81 research outputs found
Modeling the Impact of Nonpharmaceutical Interventions on COVID-19 Transmission in K-12 Schools
Background. The novel coronavirus SARS-CoV-2 spread across the world causing many waves of COVID-19. Children were at high risk of being exposed to the disease because they were not eligible for vaccination during the first 20 mo of the pandemic in the United States. While children 5 y and older are now eligible to receive a COVID-19 vaccine in the United States, vaccination rates remain low despite most schools returning to in-person instruction. Nonpharmaceutical interventions (NPIs) are important for controlling the spread of COVID-19 in K-12 schools. US school districts used varied and layered mitigation strategies during the pandemic. The goal of this article is to analyze the impact of different NPIs on COVID-19 transmission within K-12 schools. Methods. We developed a deterministic stratified SEIR model that captures the role of social contacts between cohorts in disease transmission to estimate COVID-19 incidence under different NPIs including masks, random screening, contact reduction, school closures, and test-to-stay. We designed contact matrices to simulate the contact patterns between students and teachers within schools. We estimated the proportion of susceptible infected associated with each intervention over 1 semester under the Omicron variant. Results. We find that masks and reducing contacts can greatly reduce new infections among students. Weekly screening tests also have a positive impact on disease mitigation. While self-quarantining symptomatic infections and school closures are effective measures for decreasing semester-end infections, they increase absenteeism. Conclusion. The model provides a useful tool for evaluating the impact of a variety of NPIs on disease transmission in K-12 schools. While the model is tested under Omicron variant parameters in US K-12 schools, it can be adapted to study other populations under different disease settings.HighlightsA stratified SEIR model was developed that captures the role of social contacts in K-12 schools to estimate COVID-19 transmission under different nonpharmaceutical interventions.While masks, random screening, contact reduction, school closures, and test-to-stay are all beneficial interventions, masks and contact reduction resulted in the greatest reduction in new infections among students from the tested scenarios.Layered interventions provide more benefits than implementing interventions independently
On the verge of Umdeutung in Minnesota: Van Vleck and the correspondence principle (Part One)
In October 1924, the Physical Review, a relatively minor journal at the time,
published a remarkable two-part paper by John H. Van Vleck, working in virtual
isolation at the University of Minnesota. Van Vleck combined advanced
techniques of classical mechanics with Bohr's correspondence principle and
Einstein's quantum theory of radiation to find quantum analogues of classical
expressions for the emission, absorption, and dispersion of radiation. For
modern readers Van Vleck's paper is much easier to follow than the famous paper
by Kramers and Heisenberg on dispersion theory, which covers similar terrain
and is widely credited to have led directly to Heisenberg's "Umdeutung" paper.
This makes Van Vleck's paper extremely valuable for the reconstruction of the
genesis of matrix mechanics. It also makes it tempting to ask why Van Vleck did
not take the next step and develop matrix mechanics himself.Comment: 82 page
A consensus guide to capturing the ability to inhibit actions and impulsive behaviors in the stop-signal task
The first Hubble diagram and cosmological constraints using superluminous supernovae
This paper has gone through internal review by the DES collaboration.
It has Fermilab preprint number 19-115-AE and DES
publication number 13387. We acknowledge support from EU/FP7-
ERC grant 615929. RCN would like to acknowledge support from
STFC grant ST/N000688/1 and the Faculty of Technology at the
University of Portsmouth. LG was funded by the European Union’s
Horizon 2020 Framework Programme under the Marie Skłodowska-
Curie grant agreement no. 839090. This work has been partially
supported by the Spanish grant PGC2018-095317-B-C21 within
the European Funds for Regional Development (FEDER). Funding
for the DES Projects has been provided by the U.S. Department
of Energy, the U.S. National Science Foundation, the Ministry
of Science and Education of Spain, the Science and Technology
Facilities Council of the United Kingdom, the Higher Education
Funding Council for England, the National Center for Supercomputing
Applications at the University of Illinois at Urbana-Champaign,
the Kavli Institute of Cosmological Physics at the University of
Chicago, the Center for Cosmology and Astro-Particle Physics at
the Ohio State University, the Mitchell Institute for Fundamental
Physics and Astronomy at Texas A&M University, Financiadora
de Estudos e Projetos, Fundac¸ ˜ao Carlos Chagas Filho de Amparo
`a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de
Desenvolvimento Cient´ıfico e Tecnol´ogico and the Minist´erio da
Ciˆencia, Tecnologia e Inovac¸ ˜ao, the Deutsche Forschungsgemeinschaft,
and the Collaborating Institutions in the Dark Energy Survey.
The Collaborating Institutions are Argonne National Laboratory, the
University of California at Santa Cruz, the University of Cambridge,
Centro de Investigaciones Energ´eticas, Medioambientales y Tecnol
´ogicas-Madrid, the University of Chicago, University College
London, the DES-Brazil Consortium, the University of Edinburgh,
the Eidgen¨ossische Technische Hochschule (ETH) Z¨urich, Fermi
NationalAccelerator Laboratory, theUniversity of Illinois atUrbana-
Champaign, the Institut de Ci`encies de l’Espai (IEEC/CSIC), the
Institut de F´ısica d’Altes Energies, Lawrence Berkeley National
Laboratory, the Ludwig-Maximilians Universit¨at M¨unchen and the
associated Excellence Cluster Universe, the University of Michigan,
the National Optical Astronomy Observatory, the University of
Nottingham, The Ohio State University, the University of Pennsylvania,
the University of Portsmouth, SLAC National Accelerator
Laboratory, Stanford University, the University of Sussex, Texas
A&M University, and the OzDES Membership Consortium. Based
in part on observations at Cerro Tololo Inter-American Observatory,
National Optical Astronomy Observatory, which is operated by the
Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.
The DES data management system is supported by the
National Science Foundation under grant numbers AST-1138766
and AST-1536171. The DES participants from Spanish institutions
are partially supported by MINECO under grants AYA2015-
71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-
2016-0597, and MDM-2015-0509, some of which include ERDF
funds from the European Union. IFAE is partially funded by the
CERCA program of the Generalitat de Catalunya. Research leading
to these results has received funding from the European Research
Council under the European Union Seventh Framework Programme
(FP7/2007-2013) including ERC grant agreements 240672, 291329,
and 306478.We acknowledge support from the Australian Research
Council Centre of Excellence for All-skyAstrophysics (CAASTRO),
through project number CE110001020, and the Brazilian Instituto
Nacional de Ciˆencia e Tecnologia (INCT) e-Universe (CNPq grant
465376/2014-2).
This paper has been authored by Fermi Research Alliance, LLC
under Contract No.DE-AC02-07CH11359 with theU.S.Department
of Energy, Office of Science, Office of High Energy Physics. The
United States Government retains and the publisher, by accepting
the paper for publication, acknowledges that the United States
Government retains a non-exclusive, paid-up, irrevocable, worldwide
license to publish or reproduce the published form of this paper,
or allow others to do so, for United States Government purposes.We present the first Hubble diagram of superluminous supernovae (SLSNe) out to a redshift of two, together with constraints
on the matter density, M, and the dark energy equation-of-state parameter, w(≡p/ρ). We build a sample of 20 cosmologically
useful SLSNe I based on light curve and spectroscopy quality cuts. We confirm the robustness of the peak–decline SLSN I
standardization relation with a larger data set and improved fitting techniques than previous works. We then solve the SLSN
model based on the above standardization via minimization of the χ2 computed from a covariance matrix that includes statistical
and systematic uncertainties. For a spatially flat cold dark matter ( CDM) cosmological model, we find M = 0.38+0.24
−0.19,
with an rms of 0.27 mag for the residuals of the distance moduli. For a w0waCDM cosmological model, the addition of SLSNe I
to a ‘baseline’ measurement consisting of Planck temperature together with Type Ia supernovae, results in a small improvement
in the constraints of w0 and wa of 4 per cent.We present simulations of future surveys with 868 and 492 SLSNe I (depending on
the configuration used) and show that such a sample can deliver cosmological constraints in a flat CDM model with the same
precision (considering only statistical uncertainties) as current surveys that use Type Ia supernovae, while providing a factor of
2–3 improvement in the precision of the constraints on the time variation of dark energy, w0 and wa. This paper represents the
proof of concept for superluminous supernova cosmology, and demonstrates they can provide an independent test of cosmology
in the high-redshift (z > 1) universe.EU/FP7-ERC grant 615929STFC grant ST/N000688/1Faculty of Technology at the
University of PortsmouthEuropean Union’s
Horizon 2020 Framework Programme under the Marie Skłodowska-
Curie grant agreement no. 839090Spanish grant PGC2018-095317-B-C21 within
the European Funds for Regional Development (FEDER)U.S. Department
of EnergyU.S. National Science FoundationMinistry
of Science and Education of SpainScience and Technology
Facilities Council of the United KingdomHigher Education
Funding Council for EnglandNational Center for Supercomputing
Applications at the University of Illinois at Urbana-Champaign,Kavli Institute of Cosmological Physics at the University of
ChicagoCenter for Cosmology and Astro-Particle Physics at
the Ohio State UniversityMitchell Institute for Fundamental
Physics and Astronomy at Texas A&M University, Financiadora
de Estudos e Projetos, Fundacão Carlos Chagas Filho de Amparo
`a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de
Desenvolvimento Científico e Tecnológico and the Ministério da
Ciencia, Tecnologia e InovacãoDeutsche ForschungsgemeinschaftCollaborating Institutions in the Dark Energy Survey.National Science Foundation under grant numbers AST-1138766
and AST-1536171.T MINECO under grants AYA2015-
71825, ESP2015-66861, FPA2015-68048, SEV-2016-0588, SEV-
2016-0597, and MDM-2015-0509, some of which include ERDF
funds from the European Union.CERCA program of the Generalitat de Catalunya.European Research
Council under the European Union Seventh Framework Programme
(FP7/2007-2013) including ERC grant agreements 240672, 291329,
and 306478.Australian Research
Council Centre of Excellence for All-skyAstrophysics (CAASTRO),
through project number CE110001020Brazilian Instituto
Nacional de Ciˆencia e Tecnologia (INCT) e-Universe (CNPq grant
465376/2014-2)Fermi Research Alliance, LLC
under Contract No.DE-AC02-07CH11359 with theU.S.Department
of Energy, Office of Science, Office of High Energy Physic
First cosmology results using SNe Ia from the dark energy survey: analysis, systematic uncertainties, and validation
International audienceWe present the analysis underpinning the measurement of cosmological parameters from 207 spectroscopically classified type Ia supernovae (SNe Ia) from the first three years of the Dark Energy Survey Supernova Program (DES-SN), spanning a redshift range of 0.01
First cosmology results using type Ia supernovae from the Dark Energy Survey: constraints on cosmological parameters
We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe Ia is based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat LCDM model we find a matter density Omega_m = 0.331 +_ 0.038. For a flat wCDM model, and combining our SN Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state w = -0.978 +_ 0.059, and Omega_m = 0.321 +_ 0.018. For a flat w0waCDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find w0 = -0.885 +_ 0.114 and wa = -0.387 +_ 0.430. These results are in agreement with a cosmological constant and with previous constraints using SNe Ia (Pantheon, JLA)
Potential impact of annual vaccination with reformulated COVID-19 vaccines: Lessons from the US COVID-19 scenario modeling hub
Background AU Coronavirus Disease 2019 (COVID-19) continues to cause :significant hospitalizations and deaths in the United States. Its continued burden and the impact of annually reformulated vaccines remain unclear. Here, we present projections of COVID-19 hospitalizations and deaths in the United States for the next 2 years under 2 plausible assumptions about immune escape (20% per year and 50% per year) and 3 possible CDC recommendations for the use of annually reformulated vaccines (no recommendation, vaccination for those aged 65 years and over, vaccination for all eligible age groups based on FDA approval). Methods and findings The COVID-19 Scenario Modeling Hub solicited projections of COVID-19 hospitalization and deaths between April 15, 2023 and April 15, 2025 under 6 scenarios representing the intersection of considered levels of immune escape and vaccination. Annually reformulated vaccines are assumed to be 65% effective against symptomatic infection with strains circulating on June 15 of each year and to become available on September 1. Age- and state-specific coverage in recommended groups was assumed to match that seen for the first (fall 2021) COVID-19 booster. State and national projections from 8 modeling teams were ensembled to produce projections for each scenario and expected reductions in disease outcomes due to vaccination over the projection period. From April 15, 2023 to April 15, 2025, COVID-19 is projected to cause annual epidemics peaking November to January. In the most pessimistic scenario (high immune escape, no vaccination recommendation), we project 2.1 million (90% projection interval (PI) [1,438,000, 4,270,000]) hospitalizations and 209,000 (90% PI [139,000, 461,000]) deaths, exceeding pre-pandemic mortality of influenza and pneumonia. In high immune escape scenarios, vaccination of those aged 65+ results in 230,000 (95% confidence interval (CI) [104,000, 355,000]) fewer hospitalizations and 33,000 (95% CI [12,000, 54,000]) fewer deaths, while vaccination of all eligible individuals results in 431,000 (95% CI: 264,000–598,000) fewer hospitalizations and 49,000 (95% CI [29,000, 69,000]) fewer deaths. Conclusions COVID-19 is projected to be a significant public health threat over the coming 2 years. Broad vaccination has the potential to substantially reduce the burden of this disease, saving tens of thousands of lives each year
Tracking post-error adaptation in the motor system by transcranial magnetic stimulation
Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty
Our ability to forecast epidemics far into the future is constrained by the many complexities of disease systems. Realistic longer-term projections may, however, be possible under well-defined scenarios that specify the future state of critical epidemic drivers. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make months ahead projections of SARS-CoV-2 burden, totaling nearly 1.8 million national and state-level projections. Here, we find SMH performance varied widely as a function of both scenario validity and model calibration. We show scenarios remained close to reality for 22 weeks on average before the arrival of unanticipated SARS-CoV-2 variants invalidated key assumptions. An ensemble of participating models that preserved variation between models (using the linear opinion pool method) was consistently more reliable than any single model in periods of valid scenario assumptions, while projection interval coverage was near target levels. SMH projections were used to guide pandemic response, illustrating the value of collaborative hubs for longer-term scenario projections
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