Accelerating the Pool-Adjacent-Violators Algorithm for Isotonic Distributional Regression.

In the context of estimating stochastically ordered distribution functions, the pool-adjacent-violators algorithm (PAVA) can be modified such that the computation times are reduced substantially. This is achieved by studying the dependence of antitonic weighted least squares fits on the response vector to be approximated.

From spontaneous to strategic natural window ventilation: Improving indoor air quality in Swiss schools.

Natural window ventilation is frequently employed in schools in Europe and often leads to inadequate levels of human bioeffluents. However, intervention studies that verify whether recommended ventilation targets can be achieved in practice with reasonable ventilation regimes and that are also suitable for countries with cold winters are practically non-existent. To explore the initial situation in Switzerland we carried out carbon dioxide (CO2) measurements during the winter in 100 classrooms, most of which (94%) had natural window ventilation. In more than two thirds of those, the hygienic limit value of 2000 ppm specified for CO2 in the Swiss Standard SN 520180 (2014) was exceeded. To improve ventilation behavior, an intervention was implemented in 23 classrooms during the heating season. Ventilation was performed exclusively during breaks (to avoid discomfort from cold and drafts), efficiently, and only for as long as was necessary to achieve the ventilation objective of compliance with the hygienic limit value (strategic ventilation). The intervention included verbal and written instructions, awareness-raising via a school lesson and an interactive tool for students, which was also used to estimate the required duration of ventilation. CO2 exposure was significantly reduced in pilot classes (Wilcoxon signed-rank test, p = 3.815e-06). Median CO2 levels decreased from 1600 ppm (control group) to 1097 ppm (intervention group), and the average proportion of teaching time at 400-1400 ppm CO2 increased from 40% to 70%. The duration of ventilation was similar to spontaneous natural window ventilation (+5.8%). Stricter ventilation targets are possible. The concept of the intervention is suitable for immediate adoption in schools with natural window ventilation for a limited period, pending the installation of a mechanical ventilation system. The easy integration of this intervention into everyday school life promotes compliance, which is particularly important during the COVID-19 pandemic.

Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units.

RATIONALE: The COVID-19 pandemic induces considerable strain on intensive care unit resources. OBJECTIVES: We aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic. METHODS: We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry. MEASUREMENTS: The RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19. MAIN RESULTS: The model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources. CONCLUSION: A new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios.