University of Bath

About: University of Bath is a education organization based out in Bath, Bath and North East Somerset, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 15830 authors who have published 39608 publications receiving 1358769 citations. The organization is also known as: Bath University.

Weak and strong uniform consistency of kernel regression estimates

Abstract: We study the estimation of a regression function by the kernel method. Under mild conditions on the “window”, the “bandwidth” and the underlying distribution of the bivariate observations {(X i , Y i)}, we obtain the weak and strong uniform convergence rates on a bounded interval. These results parallel those of Silverman (1978) on density estimation and extend those of Schuster and Yakowitz (1979) and Collomb (1979) on regression estimation.

Acceptance and values-based action in chronic pain: A study of treatment effectiveness and process.

Abstract: Developing approaches within cognitive behavioral therapy are increasingly process-oriented and based on a functional and contextual framework that differs from the focus of earlier work. The present study investigated the effectiveness of acceptance and commitment therapy (S. C. Hayes, K. Strosahl, & K. G. Wilson, 1999) in the treatment of chronic pain and also examined 2 processes from this model, acceptance and values-based action. Participants included 171 completers of an interdisciplinary treatment program, 66.7% of whom completed a 3-month follow-up assessment as well. Results indicated significant improvements for pain, depression, pain-related anxiety, disability, medical visits, work status, and physical performance. Effect size statistics were uniformly medium or larger. According to reliable change analyses, 75.4% of patients demonstrated improvement in at least one key domain. Both acceptance of pain and values-based action improved, and increases in these processes were associated with improvements in the primary outcome domains.

Forecasting the novel coronavirus COVID-19.

Abstract: What will be the global impact of the novel coronavirus (COVID-19)? Answering this question requires accurate forecasting the spread of confirmed cases as well as analysis of the number of deaths and recoveries. Forecasting, however, requires ample historical data. At the same time, no prediction is certain as the future rarely repeats itself in the same way as the past. Moreover, forecasts are influenced by the reliability of the data, vested interests, and what variables are being predicted. Also, psychological factors play a significant role in how people perceive and react to the danger from the disease and the fear that it may affect them personally. This paper introduces an objective approach to predicting the continuation of the COVID-19 using a simple, but powerful method to do so. Assuming that the data used is reliable and that the future will continue to follow the past pattern of the disease, our forecasts suggest a continuing increase in the confirmed COVID-19 cases with sizable associated uncertainty. The risks are far from symmetric as underestimating its spread like a pandemic and not doing enough to contain it is much more severe than overspending and being over careful when it will not be needed. This paper describes the timeline of a live forecasting exercise with massive potential implications for planning and decision making and provides objective forecasts for the confirmed cases of COVID-19.

Microreact: visualizing and sharing data for genomic epidemiology and phylogeography.

Abstract: Visualization is frequently used to aid our interpretation of complex datasets. Within microbial genomics, visualizing the relationships between multiple genomes as a tree provides a framework onto which associated data (geographical, temporal, phenotypic and epidemiological) are added to generate hypotheses and to explore the dynamics of the system under investigation. Selected static images are then used within publications to highlight the key findings to a wider audience. However, these images are a very inadequate way of exploring and interpreting the richness of the data. There is, therefore, a need for flexible, interactive software that presents the population genomic outputs and associated data in a user-friendly manner for a wide range of end users, from trained bioinformaticians to front-line epidemiologists and health workers. Here, we present Microreact, a web application for the easy visualization of datasets consisting of any combination of trees, geographical, temporal and associated metadata. Data files can be uploaded to Microreact directly via the web browser or by linking to their location (e.g. from Google Drive/Dropbox or via API), and an integrated visualization via trees, maps, timelines and tables provides interactive querying of the data. The visualization can be shared as a permanent web link among collaborators, or embedded within publications to enable readers to explore and download the data. Microreact can act as an end point for any tool or bioinformatic pipeline that ultimately generates a tree, and provides a simple, yet powerful, visualization method that will aid research and discovery and the open sharing of datasets.