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Norovirus is a significant global cause of viral gastroenteritis, with raw oyster consumption often linked to such outbreaks due to their filter-feeding in harvest waters. National water quality and depuration/relaying times are often classified using Escherichia coli , a poor proxy for norovirus levels in shellfish. The current norovirus assay is limited to only the digestive tracts of oysters, meaning the total norovirus load of an oyster may differ from reported results. These limitations motivated this work, building upon previous modelling by the authors, and considers the sequestration of norovirus into observed and cryptic (unobservable) compartments within each oyster. Results show that total norovirus levels in shellfish batches exhibit distinct peaks during the early depuration stages, with each peak's magnitude dependent on the proportion of cryptic norovirus. These results are supported by depuration trial data and other studies, where viral levels often exhibit multiphase decays. This work's significant result is that any future norovirus legislation needs to consider not only the harvest site's water classification but also the total viral load present in oysters entering the market. We show that 62 h of depuration should be undertaken before any norovirus testing is conducted on oyster samples, being the time required for cryptic viral loads to have transited into the digestive tracts where they can be detected by current assay, or have exited the oyster. • Several studies report multiphase decay of norovirus in oysters during depuration. • The model splits norovirus loads into cryptic (unobservable) and observable compartments within oysters. • Results synthesise reported multiphase decay of norovirus, with levels of cryptic norovirus dissipating after ≈ 62 hours of depuration. • To minimise food safety risks to consumers, any future legislation should assay oyster norovirus levels after 62 h of depuration to account for any cryptic norovirus levels present. [ABSTRACT FROM AUTHOR]

Norovirus is a major cause of viral gastroenteritis, with shellfish consumption being identified as one potential norovirus entry point into the human population. Minimising shellfish norovirus levels is therefore important for both the consumer’s protection and the shellfish industry’s reputation. One method used to reduce microbiological risks in shellfish is depuration; however, this process also presents additional costs to industry. Providing a mechanism to estimate norovirus levels during depuration would therefore be useful to stakeholders. This paper presents a mathematical model of the depuration process and its impact on norovirus levels found in shellfish. Two fundamental stages of norovirus depuration are considered: (i) the initial distribution of norovirus loads within a shellfish population and (ii) the way in which the initial norovirus loads evolve during depuration. Realistic assumptions are made about the dynamics of norovirus during depuration, and mathematical descriptions of both stages are derived and combined into a single model. Parameters to describe the depuration effect and norovirus load values are derived from existing norovirus data obtained from U.K. harvest sites. However, obtaining population estimates of norovirus variability is time-consuming and expensive; this model addresses the issue by assuming a ‘worst case scenario’ for variability of pathogens, which is independent of mean pathogen levels. The model is then used to predict minimum depuration times required to achieve norovirus levels which fall within possible risk management levels, as well as predictions of minimum depuration times for other water-borne pathogens found in shellfish. Times for Escherichia coli predicted by the model all fall within the minimum 42 hours required for class B harvest sites, whereas minimum depuration times for norovirus and FRNA+ bacteriophage are substantially longer. Thus this study provides relevant information and tools to assist norovirus risk managers with future control strategies. [ABSTRACT FROM AUTHOR]

Recently, football has seen the creation of various novel, ubiquitous metrics used throughout clubs' analytics departments. These can influence many of their day-to-day operations ranging from financial decisions on player transfers, to evaluation of team performance. At the forefront of this scientific movement is the metric expected goals, a measure which allows analysts to quantify how likely a given shot is to result in a goal however, xG models have not until this point considered using important features, e.g., player/team ability and psychological effects, and is not widely trusted by everyone in the wider football community. This study aims to solve both these issues through the implementation of machine learning techniques by, modelling expected goals values using previously untested features and comparing the predictive ability of traditional statistics against this newly developed metric. Error values from the expected goals models built in this work were shown to be competitive with optimal values from other papers, and some of the features added in this study were revealed to have a significant impact on expected goals model outputs. Secondly, not only was expected goals found to be a superior predictor of a football team's future success when compared to traditional statistics, but also our results outperformed those collected from an industry leader in the same area. [ABSTRACT FROM AUTHOR]

One hundred years after the 1918 influenza outbreak, are we ready for the next pandemic? This paper addresses the need to identify and develop collaborative, interdisciplinary and cross-sectoral approaches to modelling of infectious diseases including the fields of not only human and veterinary medicine, but also plant epidemiology. Firstly, the paper explains the concepts on which the most common epidemiological modelling approaches are based, namely the division of a host population into susceptible, infected and removed (SIR) classes and the proportionality of the infection rate to the size of the susceptible and infected populations. It then demonstrates how these simple concepts have been developed into a vast and successful modelling framework that has been used in predicting and controlling disease outbreaks for over 100 years. Secondly, it considers the compartmental models based on the SIR paradigm within the broader concept of a 'disease tetrahedron' (comprising host, pathogen, environment and man) and uses it to review the similarities and differences among the fields comprising the 'OneHealth' approach. Finally, the paper advocates interactions between all fields and explores the future challenges facing modellers. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'. [ABSTRACT FROM AUTHOR]