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Returning universities to full on-campus operations while the COVID-19 pandemic is ongoing has been a controversial discussion in many countries. The risk of large outbreaks in dense course settings is contrasted by the benefits of in-person teaching. Transmission risk depends on a range of parameters, such as vaccination coverage, number of contacts and adoption of non-pharmaceutical intervention measures (NPIs). Due to the generalised academic freedom in Europe, many universities are asked to autonomously decide on and implement intervention measures and regulate on-campus operations. In the context of rapidly changing vaccination coverage and parameters of the virus, universities often lack the scientific facts to base these decisions on. To address this problem, we analyse a calibrated, data-driven simulation of transmission dynamics of 10755 students and 974 faculty in a medium-sized university. We use a co-location network reconstructed from student enrolment data and calibrate transmission risk based on outbreak size distributions in other Austrian education institutions. We focus on actionable interventions that are part of the already existing decision-making process of universities to provide guidance for concrete policy decisions. Here we show that with the vaccination coverage of about 80% recently reported for students in Austria, universities can be safely reopened if they either mandate masks or reduce lecture hall occupancy to 50%. Our results indicate that relaxing NPIs within an organisation based on the vaccination coverage of its sub-population can be a way towards limited normalcy, even if nation wide vaccination coverage is not sufficient to prevent large outbreaks yet.

How to safely maintain open schools during a pandemic is still controversial. We aim to identify those measures that effectively control the spread of SARS-CoV-2 in Austrian schools. By control we mean that each source case infects less than one other person on average. We use Austrian data on 616 clusters involving 2,822 student-cases and 676 teacher-cases to calibrate an agent-based epidemiological model in terms of cluster size and transmission risk depending on age and clinical presentation. Considering a situation in which the B1.617.2 (delta) virus strain is dominant and parts of the population are vaccinated, we quantify the impact of non-pharmaceutical intervention measures (NPIs) such as room ventilation, reduction of class size, wearing of masks during lessons, vaccinations, and school entry testing by SARS-CoV2-antigen tests. In the tracing data we find that 40% of all clusters involved no more than two cases, and 3% of the clusters only had more than 20 cases. The younger the students, the more likely we found asymptomatic cases and teachers as the source case of the in-school transmissions. Based on this data, the model shows that different school types require different combinations of NPIs to achieve control of the infection spreading: If 80% of teachers and 50% of students are vaccinated, in primary schools, it is necessary to combine at least two of the above NPIs. In secondary schools, where contact networks of students and teachers become increasingly large and dense, a combination of at least three NPIs is needed. A sensitivity analysis indicated that poorly executed mitigation measures might increase the cluster size by a factor of more than 17 for primary schools and even higher increases are to be expected for the other school types. Our results suggest that school-type-specific combinations of NPIs together with vaccinations are necessary to allow for a controlled opening of schools under sustained community transmission of the SARS-CoV-2 delta variant. However, large clusters might still occur on an infrequent, however, regular basis.

Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making it impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision medicine approach, bringing together information streams from a variety of life domains of dairy cattle to predict eight common and economically important diseases. Dairy cows are part of a highly industrialised environment. The animals and their surroundings are closely monitored and environmental, behavioural and physiological observations are readily accessible yet seldomly integrated. We use random forest classifiers trained on data from 5,828 animals in 166 herds in Austria to predict occurrences of lameness, acute and chronic mastitis, anoestrus, ovarian cysts, metritis, ketosis (hyperketonemia) and periparturient hypocalcemia (milk fever). To assess the importance of specific cattle life domains and individual features for these predictions, we use multivariate logistic regression and feature permutation approaches. We show that disease in dairy cattle is a product of the complex interplay between a multitude of life domains such as housing, nutrition or climate, and identify a range of features that were previously not associated with increased disease risk. For example, we can predict anoestrus with high sensitivity and specificity (F1=0.72) and find that housing, feed and husbandry variables such as barn design and time on pasture are most predictive of this disease. We also find previously unknown associations of features with disease risk, for example humid conditions, which significantly decrease the odds for ketosis. Our findings pave the way towards data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

SummaryThe goal of this analysis is to estimate the effects of the diverse government intervention measures implemented to mitigate the spread of the Covid-19 epidemic. We use a process model based on a compartmental epidemiological framework Susceptible-Infected-Recovered-Dead (SIRD). Analysis of case data with such a mechanism-based model has advantages over purely phenomenological approaches because the parameters of the SIRD model can be calibrated using prior knowledge. This approach can be used to investigate how governmental interventions have affected the Covid-19-related transmission and mortality rate during the epidemic.