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FollowCOVID-19 Strategic Collaborations
NERSC is partnering with a number research teams to advance COVID-19 related research. These collaborations extend some of the existing partnerships and relationships NERSC has at Berkeley Lab and throughout the scientific community at large.
Health and Human Services (HHS) Epidemiology Studies
This team is using GCM, an agent-based epidemiological model being used by FEMA, to inform policymakers about the number of COVID-19 cases that are projected to occur in various regions, the need for medical resources, and to understand the impact of social distancing and other interventions. Read More »
Viral Fate & Transport for COVID-19
Potentially infectious sites are created when COVID-19 carrying aerosol particles are released as a person talks/sings/coughs/sneezes/breathes and the virus is deposited on the ground and other surfaces. This team is studying how this process occurs in a model classroom setting.
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Computer-Aided Design of Peptide Ligands to SARS-CoV-2 Targets
Researchers from the Beckman Institute at the City of Hope are designing peptides and small molecules that will bind to the coronavirus surface proteins and inhibit their binding to human proteins. Besides potential therapy, these inhibitory peptides and molecules are useful for understanding the mechanisms of virus entry and interaction with the human host and the immune system. Read More »
Health and Human Services (HHS) Epidemiology Studies
This team is using GCM, an agent-based epidemiological model being used by FEMA, to inform policymakers about the number of COVID-19 cases that are projected to occur in various regions, the need for medical resources, and to understand the impact of social distancing and other interventions. Read More »
Large Scale Docking for COVID-19
University of California, San Francisco researchers are building 3D databases of commercially available molecules and dock them into the binding site of COVID-19 target proteins. Read More »
Machine Learning to model the full space of chemical biology and drug discovery with quantum-mechanical accuracy
This project is using their new machine learning (ML) model to screen drug molecules for an affinity with protein targets associated with COVID-19 infection. The goal is to span the full space of chemical biology and drug discovery with quantum-mechanical accuracy at much reduced computational cost.
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Accelerating COVID-19 Triage and Screening
Berkeley Lab researcher Dani Ushizima and her team are working to amplify COVID-19 testing and surveillance by exploring a diverse set of data, including patient vitals, with suspected COVID-19 infection Read More »
Ancestral Recombination of SARS-CoV-2
This team is exploring the ancestral recombination and evolutionary origins of SARS-CoV-2 using Machine Learning/AI computational techniques. They are using existing tools (e.g., ARGweaver, ClonalOrigin, InferRho) to examine SARS-CoV-2 and relevant outgroup genomes for evidence of recombination. Read More »
Viral Fate & Transport for COVID-19
Potentially infectious sites are created when COVID-19 carrying aerosol particles are released as a person talks/sings/coughs/sneezes/breathes and the virus is deposited on the ground and other surfaces. This team is studying how this process occurs in a model classroom setting.
Read More »
SARS-CoV-2 Detection in Sequence Read Archive Data
This project is looking for signatures of SARS-CoV-2 in a large repository of genomics data to better track the temporal and spatial spread of the virus. The past six months of data are being scanned to better understand the background levels in the environment. Read More »
Uncertainty Quantification for Forecasting COVID-19 Dynamics
A research team led by the University of California, San Diego, is developing a fast and accurate epidemic modeling tool that can automatically learn from data collected from online platforms and global simulations, and make real-time predictions. Read More »
Analyzing COVID-19 Risk Patterns using Mobility Data
Researchers at the University of West Florida and Berkeley Lab are analyzing mobility data collected from location based services and as a result to understand the disease risk patterns.
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