Wastewater-based epidemiology is widely applied in Austria since April 2020 to monitor the SARS-CoV-2 pandemic. With a steadily increasing number of monitored wastewater facilities, 123 plants covering roughly 70 % of the 9 million population were monitored as of August 2022. In this study, the SARS-CoV-2 viral concentrations in raw sewage were analysed to infer short-term hospitalisation occupancy. The temporal lead of wastewater-based epidemiological time series over hospitalisation occupancy levels facilitates the construction of forecast models. Data pre-processing techniques are presented, including the approach of comparing multiple decentralised wastewater signals with aggregated and centralised clinical data. Time‑lead quantification was performed using cross-correlation analysis and coefficient of determination optimisation approaches. Multivariate regression models were successfully applied to infer hospitalisation bed occupancy. The results show a predictive potential of viral loads in sewage towards Covid-19 hospitalisation occupancy, with an average lead time towards ICU and non-ICU bed occupancy between 14.8-17.7 days and 8.6-11.6 days, respectively. The presented procedure provides access to the trend and tipping point behaviour of pandemic dynamics and allows the prediction of short-term demand for public health services. The results showed an increase in forecast accuracy with an increase in the number of monitored wastewater treatment plants. Trained models are sensitive to changing variant types and require recalibration of model parameters, likely caused by immunity by vaccination and/or infection. The utilised approach displays a practical and rapidly implementable application of wastewater-based epidemiology to infer hospitalisation occupancy.

Harnessing nature’s ingenuity with microorganisms for industrial production is an attractive solution to today’s climate concerns. Nature’s innate diversity allows the production of many value-added chemicals and can be expanded on through genetic engineering. Although the use of microbial cell factories (MCFs) has been extremely successful at lab scale, the numbers of successful bioprocesses remain limited. High cell densities and long cultivation times lead to reductions in productivity over the course of the cultivation through the effects of genetic and expression instability of the strain. This instability leads to population diversification. In this review, we explore the roots of genetic instability in microorganisms, focusing on prokaryotic bioprocesses, and how organisms cope with this instability. We spotlight single-cell detection methods capable of monitoring populations within the bioprocess both in- and on-line. We also examine different approaches to minimizing population diversification, both through strain development and bioprocess engineering. With this review, we highlight the fact that population-averaged metrics overlook the single-cell stresses driving genetic and functional instability, leading to an overestimation of microbial bioprocess robustness. High-throughput single-cell monitoring in industry-like conditions remains essential to identify and select truly stable microbial cell factories and bioprocesses.