An age-structured SEIR model for COVID-19 incidence in Dublin, Ireland with framework for evaluating health intervention cost.

Strategies adopted globally to mitigate the threat of COVID-19 have primarily involved lockdown measures with substantial economic and social costs with varying degrees of success. Morbidity patterns of COVID-19 variants have a strong association with age, while restrictive lockdown measures have association with negative mental health outcomes in some age groups. Reduced economic prospects may also afflict some age cohorts more than others. Motivated by this, we propose a model to describe COVID-19 community spread incorporating the role of age-specific social interactions. Through a flexible parameterisation of an age-structured deterministic Susceptible Exposed Infectious Removed (SEIR) model, we provide a means for characterising different forms of lockdown which may impact specific age groups differently. Social interactions are represented through age group to age group contact matrices, which can be trained using available data and are thus locally adapted. This framework is easy to interpret and suitable for describing counterfactual scenarios, which could assist policy makers with regard to minimising morbidity balanced with the costs of prospective suppression strategies. Our work originates from an Irish context and we use disease monitoring data from February 29th 2020 to January 31st 2021 gathered by Irish governmental agencies. We demonstrate how Irish lockdown scenarios can be constructed using the proposed model formulation and show results of retrospective fitting to incidence rates and forward planning with relevant "what if / instead of" lockdown counterfactuals. Uncertainty quantification for the predictive approaches is described. Our formulation is agnostic to a specific locale, in that lockdown strategies in other regions can be straightforwardly encoded using this model.

Increases in DXA-Derived Visceral Fat Across One Season in Professional Rugby Union Players: Importance of Visceral Fat Monitoring in Athlete Body Composition Assessment.

INTRODUCTION: In rugby, the average player body mass has increased by approximately 25% since 1955. Visceral adipose tissue (VAT) is associated with low grade inflammation, and chronic diseases, such as cardiovascular diseases. The purpose of this study was to investigate changes in VAT in relation to other indices of body composition, across 1 season in professional rugby. METHODOLOGY: One hundred and sixteen male rugby union players' (age: 26.2 ± 4.6 y, BMI: 29.40 ± 3.22 kg.m2) total body composition dual energy X-ray absorptiometry scans from 4 time points across the season (baseline, preseason, midseason, and postseason) were analyzed. Players were grouped by playing position, forwards (n = 65) and backs (n = 51). Players followed individually tailored diet plans. RESULTS: Mean baseline VAT was 404.67 ± 229.43 g (forwards: 469.36 ± 263.16 g, backs: 311.40 ± 121.15 g). Total mass, lean mass, body fat percentage (%BF), and VAT were greater in forwards than backs at all 4 timepoints. Meaningful increases in VAT across the season, were observed in 37.5% of backs and 53.6% of forwards. There was a positive linear relationship between lean mass and total mass, up to 116.04 kg total mass. Beyond this threshold, lean mass accumulation reduced and %BF and VAT mass increased. There were significant relationships between %BF, VAT, and BMI (p < 0.001), but no physiological relevant pattern was discerned. CONCLUSIONS: Despite regular high-intensive exercise and individually tailored dietary control across a professional rugby season, players from both playing positions demonstrated increases in VAT, although the cause remains unknown. Our findings indicate the importance of monitoring VAT in athletes alongside standard measures of body composition. Additionally, our findings suggest there may be an upper threshold of body mass beyond which lean mass may not increase further and instead %BF and VAT are more likely to accumulate. Further research is required to identify how increasing player size may impact long-term cardiometabolic health given the known links between VAT and cardiometabolic risk.

Interlocking directorates in Irish companies using a latent space model for bipartite networks.

We analyze the temporal bipartite network of the leading Irish companies and their directors from 2003 to 2013, encompassing the end of the Celtic Tiger boom and the ensuing financial crisis in 2008. We focus on the evolution of company interlocks, whereby a company director simultaneously sits on two or more boards. We develop a statistical model for this dataset by embedding the positions of companies and directors in a latent space. The temporal evolution of the network is modeled through three levels of Markovian dependence: one on the model parameters, one on the companies' latent positions, and one on the edges themselves. The model is estimated using Bayesian inference. Our analysis reveals that the level of interlocking, as measured by a contraction of the latent space, increased before and during the crisis, reaching a peak in 2009, and has generally stabilized since then.