Towards Real-Time Heavy Goods Vehicle Driving Behaviour Classification in the United Kingdom

This commentary explores the manner in which the current COVID-19 crisis is affecting key sources of entrepreneurial finance in the United Kingdom. We posit that the unique relational nature of entrepreneurial finance may make it highly susceptible to such a shock owing to the need for face-to-face interaction between investors and entrepreneurs. The article explores this conjecture by scrutinising a real-time data source of equity investments. Our findings suggest that the volume of new equity transactions in the United Kingdom has declined markedly since the outbreak of the COVID-19 pandemic. It appears that seed finance is the main type of entrepreneurial finance most acutely affected by the crisis, which typically goes to the most nascent entrepreneurial start-ups facing the greatest obstacles obtaining finance. Policy makers can utilise these real-time data sources to help inform their strategic policy interventions to assist the firms most affected by crisis events.

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The United Kingdom 2009 Swine Flu Outbreak As Recorded in Real Time by General Practitioners

Epidemiology Research International ◽

10.1155/2011/381597 ◽

2011 ◽

Vol 2011 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Hershel Jick ◽

Dean S. MacLaughlin ◽

Pascal Egger ◽

Peter Wiggins

Keyword(s):

General Practice ◽

United Kingdom ◽

Real Time ◽

General Practitioners ◽

Swine Flu ◽

Practice Research ◽

Research Database ◽

Planning Stage ◽

The United Kingdom ◽

The Uk

Background. Initially the course of the 2009 swine flu pandemic was uncertain and impossible to predict with any confidence. An effective prospective data resource exists in the United Kingdom (UK) that could have been utilized to describe the scope and extent of the swine flu outbreak as it unfolded. We describe the 2009 swine flu outbreak in the UK as recorded daily by general practitioners and the potential use of this database for real-time tracking of flu outbreaks. Methods. Using the General Practice Research Database, a real-time general practice, electronic database, we estimated influenza incidence from July 1998 to September 2009 according to age, region, and calendar time. Results. From 1998 to2008, influenza outbreaks regularly occurred yearly from October to March, but did not typically occur from April to September until the swine flu outbreak began in April 2009. The weekly incidence rose gradually, peaking at the end of July, and the outbreak had largely dissipated by early September. Conclusions. The UK swine flu outbreak, recorded in real time by a large group of general practitioners, was mild and limited in time. Simultaneous online access seemed feasible and could have provided additional clinical-based evidence at an early planning stage of the outbreak.

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Nonlinear Dynamics in Real-Time Equity Market Indices: Evidence from the United Kingdom

The Economic Journal ◽

10.2307/2235155 ◽

1995 ◽

Vol 105 (431) ◽

pp. 864 ◽

Cited By ~ 75

Author(s):

A. Abhyankar ◽

L. S. Copeland ◽

W. Wong

Keyword(s):

Nonlinear Dynamics ◽

United Kingdom ◽

Real Time ◽

Equity Market ◽

The United Kingdom

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Real-time Decision Support and Intelligence for Prevention and Control of Infectious Disease in the United Kingdom

International Journal of Infectious Diseases ◽

10.1016/j.ijid.2008.05.472 ◽

2008 ◽

Vol 12 ◽

pp. e190

Author(s):

C. Kara-Zaitri ◽

R. Hamilton ◽

D.R. Gelletlie ◽

M. Schweiger ◽

H. Barnes

Keyword(s):

Infectious Disease ◽

United Kingdom ◽

Decision Support ◽

Real Time ◽

Prevention And Control ◽

The United Kingdom ◽

And Control

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Syndromic surveillance insights from a symptom assessment app before and during COVID-19 measures in Germany and the United Kingdom: results from repeated cross-sectional analyses

10.1101/2020.06.16.20126466 ◽

2020 ◽

Author(s):

Alicia Mehl ◽

Francois Bergey ◽

Caoimhe Cawley ◽

Andreas Gilsdorf

Keyword(s):

Public Health ◽

United Kingdom ◽

Real Time ◽

Syndromic Surveillance ◽

Symptom Assessment ◽

Assessment Tools ◽

Added Value ◽

Cross Sectional ◽

Syndromic Surveillance System ◽

The United Kingdom

AbstractBackgroundUnprecedented lockdown measures have been introduced in countries across the world to mitigate the spread and consequences of COVID-19. While attention has focused on the effects of these measures on epidemiological indicators relating directly to the infection, there is increased recognition of their broader health implications. However, assessing these implications in real time is a challenge, due to limitations of existing syndromic surveillance data and tools.ObjectiveTo explore the added value of mobile phone app-based symptom assessment tools as real time health insight providers to inform public health policy makers.MethodsA comparative and descriptive analysis of the proportion of all self-reported symptoms entered by users during an Ada assessment in Germany and the United Kingdom (UK) was conducted between two periods: before and after the implementation of “Phase One” COVID-19 measures. Additional analyses were performed to explore the association between symptom trends and seasonality, and symptom trends and weather. Differences in the proportion of unique symptoms between the periods were analysed using Pearson’s Chi-squared test and reported as Log2 Fold Changes (Log2 FC).ResultsBetween 48,300-54,900 symptomatic users reported 140,500-170,400 symptoms during the Baseline and Measures periods in Germany. Between 34,200-37,400 symptomatic users in the UK reported 112,100-131,900 symptoms during the Baseline and Measures periods. The majority of symptomatic users were female (Germany 68,600/103,200, 66.52%; UK 51,200/71,600, 72.74%). The majority (Germany 68,500/100,000, 68.45%; UK 50,900/68,800, 73.91%) were aged between 10 and 29 years, and about a quarter (Germany 26,200/100,000, 26.15%; UK 14,900/68,800, 21.65%) were between 30-59 years. 103 symptoms were reported either more or less frequently (with statistically significant differences) during the Measures as compared to the Baseline period, and 34 of these were found in both countries. The following mental health symptoms (Log2 FC, P-value) were reported less often during the Measures period: inability to manage constant stress and demands at work (−1.07, P<.001), memory difficulty (−0.56, P<.001), depressed mood (−0.42, P<.001), and impaired concentration (−0.46, P<.001). Diminished sense of taste (2.26, P<.001) and hyposmia (2.20, P<.001) were reported more frequently during the Measures period. None of the 34 symptoms were found to be different between the same dates in 2019. Fourteen of the 34 symptoms had statistically significant associations with weather variables.ConclusionsSymptom assessment apps have an important role to play in facilitating improved understanding of the implications of public health policies such as COVID-19 lockdown measures. Not only do they provide the means to complement and cross-validate hypotheses based on data collected through more traditional channels, they can also generate novel insights through a real-time syndromic surveillance system.

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Performance of Real-Time Reverse Transcription Polymerase Chain Reaction for the Detection of Foot-and-Mouth Disease Virus during Field Outbreaks in the United Kingdom in 2007

Journal of Veterinary Diagnostic Investigation ◽

10.1177/104063870902100303 ◽

2009 ◽

Vol 21 (3) ◽

pp. 321-330 ◽

Cited By ~ 33

Author(s):

Scott M. Reid ◽

Katja Ebert ◽

Katarzyna Bachanek-Bankowska ◽

Carrie Batten ◽

Anna Sanders ◽

...

Keyword(s):

Polymerase Chain Reaction ◽

United Kingdom ◽

Real Time ◽

Reverse Transcription ◽

Foot And Mouth Disease ◽

Rt Pcr ◽

Chain Reaction ◽

Pcr Assay ◽

The United Kingdom ◽

Polymerase Chain

Rapid and accurate diagnosis is essential for effective control of foot-and-mouth disease (FMD). The present report describes the practical steps undertaken to deploy a real-time reverse transcription polymerase chain reaction (real-time RT-PCR) to process the samples received during the outbreaks of FMD in the United Kingdom in 2007. Two independent real-time RT-PCR assays targeting different regions (5′UTR and 3D) of the FMD virus (FMDV) genome were used to confirm the presence of FMDV in clinical samples collected from the first infected premises. Once the FMDV strain responsible had been sequenced, a single real-time RT-PCR assay (3D) was selected to test a total of 3,216 samples, including material from all 8 infected premises. Using a 96-well automated system to prepare nucleic acid template, up to 84 samples could be processed within 5 hr of submission, and up to 269 samples were tested per working day. A conservative cut-off was used to designate positive samples, giving rise to an assay specificity of 99.9% or 100% for negative control material or samples collected from negative premises, respectively. For the first time, real-time RT-PCR results were used to recognize preclinical FMD in a cattle herd. Furthermore, during the later stages of the outbreaks, the real-time RT-PCR assay supported an active surveillance program within high-risk cattle herds. To the authors' knowledge, this is the first documented use of real-time RT-PCR as a principal laboratory diagnostic tool following introduction of FMD into a country that was FMD-free (without vaccination) and highlights the advantages of this assay to support control decisions during disease outbreaks.

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The Disdrometer Verification Network (DiVeN): a UK network of laser precipitation instruments

10.5194/amt-2018-302 ◽

2018 ◽

Author(s):

Ben S. Pickering ◽

Ryan R. Neely III ◽

Dawn Harrison

Keyword(s):

United Kingdom ◽

Real Time ◽

Quality Index ◽

Ice Crystals ◽

First Year ◽

Classification Algorithms ◽

Immediate Feedback ◽

The United Kingdom ◽

Code Quality ◽

Internal Processing

Abstract. Starting in February 2017, a network of 14 Thies Laser Precipitation Monitors (LPMs) were installed at various locations around the United Kingdom to create the Disdrometer Verification Network (DiVeN). The instruments were installed for verification of radar hydrometeor classification algorithms but are valuable for much wider use in the scientific and operational meteorological community. Every Thies LPM is able to designate each observed hydrometeor into one of 20 diameter bins from ≥ 0.125 mm to > 8 mm, and one of 22 speed bins from > 0.0 m s−1 to > 20.0 m s−1. Using empirically-derived relationships, the instrument classifies precipitation into one of 11 possible hydrometeor classes in the form of a present weather code, with an associated indicator of uncertainty. To provide immediate feedback to data users, the observations are plotted in near real time (NRT) and made publicly available on a website within 7 minutes. Here we describe the Disdrometer Verification Network and subjectively discuss the skill of the Thies LPM for hydrometeor type identification using specific cases from the first year of observations. Cases presented here suggest that the Thies LPM performs well at identifying transitions between rain and snow, but struggles with detection of graupel and pristine ice crystals (which occur infrequently in the United Kingdom) inherently, due to internal processing. The present weather code quality index is shown to have some skill without the supplementary sensors recommended by the manufacturer. Overall the Thies LPM is a useful tool for detecting hydrometeor type at the surface and DiVeN provides a novel dataset not previously observed for the United Kingdom.

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The Disdrometer Verification Network (DiVeN): a UK network of laser precipitation instruments

Atmospheric Measurement Techniques ◽

10.5194/amt-12-5845-2019 ◽

2019 ◽

Vol 12 (11) ◽

pp. 5845-5861 ◽

Cited By ~ 5

Author(s):

Ben S. Pickering ◽

Ryan R. Neely III ◽

Dawn Harrison

Keyword(s):

United Kingdom ◽

Real Time ◽

Quality Index ◽

Ice Crystals ◽

First Year ◽

Classification Algorithms ◽

Immediate Feedback ◽

The United Kingdom ◽

Code Quality ◽

Internal Processing

Abstract. Starting in February 2017, a network of 14 Thies laser precipitation monitors (LPMs) were installed at various locations around the United Kingdom to create the Disdrometer Verification Network (DiVeN). The instruments were installed for verification of radar hydrometeor classification algorithms but are valuable for much wider use in the scientific and operational meteorological community. Every Thies LPM is able to designate each observed hydrometeor into one of 20 diameter bins from ≥0.125 to >8 mm and one of 22 speed bins from >0.0 to >20.0 m s−1. Using empirically derived relationships, the instrument classifies precipitation into one of 11 possible hydrometeor classes in the form of a present weather code, with an associated indicator of uncertainty. To provide immediate feedback to data users, the observations are plotted in near-real time (NRT) and made publicly available on a website within 7 min. Here we describe the Disdrometer Verification Network and present specific cases from the first year of observations. Cases shown here suggest that the Thies LPM performs well at identifying transitions between rain and snow, but struggles with detection of graupel and pristine ice crystals (which occur infrequently in the United Kingdom) inherently, due to internal processing. The present weather code quality index is shown to have some skill without the supplementary sensors recommended by the manufacturer. Overall the Thies LPM is a useful tool for detecting hydrometeor type at the surface and DiVeN provides a novel dataset not previously observed for the United Kingdom.

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Syndromic Surveillance Insights from a Symptom Assessment App Before and During COVID-19 Measures in Germany and the United Kingdom: Results From Repeated Cross-Sectional Analyses (Preprint)

10.2196/preprints.21364 ◽

2020 ◽

Cited By ~ 1

Author(s):

Alicia Mehl ◽

Francois Bergey ◽

Caoimhe Cawley ◽

Andreas Gilsdorf

Keyword(s):

Public Health ◽

United Kingdom ◽

Real Time ◽

Syndromic Surveillance ◽

Symptom Assessment ◽

Assessment Tools ◽

Added Value ◽

Cross Sectional ◽

Syndromic Surveillance System ◽

The United Kingdom

BACKGROUND Unprecedented lockdown measures have been introduced in countries worldwide to mitigate the spread and consequences of COVID-19. Although attention has been focused on the effects of these measures on epidemiological indicators relating directly to the infection, there is increased recognition of their broader health implications. However, assessing these implications in real time is a challenge, due to the limitations of existing syndromic surveillance data and tools. OBJECTIVE The aim of this study is to explore the added value of mobile phone app–based symptom assessment tools as real-time health insight providers to inform public health policy makers. METHODS A comparative and descriptive analysis of the proportion of all self-reported symptoms entered by users during an assessment within the Ada app in Germany and the United Kingdom was conducted between two periods, namely before and after the implementation of “Phase One” COVID-19 measures. Additional analyses were performed to explore the association between symptom trends and seasonality, and symptom trends and weather. Differences in the proportion of unique symptoms between the periods were analyzed using a Pearson chi-square test and reported as log2 fold changes. RESULTS Overall, 48,300-54,900 symptomatic users reported 140,500-170,400 symptoms during the Baseline and Measures periods in Germany. Overall, 34,200-37,400 symptomatic users in the United Kingdom reported 112,100-131,900 symptoms during the Baseline and Measures periods. The majority of symptomatic users were female (Germany: 68,600/103,200, 66.52%; United Kingdom: 51,200/71,600, 72.74%). The majority were aged 10-29 years (Germany: 68,500/100,000, 68.45%; United Kingdom: 50,900/68,800, 73.91%), and about one-quarter were aged 30-59 years (Germany: 26,200/100,000, 26.15%; United Kingdom: 14,900/68,800, 21.65%). Overall, 103 symptoms were reported either more or less frequently (with statistically significant differences) during the Measures period as compared to the Baseline period, and 34 of these were reported in both countries. The following mental health symptoms (log2 fold change, P value) were reported less often during the Measures period: inability to manage constant stress and demands at work (–1.07, P<.001), memory difficulty (–0.56, P<.001), depressed mood (–0.42, P<.001), and impaired concentration (–0.46, P<.001). Diminished sense of taste (2.26, P<.001) and hyposmia (2.20, P<.001) were reported more frequently during the Measures period. None of the 34 symptoms were found to be different between the same dates in 2019. In total, 14 of the 34 symptoms had statistically significant associations with weather variables. CONCLUSIONS Symptom assessment apps have an important role to play in facilitating improved understanding of the implications of public health policies such as COVID-19 lockdown measures. Not only do they provide the means to complement and cross-validate hypotheses based on data collected through more traditional channels, they can also generate novel insights through a real-time syndromic surveillance system.

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Evaluation of the Biofire FilmArray BioThreat-E Test (v2.5) for Rapid Identification of Ebola Virus Disease in Heat-Treated Blood Samples Obtained in Sierra Leone and the United Kingdom

Journal of Clinical Microbiology ◽

10.1128/jcm.02287-15 ◽

2015 ◽

Vol 54 (1) ◽

pp. 114-119 ◽

Cited By ~ 27

Author(s):

Simon A. Weller ◽

Daniel Bailey ◽

Steven Matthews ◽

Sarah Lumley ◽

Angela Sweed ◽

...

Keyword(s):

United Kingdom ◽

Real Time ◽

Sierra Leone ◽

Real Time Pcr ◽

Ebola Virus ◽

Reference Method ◽

Blood Samples ◽

The United Kingdom ◽

Heat Treated ◽

Significant Difference

Rapid Ebola virus (EBOV) detection is crucial for appropriate patient management and care. The performance of the FilmArray BioThreat-E test (v2.5) using whole-blood samples was evaluated in Sierra Leone and the United Kingdom and was compared with results generated by a real-time Ebola Zaire PCR reference method. Samples were tested in diagnostic laboratories upon availability, included successive samples from individual patients, and were heat treated to facilitate EBOV inactivation prior to PCR. The BioThreat-E test had a sensitivity of 84% (confidence interval [CI], 64% to 95%) and a specificity of 89% (CI, 73% to 97%) in Sierra Leone (n= 60; 44 patients) and a sensitivity of 75% (CI, 19% to 99%) and a specificity of 100% (CI, 97% to 100%) in the United Kingdom (n= 108; 70 patients) compared to the reference real-time PCR. Statistical analysis (Fisher's exact test) indicated there was no significant difference between the methods at the 99% confidence level in either country. In 9 discrepant results (5 real-time PCR positives and BioThreat-E test negatives and 4 real-time PCR negatives and BioThreat-E test positives), the majority (n= 8) were obtained from samples with an observed or probable low viral load. The FilmArray BioThreat-E test (v2.5) therefore provides an attractive option for laboratories (either in austere field settings or in countries with an advanced technological infrastructure) which do not routinely offer an EBOV diagnostic capability.

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