Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2.

More than one in ten Australians live in poverty, with many relying on government provided support and emergency payments. These payments are insufficient to cover basic costs of living, and as a result, many people are forced to engage with emergency and community food assistance. The aim of this article is to explore the experiences of those who, despite being in receipt of an Australian welfare payment and engaged with the welfare system, rely on charitable food assistance for some or all of their weekly food supply. Interviews were conducted with seventy-eight people and were thematically analysed. The main findings of this study are the significant challenges faced by people who are on very low incomes when navigating the government-provided welfare and non-government charity systems and the insufficiency of the welfare system in providing income to meet basic costs of living.

This research project was carried out by the Swedish Dairy Association as part of the development of a ‘Scheme for Animal Welfare’. As a first step, on-farm, animal-based measurements on calves, young stock and cows in 55 randomly selected herds were performed. Nine animal-based measurements formed the basis for a classification of welfare at the herd level: cleanliness and body condition in calves, cows and young stock, in combination with lameness, injuries/inflammations and rising behaviour which were recorded for cows only. The threshold (gold standard) for being a case herd with poor welfare, was a score lying among the worst 10% on two or more of the nine welfare measurements. Thirteen of the 55 herds were cases fitting this criterion. As a second step, 65 potential welfare indicators from seven different focus areas in a pre-collected data register were identified by expert opinion. In the final step, the extent to which suggested potential welfare indicators predicted farms’ risk of having poor welfare according to the definition was assessed. The final set of welfare indicators, taken from the national dairy database, included two fertility measures and calf mortality, and it correctly classified 77% of the herds, with a sensitivity of 0.62. The inclusion of cow and young stock mortality led to it correctly classifying 76% of the herds with a sensitivity of 0.77. We propose that this approach could be useful in helping to allocate advisory services to farms at risk of poor welfare.

Second language (L2) researchers have long acknowledged the role of language anxiety in communication processes, such that learners with greater language anxiety tend to be less willing to engage in communication. However, little research has explored links between L2 speakers’ perceptions of conversation and dynamic measures of anxiety. Therefore, this study measured 60 L2 English speakers’ galvanic skin response (a physiological index of anxiety) during conversation. After the conversation, speakers evaluated themselves and their partner in terms of speech fluency and comprehensibility, engagement, and anxiety, and responded to trait-anxiety and social network questionnaires. Correlational analyses explored relationships between speakers’ trait-anxiety, social network characteristics, self- and peer-perceptions and five levels of physiological response during conversation. Findings revealed that high arousal during interaction was related to speakers’ negative self-perceptions of speech fluency and negative perceptions of their partner’s engagement. Implications are discussed regarding state-anxiety as triggered by partner- or task-specific experiences.