Large-scale calibration and simulation of COVID-19 epidemiologic scenarios to support healthcare planning.

The COVID-19 pandemic has provided stiff challenges for planning and resourcing in health services in the UK and worldwide. Epidemiological models can provide simulations of how infectious disease might progress in a population given certain parameters. We adapted an agent-based model of COVID-19 to inform planning and decision-making within a healthcare setting, and created a software framework that automates processes for calibrating the model parameters to health data and allows the model to be run at national population scale on National Health Service (NHS) infrastructure. We developed a method for calibrating the model to three daily data streams (hospital admissions, intensive care occupancy, and deaths), and demonstrate that on cross-validation the model fits acceptably to unseen data streams including official estimates of COVID-19 incidence. Once calibrated, we use the model to simulate future scenarios of the spread of COVID-19 in England and show that the simulations provide useful projections of future COVID-19 clinical demand. These simulations were used to support operational planning in the NHS in England, and we present the example of the use of these simulations in projecting future clinical demand during the rollout of the national COVID-19 vaccination programme. Being able to investigate uncertainty and test sensitivities was particularly important to the operational planning team. This epidemiological model operates within an ecosystem of data technologies, drawing on a range of NHS, government and academic data sources, and provides results to strategists, planners and downstream data systems. We discuss the data resources that enabled this work and the data challenges that were faced.

binary junipr: An Interpretable Probabilistic Model for Discrimination.

junipr is an approach to unsupervised learning in particle physics that scaffolds a probabilistic model for jets around their representation as binary trees. Separate junipr models can be learned for different event or jet types, then compared and explored for physical insight. The relative probabilities can also be used for discrimination. In this Letter, we show how the training of the separate models can be refined in the context of classification to optimize discrimination power. We refer to this refined approach as binary junipr. binary junipr achieves state-of-the-art performance for quark-gluon discrimination and top tagging. The trained models can then be analyzed to provide physical insight into how the classification is achieved. As examples, we explore differences between quark and gluon jets and between gluon jets generated with two different simulations.

Precision jet substructure from boosted event shapes.

Jet substructure has emerged as a critical tool for LHC searches, but studies so far have relied heavily on shower Monte Carlo simulations, which formally approximate QCD at the leading-log level. We demonstrate that systematic higher-order QCD computations of jet substructure can be carried out by boosting global event shapes by a large momentum Q and accounting for effects due to finite jet size, initial-state radiation (ISR), and the underlying event (UE) as 1/Q corrections. In particular, we compute the 2-subjettiness substructure distribution for boosted Z→qq[over ¯] events at the LHC at next-to-next-to-next-to-leading-log order. The calculation is greatly simplified by recycling known results for the thrust distribution in e(+)e(-) collisions. The 2-subjettiness distribution quickly saturates, becoming Q independent for Q > or approximately equal to 400 GeV. Crucially, the effects of jet contamination from ISR/UE can be subtracted out analytically at large Q without knowing their detailed form. Amusingly, the Q=∞ and Q=0 distributions are related by a scaling by e up to next-to-leading-log order.