October COVID-19 Research Lightning Talks: Webinar and Q&A
Meet the scientists seeking new insights on COVID-19. Every month, we bring together a group of researchers studying wide-ranging aspects of the current pandemic, to share their research and answer questions from our community. Learn more about their ongoing efforts in the fight against COVID-19, including opportunities for collaboration.
Join us at our next event on Friday, October 16, at 12-1 pm Eastern Time, featuring lightning talks and Q&A with the following speakers. Register here to receive Zoom information.
Rachel Wu, University of California-Riverside: RAPID: Older adults’ learning and adaptation as resilience processes to counter social isolation during the COVID-19 pandemic. Funded by NSF Social, Behavioral and Economics Sciences / Behavioral and Cognitive Sciences.
- The proposed research will investigate factors that lead to or mitigate against social isolation and loneliness in older adults amid the current physical distancing restrictions. The primary hypothesis is that resilience across adulthood is dependent on two theoretically-derived factors: engagement in novel skill learning and positive personal beliefs. The results of these studies could guide the design of future interventions, such as supportive learning opportunities through technology.
Sara Rampazzi, University of Michigan: RAPID: SaTC: COVID19: Science of using wirelessly powered sensors to quickly scale up verifiable decontamination of individual N95 respirator masks. Funded by NSF Computer and Information Science and Engineering / Computer and Network Systems.
- This research project tackles the urgent research needs to (1) assure and inform healthcare workers of the conditions necessary for mask decontamination processes in ovens with known risks of non-uniform heating, (2) rapidly encourage the deployment of highly scalable and trustworthy sensor network technology for monitoring and validation of individual mask decontamination by using wirelessly powered computational RFID tags, (3) advance the knowledge and understanding of sensor-based systems security by preventing malicious interference from violating the integrity, availability, and confidentiality of sensor data.
Sarah Bowman, Hauptman-Woodward Medical Research Institute, University at Buffalo: RAPID Enhanced SARS-CoV-2 High-Throughput Crystallization for Structural Studies. Funded by NSF Biological Sciences / Biological Infrastructure.
- The Crystallization Center provides state-of-the-art robotic equipment and specialized imaging to structural biology researchers worldwide who are studying protein structures. This project will provide immediate access to these resources and enable enhanced services to be developed for researchers working directly with SARS-CoV-2 related samples. This project will also develop new experimental pipelines to accelerate response time in the face of the current and future pandemics.
Samantha Penta, University at Albany: RAPID: A Multi-Wave Study of Risk Perception, Information Seeking, and Protective Action in COVID-19. Funded by NSF Engineering / Civil, Mechanical and Manufacturing Innovation.
- Adopting a multi-wave cross-sectional design, the research team will survey samples of 500 individuals in New York, Washington, and Louisiana for a total of 1,500 participants in each wave of the study. Six waves of data collection will be completed at monthly intervals in order to capture changing risk perceptions and behavioral responses as the pandemic and pandemic response continue to evolve. This study will improve the research and health community’s understanding of how people perceive risks, particularly when the threat itself is not visible.
Murat Kantarcioglu, University of Texas at Dallas: RAPID: Collaborative: A Privacy Risk Assessment Framework for Person-Level Data Sharing During Pandemics. Funded by NSF Computer and Information Science and Engineering / Computer and Network Systems.
- To enable timely, useful and privacy-preserving releases of patient specific COVID-19 data, this project aims to develop and disseminate novel privacy-risk assessment techniques, implemented in working software, to assist data managers, as well as public health officials, to reason about the tradeoffs between privacy risks (with a focus on re-identification, according to current law) and public data utility. The project will provide the best practices and tools needed for sharing patient-specific data about individuals diagnosed with, or suspected of, COVID-19.
Ali Rahnavard, George Washington University: RAPID: A novel platform for data integration and deep learning on COVID-19. Funded by NSF Biological Sciences / Environmental Biology.
- In order to diminish both the short-term and long-term impacts of COVID-19, it is essential to develop robust, repeatable, and accessible tools to integrate and analyze the diversity of data becoming available in the face of the COVID-19 pandemic. This deep learning bioinformatics platform will allow the prioritization of genes associated with outcome predictors, including health, therapeutic, and vaccine outcomes, as well as inform improved DNA tests for predicting disease status and severity.
Jeff Grann, Credential Engine: RAPID: Increasing Healthcare Credential Open Data in Response to COVID-19. Funded by NSF Office of the Director / Office of Integrative Activities.
- The project aims to serve the national need of making more healthcare credentials and competencies publicly available to increase the nation’s capacity to fill jobs in the healthcare sector due to Coronavirus Disease 2019 (COVID-19). Leveraging partnerships with state, national healthcare, and other associations and accreditors will aid in the collection, organization, and analysis of healthcare credentials and competencies.
Praveen Rao, University of Missouri-Columbia: RAPID: Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab. Funded by NSF Computer and Information Science and Engineering / Computer and Network Systems.
- Human genetic information may hold the answers for COVID-19 drug discovery. With the ability to sequence the human genome at low cost, this project aims to democratize genome sequence (GS) analysis on CloudLab (an NSF-funded cloud computing research infrastructure) for accelerating the process of finding a cure for COVID-19. As a result, any researcher will be able to study differences in the genetic information of individuals affected by COVID-19 using CloudLab at no charge.