Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale.

SARS-CoV-2 surveillance by wastewater-based epidemiology is poised to provide a complementary approach to sequencing individual cases. However, robust quantification of variants and de novo detection of emerging variants remains challenging for existing strategies. We deep sequenced 3,413 wastewater samples representing 94 municipal catchments, covering >59% of the population of Austria, from December 2020 to February 2022. Our system of variant quantification in sewage pipeline designed for robustness (termed VaQuERo) enabled us to deduce the spatiotemporal abundance of predefined variants from complex wastewater samples. These results were validated against epidemiological records of >311,000 individual cases. Furthermore, we describe elevated viral genetic diversity during the Delta variant period, provide a framework to predict emerging variants and measure the reproductive advantage of variants of concern by calculating variant-specific reproduction numbers from wastewater. Together, this study demonstrates the power of national-scale WBE to support public health and promises particular value for countries without extensive individual monitoring.

An iterative algorithm for optimizing COVID-19 vaccination strategies considering unknown supply.

BACKGROUND AND OBJECTIVE: The distribution of the newly developed vaccines presents a great challenge in the ongoing SARS-CoV-2 pandemic. Policy makers must decide which subgroups should be vaccinated first to minimize the negative consequences of the pandemic. These decisions must be made upfront and under uncertainty regarding the amount of vaccine doses available at a given time. The objective of the present work was to develop an iterative optimization algorithm, which provides a prioritization order of predefined subgroups. The results of this algorithm should be optimal but also robust with respect to potentially limited vaccine supply. METHODS: We present an optimization meta-heuristic which can be used in a classic simulation-optimization setting with a simulation model in a feedback loop. The meta-heuristic can be applied in combination with any epidemiological simulation model capable of depicting the effects of vaccine distribution to the modeled population, accepts a vaccine prioritization plan in a certain notation as input, and generates decision making relevant variables such as COVID-19 caused deaths or hospitalizations as output. We finally demonstrate the mechanics of the algorithm presenting the results of a case study performed with an epidemiological agent-based model. RESULTS: We show that the developed method generates a highly robust vaccination prioritization plan which is proven to fulfill an elegant supremacy criterion: the plan is equally optimal for any quantity of vaccine doses available. The algorithm was tested on a case study in the Austrian context and it generated a vaccination plan prioritization favoring individuals age 65+, followed by vulnerable groups, to minimize COVID-19 related burden. DISCUSSION: The results of the case study coincide with the international policy recommendations which strengthen the applicability of the approach. We conclude that the path-dependent optimum optimum provided by the algorithm is well suited for real world applications, in which decision makers need to develop strategies upfront under high levels of uncertainty.

Model based estimation of the SARS-CoV-2 immunization level in austria and consequences for herd immunity effects.

Several systemic factors indicate that worldwide herd immunity against COVID-19 will probably not be achieved in 2021. On the one hand, vaccination programs are limited by availability of doses and on the other hand, the number of people already infected is still too low to have a disease preventing impact and new emerging variants of the virus seem to partially neglect developed antibodies from previous infections. Nevertheless, by February 2021 after one year of observing high numbers of reported COVID-19 cases in most European countries, we might expect that the immunization level should have an impact on the spread of SARS-CoV-2. Here we present an approach for estimating the immunization of the Austrian population and discuss potential consequences on herd immunity effects. To estimate immunization we use a calibrated agent-based simulation model that reproduces the actual COVID-19 pandemic in Austria. From the resulting synthetic individual-based data we can extract the number of immunized persons. We then extrapolate the progression of the epidemic by varying the obtained level of immunization in simulations of an hypothetical uncontrolled epidemic wave indicating potential effects on the effective reproduction number. We compared our theoretical findings with results derived from a classic differential equation SIR-model. As of February 2021, [Formula: see text] of the Austrian population has been affected by a SARS-CoV-2 infection which causes a [Formula: see text] reduction of the effective reproduction number and a [Formula: see text] reduction of the prevalence peak compared to a fully susceptible population. This estimation is now recomputed on a regular basis to publish model based analysis of immunization level in Austria also including the fast growing effects of vaccination programs. This provides substantial information for decision makers to evaluate the necessity of non pharmaceutical intervention measures based on the estimated impact of natural and vaccinated immunization.

Real-world experience with anti-PD-1/PD-L1 monotherapy in patients with non-small cell lung cancer : A retrospective Austrian multicenter study.

OBJECTIVE: As real-world data regarding immunotherapy for non-small cell lung cancer are lacking for Austria, we conducted a retrospective study in six hospitals to present data from real-world practice. METHODS: Patients with metastatic non-small cell lung cancer were stratified into two groups, either patients with first-line pembrolizumab monotherapy (cohort 1) or patients with second-line nivolumab, pembrolizumab or atezolizumab monotherapy (cohort 2). Primary outcome measures were objective response rate and overall survival. A matched-pair analysis was performed to compare overall survival to patients from the Tyrolean Lung Cancer Project as a historical control group. RESULTS: In total, 89 patients were identified, 42 patients in cohort 1 and 47 patients in cohort 2. The objective response rates were 43.3% and 31.4%, respectively. The median overall survival was 17.0 months (95% CI 11.7-21.5 months) in cohort 1 and 18.7 months (95% CI 9.5-23.4 months) in cohort 2. Treatment-related adverse events grades 3 and 4 were reported in 11.2% of patients. The matched-pair analysis showed a median overall survival of 15.2 months (95% CI 7.6-20.4 months) for first-line pembrolizumab monotherapy compared to 9.8 months (95% CI 7.8-11.6 months) for the historical control (p = 0.43). In cohort 2, a median overall survival of 20.3 months (95% CI 6.9-26.2 months) for second-line immunotherapy compared to 5.4 months (95% CI 3.2-11.7 months) for the historical control (p = 0.18) was shown. CONCLUSION: The results are comparable with other real-world studies and, when matched to historical controls, support the improvement in outcomes made possible by these agents.

Health Care Atlases: Informing the General Public About the Situation of the Austrian Health Care System.

There is a great number of complex data concerning the Austrian Health Care System. The goal was to process this data and present it to the general public on an easily accessible information platform. The platform focuses on data about the burden of disease of the Austrian Population, the available medical care and the services provided by the physicians. Due to the vast differences in the underlying source data, the methods used for the data acquisition range from statistical linkage over web scraping to aggregating data on the reimbursed services. The results are published on a website and are mainly displayed with interactive graphics. Overall, these dynamic and interactive websites provide a good overview of the situation of the Austrian Health Care System and presents the information in an intuitive and comprehensible manner. Furthermore, the information given in the atlases can contribute to the health care planning in order to identify distinctive service provision in Austria.

Improving Information About Private Consultants Through Data Linkage.

In Austria, there is no single source of truth holding information about all physicians and their medical practice. Therefore, different sources have to be combined to accumulate detailed information about doctors, identify data errors and increase overall data quality. The aim of this project is to link two datasets from vastly different origins utilizing reproduceable and mostly automatic procedures in contrast to manually acquired links in the past. As there is no global identifier, names and addresses of the doctors were used instead. Because of different spellings and typos of names and addresses within and in between the datasets, direct comparison does not lead to satisfactory results. Therefore, probabilistic matching with string metrics was applied. The utilized methods significantly improve the linkage and allow to match about 80 % of the private consultants in both datasets.