heads the new NIH-funded National COVID Cohort Collaborative (N3C) program, a broad partnership that's working to make it easier to share COVID-19 clinical data to better analyze the disease and its impact.  covid.cd2h.org.   Here is the corresponding website at NIH: https://ncats.nih.gov/n3c

She leads several other initiatives focused on leveraging basic research data, clinical data, and patient-generated data

  • Community outreach and coordination on best practices for reducing the impact of the COVID-19 pandemic; see https://www.flattenthecurve.com/
  • Coordinating clinical and research data and knowledge sharing across the U.S. and abroad 
  • Open science policies and scientific communication

TRACE-COVID-19

Team-based Rapid Assessment of Community-level coronavirus Epidemics

TRACE-COVID-19 is a public health project that will gather timely and lifesaving information that is essential for informing measures to slow the spread and minimize the impact of the disease. Because testing has been limited, and because only individuals with symptoms have been tested, no one knows how many people in Corvallis — or most other places — actually have the virus. TRACE-COVID-19 will fill that gap first in Corvallis, and we hope later in other Oregon communities and across the nation.

Highlights of Faculty Research and Resources to Share

Geoffrey Hollinger, Associate Professor of Mechanical Engineering and Robotics - Collaborative Robotics and Intelligent Systems Institute

Areas of research

Robotic systems: decision making, machine learning, and motion planning

I am interested in developing decision-making capabilities for robotic systems performing healthcare tasks.  Also, my work in modeling and machine learning could be applied to tracking the spread of the virus and/or developing sensing and containment strategies.

 

Weng-Keen Wong, Associate Professor School of Electrical Engineering and Computer Science

I have worked extensively in the past at the intersection of machine learning and epidemiology, specifically in syndromic surveillance. My past work has involved machine learning algorithms to detect the onset of disease outbreaks from Emergency Room visits and pharmacy sales. My current research interests that could be of use to Covid-19 research include anomaly detection (detecting unusual events), spatio-temporal data analysis (detecting clusters of cases in space-time), species distribution modeling (estimating underlying abundance of cases from observed cases) and explainable AI (explaining the outputs of AI algorithms).