Faculty: Elisabeth Root, Greg Rempala, Joe Tien, Courtney Hebert and Mark Weir
Serving on a Rapid Response Modeling Team working with the Ohio Department of Health and Ohio Hospital Association to forecast COVID timing and peak, hospital loads from COVID, infectious disease dynamics, containment processes on networks and social media, and disinformation and health behavior response.
Faculty: Greg Rempala, Elisabeth Root, Joe Tien, Courtney Hebert, Mark Weir, Ayaz Hyder, Sam Malloy and Ness Shroff
ServA Recovery Team assisting Ohio Department of Health with COVID monitoring to identify hot spots and resurgence, and with modeling “what-if” scenarios around various aspects of resuming public day-to-day activities.
Faculty: Ayaz Hyder, Subhadeep Paul, Sam Malloy and Harvey Miller
Working with Columbus Public Health using a model that is running at the Ohio Supercomputer Center and includes mobility data.
Faculty:
Lang Li – multiple projects, including data warehousing for research use
Shili Lin – virus/host interactions
Igor Jouline – coronavirus DNA and protein sequence analysis
Faculty: Tanya Berger-Wolf and Srini Parthasarathy
No results were found.
The Ohio Supercomputer Center is providing unbilled access to computing, storage and high-priority queuing for COVID-19 research projects. To create a project account, email oschelp@osc.edu or call 800-686-6472.
OSU’s Center for Clinical and Translational Science is providing emergency vouchers to OSU and Nationwide Children’s Hospital investigators to enable preliminary work and generate data for new COVID-19 projects and/or to secure fee-based core services for expert consultation services toward understanding and combatting the pandemic.
No results were found.
The annual Call for Code challenge asks innovators to create practical, effective, and high-quality applications based on one or more IBM Cloud™ services (e.g., web, mobile, data, analytics, AI, IoT, or weather) that can have an immediate and lasting impact on humanitarian issues. Winning solutions are deployed in communities that need the most help.
This year’s competition will have two tracks: one for solutions that help halt and reverse the impact of climate change, and one for solutions to mitigate the impact of global pandemics like COVID-19 on communities around the world. The contest seeks coding solutions in the areas of immediate crisis communication, enhanced remote education, or stronger community cooperation.
Funding amount: up to $270K per project
Application deadline: July 31, 2020
The National Institute for Allergy and Infectious Diseases has issued two new expedited funding opportunities for COVID-19 research. Areas of high priority include computational modeling studies to identify and evaluate interventions to protect at-risk populations and for making public health policy decisions for control and mitigation measures. Applications are being accepted on a rolling basis.
PAR-20-177 uses the R21 mechanism
PAR-20-178 uses the R01 grant mechanism
The Models of Infectious Disease Agent Study network seeks input for its work with NIGMS and the NIH Office for Data Science to improve access to datasets for COVID-19 modeling, including clinical records, insurance claims data, COVID-19 surveillance data, and data from technology companies.
Click here for types of data and sources they are currently exploring. The study wants to know if: (1) you are already working with data from some of the sources listed; (2) you know of other high-value sources; and (3) any other feedback. Email MIDAS with your feedback or edit/comment in the slides.
The Collaborative Archive & Data Research Environment (CADRE) Project, a cloud-based science gateway to text- and data-mining services for large dataset of which OSU Libraries is a partner, has created a fellowship program for researchers doing work related to COVID-19 or coronaviruses. The Research Cohort for the Study of Coronaviruses program will offer access to Web of Science and Microsoft Academic Graph, technical support and more. Currently there is no deadline for proposals.
No results were found.
