The airborne transmission of viruses causes tight transmission bottlenecks.

The transmission bottleneck describes the number of viral particles that initiate an infection in a new host. Previous studies have used genome sequence data to suggest that transmission bottlenecks for influenza and SARS-CoV-2 involve few viral particles, but the general principles of virus transmission are not fully understood. Here we show that, across a broad range of circumstances, tight transmission bottlenecks are a simple consequence of the physical process of airborne viral transmission. We use mathematical modelling to describe the physical process of the emission and inhalation of infectious particles, deriving the result that that the great majority of transmission bottlenecks involve few viral particles. While exceptions to this rule exist, the circumstances needed to create these exceptions are likely very rare. We thus provide a physical explanation for previous inferences of bottleneck size, while predicting that tight transmission bottlenecks prevail more generally in respiratory virus transmission.

A machine learning approach to identify important variables for distinguishing between fallers and non-fallers in older women.

Falls are a significant ongoing public health concern for older adults. At present, few studies have concurrently explored the influence of multiple measures when seeking to determine which variables are most predictive of fall risks. As such, this cross-sectional study aimed to identify those functional variables (i.e. balance, gait and clinical measures) and physical characteristics (i.e. strength and body composition) that could best distinguish between older female fallers and non-fallers, using a machine learning approach. Overall, 60 community-dwelling older women (≥65 years), retrospectively classified as fallers (n = 21) or non-fallers (n = 39), attended three data collection sessions. Data (281 variables) collected from tests in five separate domains (balance, gait, clinical measures, strength and body composition) were analysed using random forest (RF) and leave-one-variable-out partial least squares correlation analysis (LOVO PLSCA) to assess variable importance. The strongest discriminators from each domain were then aggregated into a multi-domain dataset, and RF, LOVO PLSCA, and logistic regression models were constructed to identify the important variables in distinguishing between fallers and non-fallers. These models were used to classify participants as either fallers or non-fallers, with their performance evaluated using receiver operating characteristic (ROC) analysis. The study found that it is possible to classify fallers and non-fallers with a high degree of accuracy (e.g. logistic regression: sensitivity = 90%; specificity = 87%; AUC = 0.92; leave-one-out cross-validation accuracy = 63%) using a combination of 18 variables from four domains, with the gait and strength domains being particularly informative for screening programmes aimed at assessing falls risk.

Clustering of match running and performance indicators to assess between- and within-playing position similarity in professional rugby league.

This study aimed to determine the similarity between and within positions in professional rugby league in terms of technical performance and match displacement. Here, the analyses were repeated on 3 different datasets which consisted of technical features only, displacement features only, and a combined dataset including both. Each dataset contained 7617 observations from the 2018 and 2019 Super League seasons, including 366 players from 11 teams. For each dataset, feature selection was initially used to rank features regarding their importance for predicting a player's position for each match. Subsets of 12, 11, and 27 features were retained for technical, displacement, and combined datasets for subsequent analyses. Hierarchical cluster analyses were then carried out on the positional means to find logical groupings. For the technical dataset, 3 clusters were found: (1) props, loose forwards, second-row, hooker; (2) halves; (3) wings, centres, fullback. For displacement, 4 clusters were found: (1) second-rows, halves; (2) wings, centres; (3) fullback; (4) props, loose forward, hooker. For the combined dataset, 3 clusters were found: (1) halves, fullback; (2) wings and centres; (3) props, loose forward, hooker, second-rows. These positional clusters can be used to standardise positional groups in research investigating either technical, displacement, or both constructs within rugby league.

A video analysis framework for the rugby league tackle.

Rugby league tackle video analysis research typically uses technical criteria from coaching cues or tackle variables from rugby union. As such, content validity and relevance could be questioned. A video analysis framework that establishes appropriate variables for rugby league is therefore required. The study aimed to adopt a 5-stage process to establish a video analysis framework for the rugby league tackle, which was content valid, relevant and reliable.The 5-stage process included 1) creation of draft video analysis framework, using available rugby tackle research, 2) expert group recruitment and critique, 3) refinement of framework to establish content validity, 4) response process validity task and agreement within expert group, 5) intra- and inter-reliability testing using Kappa statistics.The agreed framework comprised six phases including; tackle event, defensive start point, pre-contact, initial contact, post-contact and play-the-ball. Within the identified phases, 63 variables were established. The intra- and inter-reliability testing resulted in strong agreement within all phases.The video analysis framework can be used in rugby league tackle research, categorising complex tackle events, such as injurious or optimal tackles, improving both player welfare and performance. The application of the framework to future rugby league research will increase coherence and usefulness of research findings.

The investigation of the cerebral venous system in multiple sclerosis.

Bateman et al. (2021) shows that multiple sclerosis (MS) is strongly associated with raised pressure in the superior sagittal sinus (SSS) and increased jugular bulb height above the sigmoid sinus. These findings are consistent with an increased aqueductal CSF pulse, as previously described in MS. They reinforce the hypothesis that intracranial compliance is reduced in MS and that internal jugular vein abnormalities contribute to SSS hypertension. However, the contribution of this to the pathophysiology of MS has not been established. Further investigation is therefore needed to determine what role, if any, these changes play in the complex puzzle of MS.

Latent variable dose-response modelling of external training load measures and musculoskeletal responses in elite rugby league players.

Establishing dose-response relationships between training load and fatigue can help the planning of training. The aim was to establish the relative importance of external training load measurements to relate to the musculoskeletal response on a group and individual player level. Sixteen elite male rugby league players were monitored across three seasons. Two- to seven-day exponential weighted averages (EWMA) were calculated for total distance, and individualised speed thresholds (via 30-15 Intermittent Fitness Test) derived from global positioning systems. The sit and reach, dorsiflexion lunge, and adductor squeeze tests represented the musculoskeletal response. Partial least squares and repeated measures correlation analyses established the relative importance of training load measures and then investigated their relationship to the collective musculoskeletal response for individual players through the construction of latent variables. On a group level, 2- and 3-day EWMA total distance had the highest relative importance to the collective musculoskeletal response (p < 0.0001). However, the magnitude of relationships on a group (r value = 0.20) and individual (r value = 0.06) level were trivial to small. The lack of variability in the musculoskeletal response over time suggest practitioners adopting such measures to understand acute musculoskeletal fatigue responses should do so with caution.

A psychrometric model to assess the biological decay of the SARS-CoV-2 virus in aerosols.

There is increasing evidence that the 2020 COVID-19 pandemic has been influenced by variations in air temperature and humidity. However, the impact that these environmental parameters have on survival of the SARS-CoV-2 virus has not been fully characterised. Therefore, an analytical study was undertaken using published data to develop a psychrometric model to assess the biological decay rate of the virus in aerosols. This revealed that it is possible to describe with reasonable accuracy (R 2 = 0.718, p < 0.001) the biological decay constant for the SARS-CoV-2 virus using a regression model with enthalpy, vapour pressure and specific volume as predictors. Applying this to historical meteorological data from London, Paris and Milan over the pandemic period, produced results which indicate that the average half-life of the virus in aerosols outdoors was in the region 13-22 times longer in March 2020, when the outbreak was accelerating, than it was in August 2020 when epidemic in Europe was at its nadir. However, indoors, this variation is likely to be much less. As such, this suggests that changes in virus survivability due the variations in the psychrometric qualities of the air might influence the transmission of SARS-CoV-2.

Prediction of Upper Respiratory Illness Using Salivary Immunoglobulin A in Youth Athletes.

PURPOSE: To evaluate the relative importance and predictive ability of salivary immunoglobulin A (s-IgA) measures with regards to upper respiratory illness (URI) in youth athletes. METHODS: Over a 38-week period, 22 youth athletes (age = 16.8 [0.5] y) provided daily symptoms of URI and 15 fortnightly passive drool saliva samples, from which s-IgA concentration and secretion rate were measured. Kernel-smoothed bootstrapping generated a balanced data set with simulated data points. The random forest algorithm was used to evaluate the relative importance (RI) and predictive ability of s-IgA concentration and secretion rate with regards to URI symptoms present on the day of saliva sampling (URIday), within 2 weeks of sampling (URI2wk), and within 4 weeks of sampling (URI4wk). RESULTS: The percentage deviation from average healthy s-IgA concentration was the most important feature for URIday (median RI 1.74, interquartile range 1.41-2.07). The average healthy s-IgA secretion rate was the most important feature for URI4wk (median RI 0.94, interquartile range 0.79-1.13). No feature was clearly more important than any other when URI symptoms were identified within 2 weeks of sampling. The values for median area under the curve were 0.68, 0.63, and 0.65 for URIday, URI2wk, and URI4wk, respectively. CONCLUSIONS: The RI values suggest that the percentage deviation from average healthy s-IgA concentration may be used to evaluate the short-term risk of URI, while the average healthy s-IgA secretion rate may be used to evaluate the long-term risk. However, the results show that neither s-IgA concentration nor secretion rate can be used to accurately predict URI onset within a 4-week window in youth athletes.

The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime.

The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation.

A league-wide investigation into variability of rugby league match running from 322 Super League games.

Purpose:This study investigated sources of variability in the overall and phase-specific running match characteristics in elite rugby league. Methods:Microtechnology data were collected from 11 Super League (SL) teams, across 322 competitive matches within the 2018 and 2019 seasons. Total distance, high-speed running (HSR) distance (>5.5 m·s-1), average speed, and average acceleration were assessed. Variability was determined using linear mixed models, with random intercepts specified for player, position, match, and club. Results:Large within-player coefficients of variation (CV) were found across whole match, ball-in-play, attack and defence for total distance (CV range = 24% to 35%) and HSR distance (37% to 96%), whereas small to moderate CVs (≤10%) were found for average speed and average acceleration. Similarly, there was higher between-player, -position, and -match variability in total distance and HSR distance when compared with average speed and average acceleration across all periods. All metrics were stable between-teams (≤5%), except HSR distance (16% to 18%). The transition period displayed the largest variability of all phases, especially for distance (up to 42%) and HSR distance (up to 165%). Conclusion:Absolute measures of displacement display large within-player and between-player, -position, and -match variability, yet average acceleration and average speed remain relatively stable across all match-periods.

The use of technical-tactical and physical performance indicators to classify between levels of match-play in elite rugby league.

This study aimed to identify which physical and technical-tactical performance indicators (PI) can classify between levels of rugby league match-play. Data were collected from 46 European Super League (ESL) and 36 under-19 Academy (Academy) level matches over two seasons. Thirty-one ESL players and 41 Academy players participated. Microtechnology units were used to analyse the physical PI and matches were videoed and coded for individual technical-tactical PI, resulting in 157 predictor variables. Data were split into training and testing datasets. Random forests (RF) were built to reduce the dimensionality of the data, identify variables of importance and build classification models. To aid practical interpretation, conditional inference (CI) trees were built. Nine variables were identified as most important for backs, classifying between levels with 83% (RF) and 78% (CI tree) accuracy. The combination of variables with the highest classification rate was PlayerLoad2D, PlayerLoadSLOW per Kg body mass and high-speed running distance. Four variables were identified as most important for forwards, classifying with 68% (RF) and 64% (CI tree) accuracy. Defensive play-the-ball losses alone had the highest classification rate for forwards. The identified PI and their unique combinations can be developed during training to aid in progression through the rugby league playing pathway.

Upper-room ultraviolet air disinfection might help to reduce COVID-19 transmission in buildings: a feasibility study.

As the world's economies come out of the lockdown imposed by the COVID-19 pandemic, there is an urgent need for technologies to mitigate COVID-19 transmission in confined spaces such as buildings. This feasibility study looks at one such technology, upper-room ultraviolet (UV) air disinfection, that can be safely used while humans are present in the room space, and which has already proven its efficacy as an intervention to inhibit the transmission of airborne diseases such as measles and tuberculosis. Using published data from various sources, it is shown that the SARS-CoV-2 virus, the causative agent of COVID-19, is highly likely to be susceptible to UV-C damage when suspended in air, with a UV susceptibility constant likely to be in the region 0.377-0.590 m2/J, similar to that for other aerosolised coronaviruses. As such, the UV-C flux required to disinfect the virus is expected to be acceptable and safe for upper-room applications. Through analysis of expected and worst-case scenarios, the efficacy of the upper-room UV-C approach for reducing COVID-19 transmission in confined spaces (with moderate but sufficient ceiling height) is demonstrated. Furthermore, it is shown that with SARS-CoV-2, it should be possible to achieve high equivalent air change rates using upper-room UV air disinfection, suggesting that the technology might be particularly applicable to poorly ventilated spaces.

Central venous pressure estimation from ultrasound assessment of the jugular venous pulse.

OBJECTIVES: Acquiring central venous pressure (CVP), an important clinical parameter, requires an invasive procedure, which poses risk to patients. The aim of the study was to develop a non-invasive methodology for determining mean-CVP from ultrasound assessment of the jugular venous pulse. METHODS: In thirty-four adult patients (age = 60 ± 12 years; 10 males), CVP was measured using a central venous catheter, with internal jugular vein (IJV) cross-sectional area (CSA) variation along the cardiac beat acquired using ultrasound. The resultant CVP and IJV-CSA signals were synchronized with electrocardiogram (ECG) signals acquired from the patients. Autocorrelation signals were derived from the IJV-CSA signals using algorithms in R (open-source statistical software). The correlation r-values for successive lag intervals were extracted and used to build a linear regression model in which mean-CVP was the response variable and the lagging autocorrelation r-values and mean IJV-CSA, were the predictor variables. The optimum model was identified using the minimum AIC value and validated using 10-fold cross-validation. RESULTS: While the CVP and IJV-CSA signals were poorly correlated (mean r = -0.018, SD = 0.357) due to the IJV-CSA signal lagging behind the CVP signal, their autocorrelation counterparts were highly positively correlated (mean r = 0.725, SD = 0.215). Using the lagging autocorrelation r-values as predictors, mean-CVP was predicted with reasonable accuracy (r2 = 0.612), with a mean-absolute-error of 1.455 cmH2O, which rose to 2.436 cmH2O when cross-validation was performed. CONCLUSIONS: Mean-CVP can be estimated non-invasively by using the lagged autocorrelation r-values of the IJV-CSA signal. This new methodology may have considerable potential as a clinical monitoring and diagnostic tool.

The relative contribution of training intensity and duration to daily measures of training load in professional rugby league and union.

This study examined the relative contribution of exercise duration and intensity to team-sport athlete's training load. Male, professional rugby league (n = 10) and union (n = 22) players were monitored over 6- and 52-week training periods, respectively. Whole-session (load) and per-minute (intensity) metrics were monitored (league: session rating of perceived exertion training load [sRPE-TL], individualised training impulse, total distance, BodyLoad™; union: sRPE-TL, total distance, high-speed running distance, PlayerLoad™). Separate principal component analyses were conducted on the load and intensity measures to consolidate raw data into principal components (PC, k = 4). The first load PC captured 70% and 74% of the total variance in the rugby league and rugby union datasets, respectively. Multiple linear regression subsequently revealed that session duration explained 73% and 57% of the variance in first load PC, respectively, while the four intensity PCs explained an additional 24% and 34%, respectively. Across two professional rugby training programmes, the majority of the variability in training load measures was explained by session duration (~60-70%), while a smaller proportion was explained by session intensity (~30%). When modelling the training load, training intensity and duration should be disaggregated to better account for their between-session variability.

Time to embrace the complexity when analysing GPS data? A systematic review of contextual factors on match running in rugby league.

This systematic review aimed to identify and summarise associations between currently identified contextual factors and match running in senior male professional rugby league. Eligible articles included at least one contextual factor and used GPS to measure at least one displacement variable within competitive senior, male, professional rugby league matches. Of the 15 included studies, the identified contextual factors were grouped into factors related to individual characteristics (n = 3), match result (n = 4), team strength (n = 2), opposition strength (n = 3), match conditions (n = 6), technical and tactical demands (n = 6), spatial and temporal characteristics (n = 7), and nutrition (n = 1). Speed was the most commonly reported measure of match running (100%), followed by distance (47%), and acceleration (20%). Inconsistencies were found between studies for most contextual factors on match running. Higher speeds were generally associated with higher fitness, encountered earlier in the match and whilst defending. All 15 studies utilised a univariate approach to quantify associations of a contextual factor. The inconsistencies found in the associations of given contextual factors highlight the complex and multi-faceted nature of match running. Therefore, practitioners should consider contextual factors when analysing and interpreting GPS data.

Achieving a desired training intensity through the prescription of external training load variables in youth sport: More pieces to the puzzle required.

Identifying the external training load variables which influence subjective internal response will help reduce the mismatch between coach-intended and athlete-perceived training intensity. Therefore, this study aimed to reduce external training load measures into distinct principal components (PCs), plot internal training response (quantified via session Rating of Perceived Exertion [sRPE]) against the identified PCs and investigate how the prescription of PCs influences subjective internal training response. Twenty-nine school to international level youth athletes wore microtechnology units for field-based training sessions. SRPE was collected post-session and assigned to the microtechnology unit data for the corresponding training session. 198 rugby union, 145 field hockey and 142 soccer observations were analysed. The external training variables were reduced to two PCs for each sport cumulatively explaining 91%, 96% and 91% of sRPE variance in rugby union, field hockey and soccer, respectively. However, when internal response was plotted against the PCs, the lack of separation between low-, moderate- and high-intensity training sessions precluded further analysis as the prescription of the PCs do not appear to distinguish subjective session intensity. A coach may therefore wish to consider the multitude of physiological, psychological and environmental factors which influence sRPE alongside external training load prescription.

Hidden dynamics of soccer leagues: The predictive 'power' of partial standings.

OBJECTIVES: Soccer leagues reflect the partial standings of the teams involved after each round of competition. However, the ability of partial league standings to predict end-of-season position has largely been ignored. Here we analyze historical partial standings from English soccer to understand the mathematics underpinning league performance and evaluate the predictive 'power' of partial standings. METHODS: Match data (1995-2017) from the four senior English leagues was analyzed, together with random match scores generated for hypothetical leagues of equivalent size. For each season the partial standings were computed and Kendall's normalized tau-distance and Spearman r-values determined. Best-fit power-law and logarithmic functions were applied to the respective tau-distance and Spearman curves, with the 'goodness-of-fit' assessed using the R2 value. The predictive ability of the partial standings was evaluated by computing the transition probabilities between the standings at rounds 10, 20 and 30 and the final end-of-season standings for the 22 seasons. The impact of reordering match fixtures was also evaluated. RESULTS: All four English leagues behaved similarly, irrespective of the teams involved, with the tau-distance conforming closely to a power law (R2>0.80) and the Spearman r-value obeying a logarithmic function (R2>0.87). The randomized leagues also conformed to a power-law, but had a different shape. In the English leagues, team position relative to end-of-season standing became 'fixed' much earlier in the season than was the case with the randomized leagues. In the Premier League, 76.9% of the variance in the final standings was explained by round-10, 87.0% by round-20, and 93.9% by round-30. Reordering of match fixtures appeared to alter the shape of the tau-distance curves. CONCLUSIONS: All soccer leagues appear to conform to mathematical laws, which constrain the league standings as the season progresses. This means that partial standings can be used to predict end-of-season league position with reasonable accuracy.

Visualizing the Complexity of the Athlete-Monitoring Cycle Through Principal-Component Analysis.

PURPOSE: To discuss the use of principal-component analysis (PCA) as a dimension-reduction and visualization tool to assist in decision making and communication when analyzing complex multivariate data sets associated with the training of athletes. CONCLUSIONS: Using PCA, it is possible to transform a data matrix into a set of orthogonal composite variables called principal components (PCs), with each PC being a linear weighted combination of the observed variables and with all PCs uncorrelated to each other. The benefit of transforming the data using PCA is that the first few PCs generally capture the majority of the information (ie, variance) contained in the observed data, with the first PC accounting for the highest amount of variance and each subsequent PC capturing less of the total information. Consequently, through PCA, it is possible to visualize complex data sets containing multiple variables on simple 2D scatterplots without any great loss of information, thereby making it much easier to convey complex information to coaches. In the future, athlete-monitoring companies should integrate PCA into their client packages to better support practitioners trying to overcome the challenges associated with multivariate data analysis and interpretation. In the interim, the authors present here an overview of PCA and associated R code to assist practitioners working in the field to integrate PCA into their athlete-monitoring process.

Overcoming the problem of multicollinearity in sports performance data: A novel application of partial least squares correlation analysis.

OBJECTIVES: Professional sporting organisations invest considerable resources collecting and analysing data in order to better understand the factors that influence performance. Recent advances in non-invasive technologies, such as global positioning systems (GPS), mean that large volumes of data are now readily available to coaches and sport scientists. However analysing such data can be challenging, particularly when sample sizes are small and data sets contain multiple highly correlated variables, as is often the case in a sporting context. Multicollinearity in particular, if not treated appropriately, can be problematic and might lead to erroneous conclusions. In this paper we present a novel 'leave one variable out' (LOVO) partial least squares correlation analysis (PLSCA) methodology, designed to overcome the problem of multicollinearity, and show how this can be used to identify the training load (TL) variables that influence most 'end fitness' in young rugby league players. METHODS: The accumulated TL of sixteen male professional youth rugby league players (17.7 ± 0.9 years) was quantified via GPS, a micro-electrical-mechanical-system (MEMS), and players' session-rating-of-perceived-exertion (sRPE) over a 6-week pre-season training period. Immediately prior to and following this training period, participants undertook a 30-15 intermittent fitness test (30-15IFT), which was used to determine a players 'starting fitness' and 'end fitness'. In total twelve TL variables were collected, and these along with 'starting fitness' as a covariate were regressed against 'end fitness'. However, considerable multicollinearity in the data (VIF >1000 for nine variables) meant that the multiple linear regression (MLR) process was unstable and so we developed a novel LOVO PLSCA adaptation to quantify the relative importance of the predictor variables and thus minimise multicollinearity issues. As such, the LOVO PLSCA was used as a tool to inform and refine the MLR process. RESULTS: The LOVO PLSCA identified the distance accumulated at very-high speed (>7 m·s-1) as being the most important TL variable to influence improvement in player fitness, with this variable causing the largest decrease in singular value inertia (5.93). When included in a refined linear regression model, this variable, along with 'starting fitness' as a covariate, explained 73% of the variance in v30-15IFT 'end fitness' (p<0.001) and eliminated completely any multicollinearity issues. CONCLUSIONS: The LOVO PLSCA technique appears to be a useful tool for evaluating the relative importance of predictor variables in data sets that exhibit considerable multicollinearity. When used as a filtering tool, LOVO PLSCA produced a MLR model that demonstrated a significant relationship between 'end fitness' and the predictor variable 'accumulated distance at very-high speed' when 'starting fitness' was included as a covariate. As such, LOVO PLSCA may be a useful tool for sport scientists and coaches seeking to analyse data sets obtained using GPS and MEMS technologies.