Assessing infrequent large earthquakes using geomorphology and geodesy in the Malawi Rift

In regions with large, mature fault systems, a characteristic earthquake model may be more appropriate for modelling earthquake occurrence than extrapolating from a short history of small, instrumentally observed earthquakes using the Gutenberg–Richter scaling law. We illustrate how the geomorphology and geodesy of the Malawi Rift, a region with large seismogenic thicknesses, long fault scarps, and slow strain rates, can be used to assess hazard probability levels for large infrequent earthquakes. We estimate potential earthquake size using fault length and recurrence intervals from plate motion velocities and generate a synthetic catalogue of events. Since it is not possible to determine from the geomorphological information if a future rupture will be continuous (7.4 ≤ M W ≤ 8.3 with recurrence intervals of 1,000–4,300 years) or segmented (6.7 ≤ M W ≤ 7.7 with 300–1,900 years), we consider both alternatives separately and also produce a mixed catalogue. We carry out a probabilistic seismic hazard assessment to produce regional- and site-specific hazard estimates. At all return periods and vibration periods, inclusion of fault-derived parameters increases the predicted spectral acceleration above the level predicted from the instrumental catalogue alone, with the most significant changes being in close proximity to the fault systems and the effect being more significant at longer vibration periods. Importantly, the results indicate that standard probabilistic seismic hazard analysis (PSHA) methods using short instrumental records alone tend to underestimate the seismic hazard, especially for the most damaging, extreme magnitude events. For many developing countries in Africa and elsewhere, which are experiencing rapid economic growth and urbanisation, seismic hazard assessments incorporating characteristic earthquake models are critical

Anticipated impacts of Brexit scenarios on UK food prices and implications for policies on poverty and health: a structured expert judgement approach

Introduction Food insecurity is associated with increased risk for several health conditions and with poor chronic disease management. Key determinants for household food insecurity are income and food costs. Whereas short-term household incomes are likely to remain static, increased food prices would be a significant driver of food insecurity. Objectives To investigate food price drivers for household food security and its health consequences in the UK under scenarios of Deal and No-deal for Britain’s exit from the European Union. To estimate the 5% and 95% quantiles of the projected price distributions. Design Structured expert judgement elicitation, a well-established method for quantifying uncertainty, using experts. In July 2018, each expert estimated the median, 5% and 95% quantiles of changes in price for 10 food categories under Brexit Deal and No-deal to June 2020 assuming Brexit had taken place on 29 March 2019. These were aggregated based on the accuracy and informativeness of the experts on calibration questions. Participants Ten specialists with expertise in food procurement, retail, agriculture, economics, statistics and household food security. Results When combined in proportions used to calculate Consumer Price Index food basket costs, median food price change for Brexit with a Deal is expected to be +6.1% (90% credible interval −3% to +17%) and with No-deal +22.5% (90% credible interval +1% to +52%). Conclusions The number of households experiencing food insecurity and its severity is likely to increase because of expected sizeable increases in median food prices after Brexit. Higher increases are more likely than lower rises and towards the upper limits, these would entail severe impacts. Research showing a low food budget leads to increasingly poor diet suggests that demand for health services in both the short and longer terms is likely to increase due to the effects of food insecurity on the incidence and management of diet-sensitive conditions

A commentary on “how to interpret expert judgment assessments of twenty-first century sea-level rise” by Hylke de Vries and Roderik SW van de Wal

We clarify key aspects of the evaluation, by de Vries and van de Wal (2015), of our expert elicitation paper on the contributions of ice sheet melting to sea level rise due to future global temperature rise scenarios (Bamber and Aspinall 2013), and extend the conversation with further analysis of their proposed approach for combining expert uncertainty judgments.Applied Probabilit

Bayesian Network Modeling and Expert Elicitation for Probabilistic Eruption Forecasting: Pilot Study for Whakaari/White Island, New Zealand

Bayesian Networks (BNs) are probabilistic graphical models that provide a robust and flexible framework for understanding complex systems. Limited case studies have demonstrated the potential of BNs in modeling multiple data streams for eruption forecasting and volcanic hazard assessment. Nevertheless, BNs are not widely employed in volcano observatories. Motivated by their need to determine eruption-related fieldwork risks, we have worked closely with the New Zealand volcano monitoring team to appraise BNs for eruption forecasting with the purpose, at this stage, of assessing the utility of the concept rather than develop a full operational framework. We adapted a previously published BN for a pilot study to forecast volcanic eruption on Whakaari/White Island. Developing the model structure provided a useful framework for the members of the volcano monitoring team to share their knowledge and interpretation of the volcanic system. We aimed to capture the conceptual understanding of the volcanic processes and represent all observables that are regularly monitored. The pilot model has a total of 30 variables, four of them describing the volcanic processes that can lead to three different types of eruptions: phreatic, magmatic explosive and magmatic effusive. The remaining 23 variables are grouped into observations related to seismicity, fluid geochemistry and surface manifestations. To estimate the model parameters, we held a workshop with 11 experts, including two from outside the monitoring team. To reduce the number of conditional probabilities that the experts needed to estimate, each variable is described by only two states. However, experts were concerned about this limitation, in particular for continuous data. Therefore, they were reluctant to define thresholds to distinguish between states. We conclude that volcano monitoring requires BN modeling techniques that can accommodate continuous variables. More work is required to link unobservable (latent) processes with observables and with eruptive patterns, and to model dynamic processes. A provisional application of the pilot model revealed several key insights. Refining the BN modeling techniques will help advance understanding of volcanoes and improve capabilities for forecasting volcanic eruptions. We consider that BNs will become essential for handling ever-burgeoning observations, and for assessing data's evidential meaning for operational eruption forecasting

Vulnerability of bridges to scour:Insights from an international expert elicitation workshop

Scour (localised erosion) during flood events is one of the most significant threats to bridges over rivers and estuaries, and has been the cause of numerous bridge failures, with damaging consequences. Mitigation of the risk of bridges being damaged by scour is therefore important to many infrastructure owners, and is supported by industry guidance. Even after mitigation, some residual risk remains, though its extent is difficult to quantify because of the uncertainties inherent in the prediction of scour and the assessment of the scour risk. This paper summarises findings from an international expert workshop on bridge scour risk assessment that explores uncertainties about the vulnerability of bridges to scour. Two specialised structured elicitation methods were applied to explore the factors that experts in the field consider important when assessing scour risk and to derive pooled expert judgements of bridge failure probabilities that are conditional on a range of assumed scenarios describing flood event severity, bridge and watercourse types and risk mitigation protocols. The experts' judgements broadly align with industry good practice, but indicate significant uncertainty about quantitative estimates of bridge failure probabilities, reflecting the difficulty in assessing the residual risk of failure. The data and findings presented here could provide a useful context for the development of generic scour fragility models and their associated uncertainties

Alternative Covid-19 mitigation measures in school classrooms:analysis using an agent-based model of SARS-CoV-2 transmission

The SARS-CoV-2 epidemic has impacted children's education, with schools required to implement infection control measures that have led to periods of absence and classroom closures. We developed an agent-based epidemiological model of SARS-CoV-2 transmission in a school classroom that allows us to quantify projected infection patterns within primary school classrooms, and related uncertainties. Our approach is based on a contact model constructed using random networks, informed by structured expert judgement. The effectiveness of mitigation strategies in suppressing infection outbreaks and limiting pupil absence are considered. COVID-19 infections in primary schools in England in autumn 2020 were re-examined and the model was then used to estimate infection levels in autumn 2021, as the Delta variant was emerging and it was thought likely that school transmission would play a major role in an incipient new wave of the epidemic. Our results were in good agreement with available data. These findings indicate that testing-based surveillance is more effective than bubble quarantine, both for reducing transmission and avoiding pupil absence, even accounting for insensitivity of self-administered tests. Bubble quarantine entails large numbers of absences, with only modest impact on classroom infections. However, maintaining reduced contact rates within the classroom can have a major benefit for managing COVID-19 in school settings