Oliver Alber

Wastewater data can be very useful for epidemic control during a disease outbreak and proper synthesis of different sources of information can be integrated towards an alerting system, that can be used for decision support. Wastewater data are considered to be of high quality, since they do not depend on testing and can take into account asymptomatic cases. However, little effort has been given into utilizing such information in statistical process control procedures, usually aimed at industrial problems. In this article, we demonstrate how wastewater data can be utilized in health surveillance and propose a statistical framework that can act as a decision support tool. Specifically, we analyze SARS-CoV-2 wastewater data from Austria, constructing summary variables to implicitly describe the Covid-19 prevalence and, based on them, we assess the effectiveness of the current sampling strategy of Austria. We propose a framework of a statistical process monitoring system to aid epidemic management procedures in case SARS-CoV-2 concentration gets dangerously high.

The management of variety collections can be supported by integrating molecular data into Distinctness, Uniformity, and Stability (DUS) testing. DurdusTools is a genetic distance (GD) calculation tool that supports planning field trials in durum wheat. A commercially available single-nucleotide polymorphism (SNP) wheat microarray is used to profile varieties and candidate varieties. Their molecular profiles are stored in a database. SNP markers selected based on quality parameters are used to calculate the pairwise genetic distance (GD) between the varieties. Combining molecular and variety information creates a downloadable, user-friendly Excel-based output. The file shows the pairwise GD of the varieties most similar to the candidate variety of interest and selected variety information. After the first year of field trials, the DUS experts use the phenotypic assessment data together with the GD information to select genotypes to be grown in the field for side-by-side comparisons. The principles of DurdusTools allow for an easy integration of molecular data into DUS testing. Using the tool requires neither specific infrastructure nor molecular expert knowledge and was developed by its users for DUS testing purposes. This makes DurdusTools an easily accessible and user-friendly tool that supports variety selection for DUS field trials through an improved data basis.

Radon is a noble gas that occurs naturally as a decay product of uranium. Aside from smoking, radon is considered to be one of the major causes of lung cancer. Indoor environments, where radon can accumulate and potentially reach high concentrations, are of a particular concern. A mixed effects additive model along with a data-driven cross validation model selection method is applied to model the mean indoor radon concentration of dwellings in Austria. For this model a prediction approach is introduced, which enables the mapping of indoor radon potential to identify radon areas in Austria. The data used for modeling was collected in monitoring campaigns for private dwellings in Austria from 2013 to 2019. The proposed method allows policy makers to identify regions with high indoor radon concentrations and enables them to meet regulatory requirements or prioritize radon protection measures. The currently published Austrian radon map and the delineation of radon areas in Austria is based on this proposed method.