Decision-making accuracy of soccer referees in relation to markers of internal and external load.

This study examined the relationships between the decision-making performances of soccer referees and markers of physiological load. Following baseline measurements and habituation procedures, 13 national-level male referees completed a novel Soccer Referee Simulation whilst simultaneously adjudicating on a series of video-based decision-making clips. The correctness of each decision was assessed in relation to the mean heart rate (HR), respiratory rate (RR), minute ventilation (VE), perceptions of breathlessness (RPE-B) and local muscular (RPE-M) exertion and running speeds recorded in the 10-s and 60-s preceding decisions. There was a significant association between decision-making accuracy and the mean HR (p = 0.042; VC = 0.272) and RR (p = 0.024, VC = 0.239) in the 10-s preceding decisions, with significantly more errors observed when HR ≥ 90% of HRmax (OR, 5.39) and RR ≥ 80% of RRpeak (OR, 3.34). Decision-making accuracy was also significantly associated with the mean running speeds performed in the 10-s (p = 0.003; VC = 0.320) and 60-s (p = 0.016; VC = 0.253) preceding decisions, with workloads of ≥250 m·min-1 associated with an increased occurrence of decisional errors (OR, 3.84). Finally, there was a significant association between decision-making accuracy and RPE-B (p = 0.021; VC = 0.287), with a disproportionate number of errors occurring when RPE-B was rated as "very strong" to "maximal" (OR, 7.19). Collectively, the current data offer novel insights into the detrimental effects that high workloads may have upon the decision-making performances of soccer referees. Such information may be useful in designing combined physical and decision-making training programmes that prepare soccer referees for the periods of match play that prove most problematic to their decision-making.

Optimal age-Based vaccination and economic mitigation policies for the second phase of the covid-19 pandemic.

In this paper we ask how to best allocate a given time-varying supply of vaccines during the second phase of the Covid-19 pandemic across individuals of different ages. Building on our previous heterogeneous household model of optimal economic mitigation and redistribution (Glover et al., 2021) we contrast the actual vaccine deployment path that prioritized older, retired individuals with one that first vaccinates younger workers. Vaccinating the old first saves more lives but slows the economic recovery, relative to inoculating the young first. Vaccines deliver large welfare benefits in both scenarios (relative to a world without vaccines), but the old-first policy is optimal under a utilitarian social welfare function. The welfare gains from having vaccinated the old first are especially significant once the economy is hit by a more infectious Delta variant in the summer of 2021.

A vector-host model to assess the impact of superinfection exclusion on vaccination strategies using dengue and yellow fever as case studies.

Superinfection exclusion is a phenomenon whereby the co-infection of a host with a secondary pathogen is prevented due to a current infection by another closely-related pathogenic strain. We construct a novel vector-host mathematical model for two pathogens that exhibit superinfection exclusion and simultaneously account for vaccination strategies against them. We then derive the conditions under which an endemic disease will prevent the establishment of another through the action of superinfection exclusion and show that vaccination against the endemic strain can enable the previously suppressed strain to invade the population. Through appropriate parameterisation of the model for dengue and yellow fever we find that superinfection exclusion alone is unlikely to explain the absence of yellow fever in many regions where dengue is endemic, and that the rollout of the recently licensed dengue vaccine, Dengvaxia, is unlikely to enable the establishment of Yellow Fever in regions where it has previously been absent.

Impact of specialist care on clinical outcomes for medical emergencies.

General hospitals have commonly involved a wide range of medical specialists in the care of unselected medical emergency admissions. In 1999, the Royal Liverpool University Hospital, a 915-bed hospital with a busy emergency service, changed its system of care for medical emergencies to allow early placement of admitted patients under the care of the most appropriate specialist team, with interim care provided by specialist acute physicians on an acute medicine unit - a system we have termed 'specialty triage'. Here we describe a retrospective study in which all 133,509 emergency medical admissions from February 1995 to January 2003 were analysed by time-series analysis with correction for the underlying downward trend from 1995 to 2003. This showed that the implementation of specialty triage in May 1999 was associated with a subsequent additional reduction in the mortality of the under-65 age group by 0.64% (95% CI 0.11 to 1.17%; P=0.021) from the 2.4% mortality rate prior to specialty triage, equivalent to approximately 51 fewer deaths per year. No significant effect was seen for those over 65 or all age groups together when corrected for the underlying trend. Length of stay and readmission rates showed a consistent downward trend that was not significantly affected by specialty triage. The data suggest that appropriate specialist management improves outcomes for medical emergencies, particularly amongst younger patients.