The increase of multidrug resistance and resistance to last-line antibiotics is a major global public health threat. Although surveillance programs provide useful current and historical information on the scale of the problem, the future emergence and spread of antibiotic resistance is uncertain, and quantifying this uncertainty is crucial for guiding decisions about investment in antibiotics and resistance control strategies. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts in France, Spain, and the United Kingdom projected that resistance for five pairs on the World Health Organization’s priority pathogens list (E. coli and K. pneumoniae resistant to third-generation cephalosporins and carbapenems and MRSA) would remain below 50% in 2026. In Italy, although upper bounds of 90% credible ranges exceed 50% resistance for some pairs, the medians suggest Italy will sustain or improve its current rates. We compare these expert projections to statistical forecasts based on historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net). Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts, demonstrating the potential of structured expert judgment as a tool for better understanding the uncertainty about future antibiotic resistance.@en

This chapter sets the background for when, and discusses the contexts in which, eliciting expert judgements is paramount. The way judgements are elicited and aggregated plays an essential part in distinguishing structured/formal elicitation protocols from informal ones. We emphasise the importance of properly reporting the steps and decision taken during an elicitation, and draw a parallel to the reporting of experimental designs underpinning the data collection. Directions for future research are proposed, and the chapter ends with an outline of the book.@en

The problem of failure rate estimation is considered in the light of data acquired from the ATV reporting system. A number of different models for preventive maintenance are discussed which make the failure rate identifiable, as are more general bounding methods which do not require identifiability. These techniques are illustrated on ATV data which is shown in graphs of subsurvival functions. These graphs show, amongst other things, the effect of different maintenance policies on identical systems.@en