Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.

Water resources systems, as facilities for storing water and supplying demands, have been critically important due to their operational requirements. This paper presents the applications of an R package in a large-scale water resources operation. The WRSS (Water Resources System Simulator) is an object-oriented open-source package for the modeling and simulation of water resources systems based on Standard Operation Policy (SOP). The package provides R users several functions and methods to build water supply and energy models, manipulate their components, create scenarios, and publish and visualize the results. WRSS is capable of incorporating various components of a complex supply–demand system, including numerous reservoirs, aquifers, diversions, rivers, junctions, and demand nodes, as well as hydropower analysis, which have not been presented in any other R packages. For the WRSS’s development, a novel coding system was devised, allowing the water resources components to interact with one another by transferring the mass in terms of seepage, leakage, spillage, and return-flow. With regard to the running time, as a key factor in complex models, WRSS outshone the existing commercial tools such as the Water Evaluation and Planning System (WEAP) significantly by reducing the processing time by 50 times for a single unit reservoir. Additionally, the WRSS was successfully applied to a large-scale water resources system comprising of 5 medium- to large-size dams with 11 demand nodes. The results suggested dams with larger capacity sizes may meet agriculture sector demand but smaller capacities to fulfill environmental water requirement. Additionally, large-scale approach modeling in the operation of one of the studied dams indicated its implication on the reservoirs supply resiliency by increasing 10 percent of inflow compared with single unit operation.