Integrating a multivariate extreme value method within a system flood risk analysis model

Effective management of flooding requires models that are capable of quantifying flood risk. Quantification of flood risk involves both the quantification of probabilities of flooding and the associated consequences. Modern flood risk models account for the probabilities of extreme hydraulic loading events and also include a probabilistic representation of the performance of flood defence infrastructure and its associated reliability. The spatial and temporal variability of flood events makes probabilistic representation of the hydraulic loading conditions on the flood defences complex. In the system method used widely within England and Wales, simplifying assumptions relating to the spatial dependence of flood events are made. Recent research has shown the benefits of using improved multivariate extreme value methods to define the hydraulic loading conditions for flood risk analysis models. This paper describes the development of an improved modelling system that enhances the systems-based risk analysis model currently applied in practice, through the incorporation of a multivariate extreme value model. The improved system has been presented on a case study site in the North West of England

A generic and practical wave overtopping model that includes uncertainty

Mean wave overtopping discharge is generally accepted to be a primary design criterion for assessing the performance of coastal structures. It is a boundary condition for many coastal flood risk assessments. Modern methods for assessing wave overtopping discharges and their consequences are well documented and reported. Among the various tools available for assessing wave overtopping, the use of artificial neural networks has become increasingly popular. This paper introduces the next stage in the development of these models. Using the same source data, the new generic meta-modelling overtopping model reduces uncertainties and gives clear guidance on the range and validity of the outputs

Gravel beach profile response allowing for bimodal sea-states

The south coast of the UK is identified as a location where significant wave swell components are present within the regional wave climate. During the winters of 2006 and 2014, several sites along the south coast of the UK were subject to significant damages where flood events were recorded. These sea–states were characterised by having a double–peaked wave spectra, observing a connection between wave spectrum shape and beach response. A 2D physical model study was carried out to investigate the effect of gravel beach profile response under wave spectra characterised by swell and wind wave periods in various combinations. The physical model results have shown the effect of bimodal wave spectrum on the beach crest erosion and have been compared with the parametric model of SHINGLE and the numerical model XBeach-G. Based on this 2D physical model study a new parametric model, Shingle–B, has been derived and an online tool has been developed and made available on the website for the National Network of Regional Coastal Monitoring Programmes of England. This new tool has been validated with two sites in the South of England where field data of both waves and profiles was available

A multisite evaluation of antifungal use in critical care: implications for antifungal stewardship

Background: ICUs are settings of high antifungal consumption. There are few data on prescribing practices in ICUs to guide antifungal stewardship implementation in this setting. Methods: An antifungal therapy (AFT) service evaluation (15 May–19 November 2019) across ICUs at three London hospitals, evaluating consumption, prescribing rationale, post-prescription review, de-escalation and final invasive fungal infection (IFI) diagnostic classification. Results: Overall, 6.4% of ICU admissions (305/4781) received AFT, accounting for 11.41 days of therapy/100 occupied bed days (DOT/100 OBD). The dominant prescribing mode was empirical (41% of consumption), followed by targeted (22%), prophylaxis (18%), pre-emptive (12%) and non-invasive (7%). Echinocandins were the most commonly prescribed drug class (4.59 DOT/100 OBD). In total, 217 patients received AFT for suspected or confirmed IFI; 12%, 10% and 23% were classified as possible, probable or proven IFI, respectively. Hence, in 55%, IFI was unlikely. Proven IFI (n = 50) was mostly invasive candidiasis (92%), of which 48% had been initiated on AFT empirically before yeast identification. Where on-site (1 → 3)-β-D-glucan (BDG) testing was available (1 day turnaround), in those with suspected but unproven invasive candidiasis, median (IQR) AFT duration was 10 (7–15) days with a positive BDG (≥80 pg/mL) versus 8 (5–9) days with a negative BDG (<80 pg/mL). Post-prescription review occurred in 79% of prescribing episodes (median time to review 1 [0–3] day). Where suspected IFI was not confirmed, 38% episodes were stopped and 4% de-escalated within 5 days. Conclusions: Achieving a better balance between promptly treating IFI patients and avoiding inappropriate antifungal prescribing in the ICU requires timely post-prescription review by specialist multidisciplinary teams and improved, evidence-based-risk prescribing strategies incorporating rapid diagnostics to guide AFT start and stop decisions