Seasonal variation in SARS-CoV-2 transmission in temperate climates: a Bayesian modelling study in 143 European regions

Although seasonal variation has a known influence on the transmission of several respiratory viral infections, its role in SARS-CoV-2 transmission remains unclear. While there is a sizable and growing literature on environmental drivers of COVID-19 transmission, recent reviews have highlighted conflicting and inconclusive findings. This indeterminacy partly owes to the fact that seasonal variation relates to viral transmission by a complicated web of causal pathways, including many interacting biological and behavioural factors. Since analyses of specific factors cannot determine the aggregate strength of seasonal forcing, we sidestep the challenge of disentangling various possible causal paths in favor of a holistic approach. We model seasonality as a sinusoidal variation in transmission and infer a single Bayesian estimate of the overall seasonal effect. By extending two state-of-the-art models of non-pharmaceutical intervention (NPI) effects and their datasets covering 143 regions in temperate Europe, we are able to adjust our estimates for the role of both NPIs and mobility patterns in reducing transmission. We find strong seasonal patterns, consistent with a reduction in the time-varying reproduction number R(t) (the expected number of new infections generated by an infectious individual at time t) of 42.1% (95% CI: 24.7%—53.4%) from the peak of winter to the peak of summer. These results imply that the seasonality of SARSCoV-2 transmission is comparable in magnitude to the most effective individual NPIs but less than the combined effect of multiple interventions

100,000 lumens to treat seasonal affective disorder: a proof of concept RCT of Bright, whole-ROom, All-Day (BROAD) light therapy

Background: Seasonal affective disorder (SAD) is common and debilitating. The standard of care includes light therapy provided by a light box; however, this treatment is restrictive and only moderately effective. Advances in LED technology enable lighting solutions that emit vastly more light than traditional light boxes. Here, we assess the feasibility of BROAD (Bright, whole-ROom, All-Day) light therapy and get a first estimate for its potential effectiveness. Methods: Patients were randomly assigned to a treatment for 4 weeks; either a very brightly illuminated room in their home for at least 6 h per day (BROAD light therapy) or 30 min in front of a standard 10,000 lux SAD light box. Feasibility was assessed by monitoring recruitment, adherence, and side effects. SAD symptoms were measured at baseline and after 2 and 4 weeks, with the Hamilton Depression Rating Scale-Seasonal Affective Disorders 29-items, self-report version. Results: All 62 patients who started treatment were available at 4-week follow-up and no significant adverse effects were reported. SAD symptoms of both groups improved similarly and considerably, in line with previous results. Exploratory analyses indicate that a higher illuminance (lux) is associated with a larger symptom improvement in the BROAD light therapy group. Conclusions: BROAD light therapy is feasible and seems similarly effective as the standard of care while not confining the participants to 30 min in front of a light box. In follow-up trials, BROAD light therapy could be modified for increased illuminance, which would likely improve its effectiveness

Prioritized training on points that are learnable, worth learning, and not yet learned

We introduce Goldilocks Selection, a technique for faster model training which selects a sequence of training points that are "just right". We propose an information-theoretic acquisition function -- the reducible validation loss -- and compute it with a small proxy model -- GoldiProx -- to efficiently choose training points that maximize information about a validation set. We show that the "hard" (e.g. high loss) points usually selected in the optimization literature are typically noisy, while the "easy" (e.g. low noise) samples often prioritized for curriculum learning confer less information. Further, points with uncertain labels, typically targeted by active learning, tend to be less relevant to the task. In contrast, Goldilocks Selection chooses points that are "just right" and empirically outperforms the above approaches. Moreover, the selected sequence can transfer to other architectures; practitioners can share and reuse it without the need to recreate it

All-cause versus cause-specific excess deaths for estimating influenza-associated mortality in Denmark, Spain, and the United States

Background: Seasonal influenza-associated excess mortality estimates can be timely and provide useful information on the severity of an epidemic. This methodology can be leveraged during an emergency response or pandemic. Method: For Denmark, Spain, and the United States, we estimated age-stratified excess mortality for (i) all-cause, (ii) respiratory and circulatory, (iii) circulatory, (iv) respiratory, and (v) pneumonia, and influenza causes of death for the 2015/2016 and 2016/2017 influenza seasons. We quantified differences between the countries and seasonal excess mortality estimates and the death categories. We used a time-series linear regression model accounting for time and seasonal trends using mortality data from 2010 through 2017. Results: The respective periods of weekly excess mortality for all-cause and cause-specific deaths were similar in their chronological patterns. Seasonal all-cause excess mortality rates for the 2015/2016 and 2016/2017 influenza seasons were 4.7 (3.3-6.1) and 14.3 (13.0-15.6) per 100,000 population, for the United States; 20.3 (15.8-25.0) and 24.0 (19.3-28.7) per 100,000 population for Denmark; and 22.9 (18.9-26.9) and 52.9 (49.1-56.8) per 100,000 population for Spain. Seasonal respiratory and circulatory excess mortality estimates were two to three times lower than the all-cause estimates. Discussion: We observed fewer influenza-associated deaths when we examined cause-specific death categories compared with all-cause deaths and observed the same trends in peaks in deaths with all death causes. Because all-cause deaths are more available, these models can be used to monitor virus activity in near real time. This approach may contribute to the development of timely mortality monitoring systems during public health emergencies.This study was conducted as part of Sebastian Schmidt's research fellowship, which was financially supported by the Novo Nordic Foundation and A.P. Møller Fonden. The EuroMOMO network has received financial support from the European Centre for Disease Prevention and Control (ECDC) and from the World Health Organization (WHO) Regional Office for Europe.S

Extensional and Intensional Strategies

This paper is a contribution to the theoretical foundations of strategies. We first present a general definition of abstract strategies which is extensional in the sense that a strategy is defined explicitly as a set of derivations of an abstract reduction system. We then move to a more intensional definition supporting the abstract view but more operational in the sense that it describes a means for determining such a set. We characterize the class of extensional strategies that can be defined intensionally. We also give some hints towards a logical characterization of intensional strategies and propose a few challenging perspectives

Mask wearing in community settings reduces SARS-CoV-2 transmission

The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al., Lancet 395, 1973–1987 (2020); J. Howard et al., Proc. Natl. Acad. Sci. U.S.A. 118, e2014564118 (2021); Y. Cheng et al., Science eabg6296 (2021)], studies in community settings report inconsistent results [H. M. Ollila et al., medRxiv (2020); J. Brainard et al., Eurosurveillance 25, 2000725 (2020); T. Jefferson et al., Cochrane Database Syst. Rev. 11, CD006207 (2020)]. Most such studies focus on how masks impact transmission, by analyzing how effective government mask mandates are. However, we find that widespread voluntary mask wearing, and other data limitations, make mandate effectiveness a poor proxy for mask-wearing effectiveness. We directly analyze the effect of mask wearing on SARS-CoV-2 transmission, drawing on several datasets covering 92 regions on six continents, including the largest survey of wearing behavior ([Formula: see text] 20 million) [F. Kreuter et al., https://gisumd.github.io/COVID-19-API-Documentation (2020)]. Using a Bayesian hierarchical model, we estimate the effect of mask wearing on transmission, by linking reported wearing levels to reported cases in each region, while adjusting for mobility and nonpharmaceutical interventions (NPIs), such as bans on large gatherings. Our estimates imply that the mean observed level of mask wearing corresponds to a 19% decrease in the reproduction number R. We also assess the robustness of our results in 60 tests spanning 20 sensitivity analyses. In light of these results, policy makers can effectively reduce transmission by intervening to increase mask wearing

A quasi classical approach to electron impact ionization

A quasi classical approximation to quantum mechanical scattering in the Moeller formalism is developed. While keeping the numerical advantage of a standard Classical--Trajectory--Monte--Carlo calculation, our approach is no longer restricted to use stationary initial distributions. This allows one to improve the results by using better suited initial phase space distributions than the microcanonical one and to gain insight into the collision mechanism by studying the influence of different initial distributions on the cross section. A comprehensive account of results for single, double and triple differential cross sections for atomic hydrogen will be given, in comparison with experiment and other theories.Comment: 21 pages, 10 figures, submitted to J Phys

Establishing a persistent interoperability test-bed for European geospatial research

The development of standards for geospatial web services has been spearheaded by the Open Geospatial Consortium (OGC) - a group of over 370 private, public and academic organisations (OGC, 1999-2009). The OGC aims to facilitate interoperability between geospatial technologies through education, standards and other initiatives. The OGC Service Architecture, described in the international standard ISO 19119, offers an abstract specification for web services covering data dissemination, processing, portrayal, workflows and other areas. The development of specifications covering each of these categories of web services has led to a significant number of geospatial data and computational services available on the World Wide Web (the Web). A project1 to establish a persistent geospatial interoperability test-bed (PTB) was commissioned in 2007 by the Association of Geographic Information Laboratories in Europe (AGILE), Commission 5 (Networks) of the European Spatial Data Research (EuroSDR) organisation and the OGC

Acumen : an open-source testbed for cyber-physical systems research

Developing Cyber-Physical Systems requires methods and tools to support simulation and verification of hybrid (both continuous and discrete) models. The Acumen modeling and simulation language is an open source testbed for exploring the design space of what rigorousbut- practical next-generation tools can deliver to developers of Cyber- Physical Systems. Like verification tools, a design goal for Acumen is to provide rigorous results. Like simulation tools, it aims to be intuitive, practical, and scalable. However, it is far from evident whether these two goals can be achieved simultaneously. This paper explains the primary design goals for Acumen, the core challenges that must be addressed in order to achieve these goals, the “agile research method” taken by the project, the steps taken to realize these goals, the key lessons learned, and the emerging language design