Learning as We Go: An Examination of the Statistical Accuracy of COVID19 Daily Death Count Predictions

This paper provides a formal evaluation of the predictive performance of a model (and its various updates) developed by the Institute for Health Metrics and Evaluation (IHME) for predicting daily deaths attributed to COVID19 for each state in the United States. The IHME models have received extensive attention in social and mass media, and have influenced policy makers at the highest levels of the United States government. For effective policy making the accurate assessment of uncertainty, as well as accurate point predictions, are necessary because the risks inherent in a decision must be taken into account, especially in the present setting of a novel disease affecting millions of lives. To assess the accuracy of the IHME models, we examine both forecast accuracy as well as the predictive performance of the 95% prediction intervals provided by the IHME models. We find that the initial IHME model underestimates the uncertainty surrounding the number of daily deaths substantially. Specifically, the true number of next day deaths fell outside the IHME prediction intervals as much as 70% of the time, in comparison to the expected value of 5%. In addition, we note that the performance of the initial model does not improve with shorter forecast horizons. Regarding the updated models, our analyses indicate that the later models do not show any improvement in the accuracy of the point estimate predictions. In fact, there is some evidence that this accuracy has actually decreased over the initial models. Moreover, when considering the updated models, while we observe a larger percentage of states having actual values lying inside the 95% prediction intervals (PI), our analysis suggests that this observation may be attributed to the widening of the PIs. The width of these intervals calls into question the usefulness of the predictions to drive policy making and resource allocation

European household waste management schemes: Their effectiveness and applicability in England.

This paper reviews European household waste management schemes and provides an insight into their effectiveness in reducing or diverting household waste. The paper also considers the feasibility of replicating such schemes in England. Selected case studies include those implemented using variable charging schemes, direct regulation and household incentivisation (reduced disposal charges). A total of 15 case studies were selected from developed countries in the EU where some schemes have operated for more than a decade. Criteria for assessing the effectiveness and replicability of schemes were developed using scheme progress towards targets, response time, compatibility with government policy, ease of administration and operation, and public acceptance as attributes. The study demonstrates the capability of these schemes to significantly reduce household waste and suggests changes to allow their possible adoption in England. One of the main barriers to their adoption is the Environmental Protection Act, 1990 that prevents English local authorities (LAs) from implementing the variable charging method for household waste management. This barrier could be removed through a change in legislation. The need to derive consistent data and standardise the method of measuring the effectiveness of schemes is also highlighted

The neuroanatomical correlates of repetitive negative thinking: A systematic review

Repetitive negative thinking (RNT) is a cognitive process characterised by intrusive, repetitive, and difficult-to-disengage-from negative thoughts. Heightened RNT levels are prevalent across clinical disorders and have been associated with ill-health (e.g. cardiovascular disease), even at lower, non-clinical levels. Identifying the neuroanatomical correlates of RNT could help characterise structural alterations that transcend diagnostic boundaries and further understanding of the pathogenesis of clinical disorders. We therefore conducted a systematic review to investigate associations between RNT and brain morphology. Following title/abstract and full-text screening, 24 studies were included. We found evidence that RNT severity is associated with grey and white matter volumes/microstructure, particularly in the dorsolateral prefrontal cortex, anterior cingulate cortex and superior longitudinal fasciculus, regions heavily implicated in cognitive control, and emotional processing and regulation. However, inconsistent associations, potentially due to the heterogeneity of included studies (e.g. methodological differences, type of RNT assessed), preclude specific conclusions being reached regarding any one region's association with RNT. Further, given the defuse nature of thoughts, it may be that RNT is associated with distributed brain regions operating within large-scale networks, rather than with a single structure. High quality longitudinal studies, investigating structural networks, are required to confirm the neuroanatomical basis of RNT and elucidate the direction of relationships

Luminous supernovae associated with ultra-long gamma-ray bursts from hydrogen-free progenitors extended by pulsational pair-instability

We show that the luminous supernovae (SNe) associated with ultra-long gamma-ray bursts (GRBs) can be related to the slow cooling from the explosions of hydrogen-free progenitors extended by pulsational pair-instability. In the accompanying paper (Marchant & Moriya 2020), we have shown that some rapidly-rotating hydrogen-free GRB progenitors that experience pulsational pair-instability can keep an extended structure caused by pulsational pair-instability until the core collapse. Such progenitors have large radii exceeding 10 Rsun and they sometimes reach beyond 1000 Rsun at the time of the core collapse. They are, therefore, promising progenitors of ultra-long GRBs. We here perform the light-curve modeling of the explosions of one extended hydrogen-free progenitor with a radius of 1962 Rsun. Thanks to the large progenitor radius, the ejecta experience slow cooling after the shock breakout and they become rapidly evolving (~ 1e43 erg/s) SNe in optical even without the energy input from the 56Ni nuclear decay when the explosion energy is more than 1e52 erg. The 56Ni decay energy input can affect the light curves after the optical light-curve peak and make the light-curve decay slow when the 56Ni mass is around 1 Msun. They also have fast photospheric velocity above 10,000 km/s and hot photospheric temperature above 10,000 K at around the peak luminosity. We find that the rapid rise and luminous peak found in the optical light curve of SN 2011kl, which is associated with the ultra-long GRB 111209A, can be explained as the cooling phase of the extended progenitor. The ultra-long GRB progenitors proposed in Marchant & Moriya (2020) can explain both the ultra-long GRB duration and the accompanying SN properties. When the GRB jet is off-axis or choked, the luminous SNe could be observed as fast blue optical transients without accompanying GRBs. (abridged)Comment: 5 pages, 5 figures, accepted by Astronomy & Astrophysics Letter

Mid-Miocene cooling and the extinction of tundra in continental Antarctica

A major obstacle in understanding the evolution of Cenozoic climate has been the lack of well dated terrestrial evidence from high-latitude, glaciated regions. Here, we report the discovery of exceptionally well preserved fossils of lacustrine and terrestrial organisms from the McMurdo Dry Valleys sector of the Transantarctic Mountains for which we have established a precise radiometric chronology. The fossils, which include diatoms, palynomorphs, mosses, ostracodes, and insects, represent the last vestige of a tundra community that inhabited the mountains before stepped cooling that first brought a full polar climate to Antarctica. Paleoecological analyses, 40Ar/39Ar analyses of associated ash fall, and climate inferences from glaciological modeling together suggest that mean summer temperatures in the region cooled by at least 8°C between 14.07 ± 0.05 Ma and 13.85 ± 0.03 Ma. These results provide novel constraints for the timing and amplitude of middle-Miocene cooling in Antarctica and reveal the ecological legacy of this global climate transition

Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model

Improving air quality is an important environmental challenge of our time. Chile currently has one of the most stable and emerging economies in Latin America, where human impact on natural resources and air quality does not go unperceived. Santiago, the capital of Chile, is one of the cities in which particulate matter (PM) levels exceed national and international limits. Its location and climate cause critical conditions for human health when interaction with anthropogenic emissions is present. In this paper, we propose a predictive model based on bivariate regression to estimate PM levels, related to PM2.5 and PM10, simultaneously. Birnbaum-Saunders distributions are used in the joint modeling of real-world PM2.5 and PM10 data by considering as covariates some relevant meteorological variables employed in similar studies. The Mahalanobis distance is utilized to assess bivariate outliers and to detect suitability of the distributional assumption. In addition, we use the local influence technique for analyzing the impact of a perturbation on the overall estimation of model parameters. In the predictions, we check the categorization for the observed and predicted cases of the model according to the primary air quality regulations for PM