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Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model evidence or the Bayes factor. Explicit expressions for these quantities are available for the simplest linear models with unrealistic priors, but in most cases, direct computation is impossible. In practice, Markov Chain Monte Carlo approaches are widely used, such as sequential Monte Carlo, but it is not always clear how well such techniques perform. We present a method for estimation of the log model evidence, by an intermediate marginalisation over non-variance parameters. This reduces the dimensionality of any Monte Carlo sampling algorithm, which in turn yields more consistent estimates. The aim of this paper is to show how this framework fits together and works in practice, particularly on data with hierarchical structure. We illustrate this method on simulated multilevel data and on a popular dataset containing levels of radon in homes in the US state of Minnesota. [ABSTRACT FROM AUTHOR]

Porcine reproductive and respiratory syndrome (PRRS) is an extremely contagious disease that causes great damage to the U.S. pork industry. PRRS is not subject to official control in the U.S., but most producers adopt control strategies, including vaccination. However, the PRRS virus mutates frequently, facilitating its ability to infect even vaccinated animals. In this paper we analyze how increased vaccination on sow farms reduces PRRS losses and when vaccination is profitable. We develop a SIR model to simulate the spread of an outbreak between and within swine farms located in a region of Minnesota. Then, we estimate economic losses due to PRRS and calculate the benefits of vaccination. We find that increased vaccination of sow farms increases the private profitability of vaccination, and also transmits positive externalities to farms that do not vaccinate. Although vaccination reduces industry losses, a low to moderate vaccine efficacy implies that large PRRS losses remain, even on vaccinated farms. Our approach provides useful insight into the dynamics of an endemic animal disease and the benefits of different vaccination regimens. [ABSTRACT FROM AUTHOR]

Aim: To assess the effectiveness of an infection control protocol developed to mitigate the spread of COVID-19 at two multi-week residential summer camps in 2021. Subject and methods: Data were collected from 595 camp attendees and staff members at two wilderness camps in Northern Minnesota. Testing was undertaken in all unvaccinated campers before arrival at camp, on day 4 of camp, and in the event of respiratory symptoms. Campers were limited to cohorts during the first 4 days of camp and wore masks indoors. The number of positive COVID-19 cases measured the efficacy of the protocol. Results: The testing and cohorting protocol successfully prevented the spread of COVID-19 among campers and staff. During the first summer session, there were zero positive cases of COVID-19 among 257 campers and 127 staff. During the second summer session, compliance with the protocol limited the spread of COVID-19 to just three individuals of 266 campers and 129 staff. Maintaining cohorts at arrival limited spread from a single positive case to only two tent companions. Conclusion: The testing and cohorting protocol limited the spread of COVID-19 among residential summer wilderness campers and staff. Post-arrival testing ensured newly acquired virus was limited in spread before COVID-19 precautions were relaxed on camp day 5. A strict evidence-based cohorting protocol limited in-camp spread and allowed for a successful summer camp season. The usefulness of this protocol with an evolving pandemic, increasing vaccination rates, and virus variants could have implications for future practice. [ABSTRACT FROM AUTHOR]

The closure of all sports facilities during the two lockdowns in Germany favoured a reduction of leisure time physical activity during the COVID-19 pandemic. The aim of this study was to compare leisure time physical activity during the 1st and 2nd lockdown and to examine exercise performance before and after resumption of exercise. Leisure time physical activity was measured by the Longitudinal Urban Cohort Ageing Study (LUCAS) functional ability index and energy expenditure in the Minnesota Leisure Time Physical Activity Questionnaire. Participants' exercise performance was extracted from a chip-controlled fitness circuit. Differences were tested for statistical significance using Friedman tests. 35 participants above 60 years were included from the Oldenburg area (20 women, 15 men, mean age and standard deviation 71±6 years). The decline in energy expenditure was higher during the 2nd lockdown (1st lockdown: Median -55.7 kcal.day-1, Q0.25−121.3 kcal.day-1, Q0.75 132.9 kcal.day-1; 2nd lockdown: Median -119.7 kcal.day-1, Q0.25−255.6 kcal.day-1, Q0.75−65.1 kcal.day-1; Friedman test: p<0.001, n = 35, W = 0.262). The time spent in the fitness circuit decreased from lockdown to lockdown as well as the number of participants exercising there. Intense activities were performed during the two lockdowns by only 7 and 3 participants, respectively, and were not resumed by two-thirds of the participants after the 2nd lockdown. During the 1st lockdown, exercise performance on resistance exercise devices increased in most of them, while it decreased by 1 to 7% during the 2nd lockdown. The lockdowns limited leisure time physical activity in older adults. This was more pronounced during the lockdown in winter 2020/2021, when participants engaged less in outdoor activities. Therefore, measures should be taken to maintain physical activity and muscle strength, especially during winter months, with a home-based training, if visiting gyms is not possible. [ABSTRACT FROM AUTHOR]

Visible disruptions of appropriate food distribution for end consumers during the onset of the COVID-19 pandemic prompted calls for an urgent, renewed look at how the U.S. agri-food system is impacted by and responds to pandemics, natural disasters, and human-made crises. Previous studies suggest the COVID-19 pandemic yielded uneven impacts across agri-food supply chain segments and regions. For a rigorously comparable assessment of the impact of COVID-19 on agri-food businesses, a survey was administered from February to April 2021 to five segments of the agri-food supply chain in three study regions (California, Florida, and the two-state region of Minnesota-Wisconsin). Results (N = 870) measuring the self-reported changes in quarterly business revenue in 2020 compared to businesses' typical experience pre-COVID-19 suggest significant differences across supply chain segments and regions. In the Minnesota-Wisconsin region, restaurants took the largest hit and the upstream supply chains were relatively unaffected. In California, however, the negative impacts were felt throughout the supply chain. Two factors likely contributed to regional differences: (1) regional disparities in pandemic evolution and governance and (2) structural differences in regional agri-food systems. Regionalized and localized planning and the development of best-practices will be necessary for the U.S. agri-food system to enhance preparedness for and resilience to future pandemics, natural disasters, and human-made crises. [ABSTRACT FROM AUTHOR]

Given recent and abrupt declines in the abundance of moose (Alces alces) throughout parts of Minnesota and elsewhere in North America, accurately estimating statewide population trends and demographic parameters is a high priority for their continued management and conservation. Statistical population reconstruction using integrated population models provides a flexible framework for combining information from multiple studies to produce robust estimates of population abundance, recruitment, and survival. We used this framework to combine aerial survey data and survival data from telemetry studies to recreate trends and demographics of moose in northeastern Minnesota, USA, from 2005 to 2020. Statistical population reconstruction confirmed the sharp decline in abundance from an estimated 7,841 (90% CI = 6,702–8,933) in 2009 to 3,386 (90% CI = 2,681–4,243) animals in 2013, but also indicated that abundance has remained relatively stable since then, except for a slight decline to 3,163 (90% CI = 2,403–3,718) in 2020. Subsequent stochastic projection of the population from 2021 to 2030 suggests that this modest decline will continue for the next 10 years. Both annual adult survival and per-capita recruitment (number of calves that survived to 1 year per adult female alive during the previous year) decreased substantially in years 2005 and 2019, from 0.902 (SE = 0.043) to 0.689 (SE = 0.061) and from 0.386 (SE = 0.030) to 0.303 (SE = 0.051), respectively. Sensitivity analysis revealed that moose abundance was more sensitive to fluctuations in adult survival than recruitment; thus, we conclude that the steep decline in 2013 was driven primarily by decreasing adult survival. Our analysis demonstrates the potential utility of using statistical population reconstruction to monitor moose population trends and to identify population declines more quickly. Future studies should focus on providing better estimates of per-capita recruitment, using pregnancy rates and calf survival, which can then be incorporated into reconstruction models to help improve estimates of population change through time. [ABSTRACT FROM AUTHOR]

The microbial communities of lake sediments have the potential to serve as valuable bioindicators and integrators of watershed land-use and water quality; however, the relative sensitivity of these communities to physio-chemical and geographical parameters must be demonstrated at taxonomic resolutions that are feasible by current sequencing and bioinformatic approaches. The geologically diverse and lake-rich state of Minnesota (USA) is uniquely situated to address this potential because of its variability in ecological region, lake type, and watershed land-use. In this study, we selected twenty lakes with varying physio-chemical properties across four ecological regions of Minnesota. Our objectives were to (i) evaluate the diversity and composition of the bacterial community at the sediment-water interface and (ii) determine how lake location and watershed land-use impact aqueous chemistry and influence bacterial community structure. Our 16S rRNA amplicon data from lake sediment cores, at two depth intervals, data indicate that sediment communities are more likely to cluster by ecological region rather than any individual lake properties (e.g., trophic status, total phosphorous concentration, lake depth). However, composition is tied to a given lake, wherein samples from the same core were more alike than samples collected at similar depths across lakes. Our results illustrate the diversity within lake sediment microbial communities and provide insight into relationships between taxonomy, physicochemical, and geographic properties of north temperate lakes. [ABSTRACT FROM AUTHOR]