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Machine learning-based analysis of COVID-19 pandemic impact on US research networks
Here in this study we explore how fallout from the changing public health policy around COVID-19 has changed how researchers access and process their science experiments. Using a combination of techniques from statistical analysis and machine learning, we conduct a retrospective analysis of historical network data for a period around the stay-at-home orders that took place in March 2020. Our analysis takes data from the entire ESnet infrastructure to explore DOE high-performance computing (HPC) resources at OLCF, ALCF, and NERSC, as well as User sites such as PNNL and JLAB. We look at detecting and quantifying changes in site activity using a combination of t-Distributed Stochastic Neighbor Embedding (t-SNE) and decision tree analysis. Our findings bring insights into the working patterns and impact on data volume movements, particularly during late-night hours and weekends.
DIMPLES: Distributed Influence Maximization for Pandemic pLanning on Exascale Systems
We study exascale parallel algorithms for the selection of intervention or monitoring strategies in massive realistic socio-technical networks through scalable Influence Maximization (InfMax) algorithms. We employ novel techniques to enable efficient scaling on up to 8k nodes of OLCF Frontier, with 65k AMD GPUs and 458k AMD CPU cores. Current state-of-the-art InfMax tools are limited to networks with only a few million actors (vertices) and a few hundred million interactions (edges). By overcoming these limitations, we show that our approach is capable of processing a realistic social contact network of the United States with 285 million nodes and about 8 billion edges. This two orders-of-magnitude improvement over the previous state-of-the-art is obtained by leveraging algorithmic advancements for the InfMax problem and designing several problem-specific approaches to overlap communication with computation, improve GPU efficiency, and lower the application’s memory requirements. We evaluate strong scaling for computing 10k most influential seeds using up to 8k nodes of an exascale system, and weak scaling from 128 to 8k system nodes for seed sets ranging from 625 to 40k seeds. We achieve the fastest-known runtime of 25 minutes while performing 48 million diffusion simulations totaling 2.31 petabytes to identify 40k influential seeds using 8k nodes, and take 5.75 minutes to identify 10k seeds while using 4k nodes.
COVID-19 global pandemic planning: Performance and electret charge of N95 respirators after recommended decontamination methods
Shortages of N95 respirators for use by medical personnel have driven consideration of novel conservation strategies, including decontamination for reuse and extended use. Decontamination methods listed as promising by the Centers for Disease Control and Prevention (CDC) (vaporous hydrogen peroxide (VHP), wet heat, ultraviolet irradiation (UVI)) and several methods considered for low resource environments (bleach, isopropyl alcohol and detergent/soap) were studied for two commonly used surgical N95 respirators (3M™ 1860 and 1870+ Aura™). Although N95 filtration performance depends on the electrostatically charged electret filtration layer, the impact of decontamination on this layer is largely unexplored. As such, respirator performance following decontamination was assessed based on the fit, filtration efficiency, and pressure drop, along with the relationship between (1) surface charge of the electret layer, and (2) elastic properties of the straps. Decontamination with VHP, wet heat, UVI, and bleach did not degrade fit and filtration performance or electret charge. Isopropyl alcohol and soap significantly degraded fit, filtration performance, and electret charge. Pressure drop across the respirators was unchanged. Modest degradation of N95 strap elasticity was observed in mechanical fatigue testing, a model for repeated donnings and doffings. CDC recommended decontamination methods including VHP, wet heat, and UV light did not degrade N95 respirator fit or filtration performance in these tests. Extended use of N95 respirators may degrade strap elasticity, but a loss of face seal integrity should be apparent during user seal checks. NIOSH recommends performing user seal checks after every donning to detect loss of appropriate fit. Decontamination methods which degrade electret charge such as alcohols or detergents should not be used on N95 respirators. The loss of N95 performance due to electret degradation would not be apparent to a respirator user or evident during a negative pressure user seal check.
Impact of COVID-19 on materials science research innovation and related pandemic response
The scope of impact that the coronavirus SARS-CoV-2 has had and continues to have on life, society, and the world as we know it will be debated for years to come. One thing is for certain, scientists, engineers, clinicians, and researchers around the globe rallied to heed the call for innovation, particularly in the field of materials science. Here, we feature six articles, two of which showcase primary consumable materials research and development, along with four review articles highlighting materials innovation over the last 18 months in diagnostics, prevention, and treatment of SARS-CoV-2 infection.
Coronaviruses: Epidemics and Pandemics
Coronaviruses constitute a large group of viruses that circulate in domestic and wild mammals and birds (human, cat, dog, pig, mouse, rat, bat, cattle, deer, giraffe, llama, sheep, elk, caribou, and birds). Seven of them have made their way into humans. This presentation gives an overview of three coronavirus epidemics with an emphasis on Severe Acute Respiratory Syndrome Coronavirus-2 (SARS CoV-2) which causes coronavirus disease -2019 (COVID-19).
Coordination of Emergency Management, Biosafety, Occupational Health Personnel, and Senior Management during the COVID-19 Pandemic at Los Alamos National Laboratory [Slides]
The presentation was intended for the EMI SIG Annual Meeting Subcommittee on Technical Analysis and Response Support in August 2020 and included strategies and policies regarding essential workers and returning to work.
Python in 3 parts - A pandemic-adapted professional development workshop [Slides]
In this professional development workshop for teachers in New Mexico, the goals are to provide practical hands-on work in Python and create a deep awareness of how programming and Python fit in what we do.
NVBL (National Virtual Biotechnology Laboratory): Pandemic Monitoring and Modeling
This virtual symposium was held to highlight the impact the U.S. Department of Energy’s (DOE) National Virtual Biotechnology Laboratory (NVBL) has had utilizing the unique capabilities of the DOE to tackle the science and technology challenges associated with COVID-19, and to discuss areas in which the NVBL can have impact in the future. With perspectives from Chris Fall, Director, Office of Science, DOE, and William A. Bookless, Principal Deputy Administrator of the National Nuclear Security Administration, the event featured Presentations from lead investigators reporting progress in: Epidemiological modeling, Therapeutics, Testing, Understanding transport of the virus, and Solving issues around supply chain challenges. In addition, keynote speakers from outside the NVBL discussed the upcoming science and technology needs in computing, testing and surveillance, and vaccines. The event was aimed at the S&T community, media, and general public.
Computing and AI for Pandemic Response: Looking Forward
This virtual symposium was held to highlight the impact the U.S. Department of Energy’s (DOE) National Virtual Biotechnology Laboratory (NVBL) has had utilizing the unique capabilities of the DOE to tackle the science and technology challenges associated with COVID-19, and to discuss areas in which the NVBL can have impact in the future. With perspectives from Chris Fall, Director, Office of Science, DOE, and William A. Bookless, Principal Deputy Administrator of the National Nuclear Security Administration, the event featured Presentations from lead investigators reporting progress in: Epidemiological modeling, Therapeutics, Testing, Understanding transport of the virus, and Solving issues around supply chain challenges. In addition, keynote speakers from outside the NVBL discussed the upcoming science and technology needs in computing, testing and surveillance, and vaccines. The event was aimed at the S&T community, media, and general public.
Final CTAP Report: Technical Assistance to Pandemic Response Team with Pueblo of Zuni
The Statement of Work, Deliverables and Costs for the project are provided.
The five stages of IT grief: Sandia IT's response to the global pandemic.
Abstract not provided.
Trends in infrasound and low-audio range acoustic power resulting from the COVID-19 pandemic.
Abstract not provided.
Changes in ambient urban infrasound during the COVID-19 pandemic.
Abstract not provided.
Provide Radiological Worker and Radiological Control Technician Training During the Pandemic.
Abstract not provided.
Territorial Government Revenue Vulnerability Index (TGRVI): Measuring Financial Impacts to Territorial Governments during the COVID-19 Pandemic
The Territorial Government Revenue Vulnerability Index (TGRVI) measures the vulnerability of U.S. territorial government revenues by estimating monthly changes relative to a January 2020 baseline. Revenues accounted for in the index include: taxes on products and sales, transportation and housing revenues, individual income taxes, severance taxes and royalties, and property taxes.
Applying Lessons Learned From the COVID‐19 Global Pandemic to Increase Radioactive Source Security System Resilience.
Abstract not provided.