In the paper, we propose a semiparametric framework for modeling the COVID-19 pandemic. The stochastic part of the framework is based on Bayesian inference. The model is informed by the actual COVID-19 data and the current epidemiological findings about the disease. The framework combines many available data sources (number of positive cases, number of patients in hospitals and in intensive care, etc.) to make outputs as accurate as possible and incorporates the times of non-pharmaceutical governmental interventions which were adopted worldwide to slow-down the pandemic. The model estimates the reproduction number of SARS-CoV-2, the number of infected individuals and the number of patients in different disease progression states in time. It can be used for estimating current infection fatality rate, proportion of individuals not detected and short term forecasting of important indicators for monitoring the state of the healthcare system. With the prediction of the number of patients in hospitals and intensive care units, policy makers could make data driven decisions to potentially avoid overloading the capacities of the healthcare system. The model is applied to Slovene COVID-19 data showing the effectiveness of the adopted interventions for controlling the epidemic by reducing the reproduction number of SARS-CoV-2. It is estimated that the proportion of infected people in Slovenia was among the lowest in Europe (0.350%, 90% CI [0.245–0.573]%), that infection fatality rate in Slovenia until the end of first wave was 1.56% (90% CI [0.94–2.21]%) and the proportion of unidentified cases was 88% (90% CI [83–93]%). The proposed framework can be extended to more countries/regions, thus allowing for comparison between them. One such modification is exhibited on data for Slovene hospitals.

The Mann–Whitney test is a commonly used non-parametric alternative of the two-sample t-test. Despite its frequent use, it is only rarely accompanied with confidence intervals of an effect size. If reported, the effect size is usually measured with the difference of medians or the shift of the two distribution locations. Neither of these two measures directly coincides with the test statistic of the Mann–Whitney test, so the interpretation of the test results and the confidence intervals may be importantly different. In this paper, we focus on the probability that random variable X is lower than random variable Y. This measure is often referred to as the degree of overlap or the probabilistic index; it is in one-to-one relationship with the Mann–Whitney test statistic. The measure equals the area under the ROC curve. Several methods have been proposed for the construction of the confidence interval for this measure, and we review the most promising ones and explain their ideas. We study the properties of different variance estimators and small sample problems of confidence intervals construction. We identify scenarios in which the existing approaches yield inadequate coverage probabilities. We conclude that the DeLong variance estimator is a reliable option regardless of the scenario, but confidence intervals should be constructed using the logit scale to avoid values above 1 or below 0 and the poor coverage probability that follows. A correction is needed for the case when all values from one sample are smaller than the values of the other. We propose a method that improves the coverage probability also in these cases.

Background: It is well recognized that dental procedures represent a potential way of infection transmission. With the COVID-19 pandemic, the focus of dental procedure associated transmission has rapidly changed from bacteria to viruses. The aim was to develop an experimental setup for testing the spread of viruses by ultrasonic scaler (USS) generated dental spray and evaluate its mitigation by antiviral coolants. Methods: In a virus transmission tunnel, the dental spray was generated by USS with saline coolant and suspension of Equine Arteritis Virus (EAV) delivered to the USS tip. Virus transmission by settled droplets was evaluated with adherent RK13 cell lines culture monolayer. The suspended droplets were collected by a cyclone aero-sampler. Antiviral activity of 0.25% NaOCl and electrolyzed oxidizing water (EOW) was tested using a suspension test. Antiviral agents' transmission prevention ability was evaluated by using them as a coolant. Results: In the suspension test with 0.25% NaOCl or EOW, the TCID50/mL was below the detection limit after 5 seconds. With saline coolant, the EAV-induced cytopathic effect on RK13 cells was found up to the distance of 45 cm, with the number of infected cells decreasing with distance. By aero-sampler, viral particles were detected in concentration ≤4.2 TCID50/mL. With both antiviral agents used as coolants, no EAV-associated RK-13 cell infection was found. Conclusion:We managed to predictably demonstrate EAV spread by droplets because of USS action. More importantly, we managed to mitigate the spread by a simple substitution of the USS coolant with NaOCl or EOW.

Multi-state models provide an extension of the usual survival/event-history analysis setting. In the medical domain, multi-state models give the possibility of further investigating intermediate events such as relapse and remission. In this work, a further extension is proposed using relative survival, where mortality due to population causes (i.e. non-disease-related mortality) is evaluated. The objective is to split all mortality in disease and non-disease-related mortality, with and without intermediate events, in datasets where cause of death is not recorded or is uncertain. To this end, population mortality tables are integrated into the estimation process, while using the basic relative survival idea that the overall mortality hazard can be written as a sum of a population and an excess part. Hence, we propose an upgraded non-parametric approach to estimation, where population mortality is taken into account. Precise definitions and suitable estimators are given for both the transition hazards and probabilities. Variance estimating techniques and confidence intervals are introduced and the behaviour of the new method is investigated through simulations. The newly developed methodology is illustrated by the analysis of a cohort of patients followed after an allogeneic hematopoietic stem cell transplantation. The work has been implemented in the R package mstate.