scholarly journals Hey everybody, I'm Tom Smith from the Office for National Statistics’ Data Science Campus. We’re using data to help the UK improve people’s lives. Ask Me Anything!

The Winnower ◽  
2020 ◽  
Author(s):  
ONS_UK ◽  
r/Science
Keyword(s):  
Data Science ◽  
National Statistics ◽  
Using Data ◽  
The Uk

Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Haider Ilyas ◽  
Ahmed Anwar ◽  
Ussama Yaqub ◽  
Zamil Alzamil ◽  
Deniz Appelbaum
Keyword(s):  
Data Science ◽  
Supervised Machine Learning ◽  
Content Type ◽  
The Public ◽  
Public Sentiment ◽  
The People ◽  
The Usa ◽  
Using Data ◽  
The Uk

Purpose This paper aims to understand, examine and interpret the main concerns and emotions of the people regarding COVID-19 pandemic in the UK, the USA and India using Data Science measures. Design/methodology/approach This study implements unsupervised and supervised machine learning methods, i.e. topic modeling and sentiment analysis on Twitter data for extracting the topics of discussion and calculating public sentiment. Findings Governments and policymakers remained the focus of public discussion on Twitter during the first three months of the pandemic. Overall, public sentiment toward the pandemic remained neutral except for the USA. Originality/value This paper proposes a Data Science-based approach to better understand the public topics of concern during the COVID-19 pandemic.

scholarly journals The Shared Economy

2021 ◽  
Author(s):  
KC LOH
Keyword(s):  
Marginal Utility ◽  
National Income ◽  
Disposable Income ◽  
Allocation Of Resources ◽  
National Statistics ◽  
Wide Range ◽  
Household Disposable Income ◽  
Using Data ◽  
The Uk

Abstract: The theory of marginal utility describes how consumers choose between goods. However, marginal utility has also found application in a wide range of weightier subjects. For example, marginal utility can be used in the allocation of resources in healthcare programmes. This paper posits that marginal utility is also applicable in the allocation of the national income among corporations, government, and households. Using data from the UK Office for National Statistics, this paper finds that for the most part of the decade, from 2009 to 2018, household disposable income fell short of what might be considered an optimal share of the national income.

scholarly journals Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogramand Bagging

2021 ◽  
Vol 12 (3) ◽  
pp. 1916-1922
Author(s):  
Chew-Wai Yap Et.al
Keyword(s):  
Diabetic Retinopathy ◽  
Data Science ◽  
Learning Algorithms ◽  
Clinical Excellence ◽  
Fundus Images ◽  
Data Set ◽  
Detection Model ◽  
Nearest Neighbours ◽  
Using Data ◽  
The Uk

An application of auto-detecting Diabetic Retinopathy (DR) is indispensable to aid the ophthalmologists in diagnosing patients and also to help relevant organisations in accumulating and analysing data. This project presents DR Miner, an application that can extract data from fundus images, identify the symptoms of DR in retina images by using data science approaches, and collect the ophthalmologist’s review to improve the detection model in the future. To form the DR data set with binary classes, Auto Colour Correlogram (ACC) was utilised to extract the features from DR images. Over-sampling was then conducted to balance the class distribution in the data set. To reduce the variance of the single learning algorithms, we evaluated various bagging approaches. Theresults showed that the bagging approaches gave better results than the single learning algorithms in general. Out of all bagging approaches we evaluated, bagged k-nearest neighbours gave the best result. The sensitivity achieved was 85.1%, which met the requirement set by the UK National Institute for Clinical Excellence.

scholarly journals Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration

2018 ◽  
Vol 376 (2128) ◽  
pp. 20170357 ◽  
Author(s):  
Slava Jankin Mikhaylov ◽  
Marc Esteve ◽  
Averill Campion
Keyword(s):  
Artificial Intelligence ◽  
Public Sector ◽  
Data Science ◽  
Grand Challenge ◽  
Public And Private ◽  
The Public ◽  
Using Data ◽  
The Uk ◽  
High Uncertainty

Public sector organizations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high-uncertainty environments. The long-term success of data science and artificial intelligence (AI) in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and the public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities for and challenges of AI for the public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations.This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.

scholarly journals Crise Económica em Portugal: Evolução da Incidência de Depressão e Correlação com o Desemprego

2020 ◽  
Vol 33 (13) ◽  
Author(s):  
Inês Laplanche Coelho ◽  
Mafalda Sousa-Uva ◽  
Nuno Pina ◽  
Sara Marques ◽  
Carlos Matias-Dias ◽  
...  
Keyword(s):  
Social Vulnerability ◽  
Ecological Study ◽  
Preventive Measures ◽  
Portuguese Population ◽  
Sentinel Network ◽  
Unemployment Rates ◽  
Positive Correlation ◽  
National Statistics ◽  
Using Data ◽  
The Impact

Introduction: Previous studies have found an increase in the incidence rate of depression between 2007 – 2013 in Portugal, with a positive correlation with the unemployment rate, namely, in men. So, it was hypothesized that this increase is related with the situation of economic crisis. This study aimed to investigate if the correlation between unemployment rates and the incidence of depression is maintained in the post-crisis period of economic recovery in Portugal (2016 – 2018).Material and Methods: An ecological study was carried out, using data from the General Practitioners Sentinel Network concerning depression incidence (first episodes and relapses) and data from the National Statistics Institute on unemployment rates in the Portuguese population. The correlation coefficient was estimated using linear regression and the results were disaggregated by sex.Results: Between 2016 and 2018, there was a consistent decrease in the incidence of depression in both sexes. During the 1995 – 2018 period, a positive correlation was observed between unemployment and depression, with a coefficient of 0.833 (p = 0.005) in males and of 0.742 (p = 0.022) in females.Discussion: The reduction in the incidence of depression in both sexes observed between 2016 – 2018 corroborates a positive correlation between unemployment and depression in the Portuguese population, previously observed between 2007 – 2013.Conclusion: This study highlights the need to monitor the occurrence of mental illness in the Portuguese population, especially in moments of greatest social vulnerability in order to establish preventive measures, as a way to mitigate the impact of future economic crises.

scholarly journals Validation of the BATT score for prehospital risk stratification of traumatic haemorrhagic death: usefulness for tranexamic acid treatment criteria

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Francois-Xavier Ageron ◽  
Timothy J. Coats ◽  
Vincent Darioli ◽  
Ian Roberts
Keyword(s):  
Tranexamic Acid ◽  
Acid Treatment ◽  
Injury Severity ◽  
External Validation ◽  
Research Network ◽  
Brier Score ◽  
Trauma Patients ◽  
Using Data ◽  
The Uk ◽  
The Impact

Abstract Background Tranexamic acid reduces surgical blood loss and reduces deaths from bleeding in trauma patients. Tranexamic acid must be given urgently, preferably by paramedics at the scene of the injury or in the ambulance. We developed a simple score (Bleeding Audit Triage Trauma score) to predict death from bleeding. Methods We conducted an external validation of the BATT score using data from the UK Trauma Audit Research Network (TARN) from 1st January 2017 to 31st December 2018. We evaluated the impact of tranexamic acid treatment thresholds in trauma patients. Results We included 104,862 trauma patients with an injury severity score of 9 or above. Tranexamic acid was administered to 9915 (9%) patients. Of these 5185 (52%) received prehospital tranexamic acid. The BATT score had good accuracy (Brier score = 6%) and good discrimination (C-statistic 0.90; 95% CI 0.89–0.91). Calibration in the large showed no substantial difference between predicted and observed death due to bleeding (1.15% versus 1.16%, P = 0.81). Pre-hospital tranexamic acid treatment of trauma patients with a BATT score of 2 or more would avoid 210 bleeding deaths by treating 61,598 patients instead of avoiding 55 deaths by treating 9915 as currently. Conclusion The BATT score identifies trauma patient at risk of significant haemorrhage. A score of 2 or more would be an appropriate threshold for pre-hospital tranexamic acid treatment.

scholarly journals Lockdown, slow down: impact of the COVID-19 pandemic on physical activity—an observational study

Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001600
Author(s):  
Joanne Kathryn Taylor ◽  
Haarith Ndiaye ◽  
Matthew Daniels ◽  
Fozia Ahmed
Keyword(s):  
Physical Activity ◽  
New York ◽  
Heart Association ◽  
Evaluation Study ◽  
Activity Data ◽  
Cardiac Devices ◽  
Significant Difference ◽  
New York Heart Association ◽  
Using Data ◽  
The Uk

AimsIn response to the COVID-19 pandemic, the UK was placed under strict lockdown measures on 23 March 2020. The aim of this study was to quantify the effects on physical activity (PA) levels using data from the prospective Triage-HF Plus Evaluation study.MethodsThis study represents a cohort of adult patients with implanted cardiac devices capable of measuring activity by embedded accelerometery via a remote monitoring platform. Activity data were available for the 4 weeks pre-implementation and post implementation of ‘stay at home’ lockdown measures in the form of ‘minutes active per day’ (min/day).ResultsData were analysed for 311 patients (77.2% men, mean age 68.8, frailty 55.9%. 92.2% established heart failure (HF) diagnosis, of these 51.2% New York Heart Association II), with comorbidities representative of a real-world cohort.Post-lockdown, a significant reduction in median PA equating to 20.8 active min/day was seen. The reduction was uniform with a slightly more pronounced drop in PA for women, but no statistically significant difference with respect to age, body mass index, frailty or device type. Activity dropped in the immediate 2-week period post-lockdown, but steadily returned thereafter. Median activity week 4 weeks post-lockdown remained significantly lower than 4 weeks pre-lockdown (p≤0.001).ConclusionsIn a population of predominantly HF patients with cardiac devices, activity reduced by approximately 20 min active per day in the immediate aftermath of strict COVID-19 lockdown measures.Trial registration numberNCT04177199.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

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