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Background: There is a lack of population-based studies on the effectiveness and durability of the SARS-CoV-2-induced immune protection during the Omicron wave. Methods: This retrospective study included 470 159 cases aged 60 years or older, who tested positive for SARS-CoV-2 between January 1 and June 30, 2022 in Bavaria, Germany. We examined time to death, measured from the earliest date of reporting/symptom onset until day 60. Cox models were used to estimate adjusted hazard ratios (HR) for sex, age, earliest calendar date of documented infection, and level of vaccine-induced or hybrid immunity (LI). Results: We observed 3836 deaths (case fatality rate 0.82%). Risk of death was significantly lower in all LIs than in unvaccinated cases (adjusted HR for a full primary LI achieved within the last six months: 0.30, 95% confidence interval (CI) 0.23-0.39; after six months: 0.46, 95% CI 0.35-0.60). Boosting further decreased the risk of death (within the last three months: HR 0.17, 95% CI 0.15-0.20; after three months: HR 0.25, 95% CI 0.21-0.29). Conclusion: In elderly Bavarians, an increasing LI was associated with an increasing protection against death during the Omicron wave. Protection, however, may decrease to some extent after several months.

Over the course of the COVID-19 pandemic, Generalised Additive Models (GAMs) have been successfully employed on numerous occasions to obtain vital data-driven insights. In this paper we further substantiate the success story of GAMs, demonstrating their flexibility by focusing on three relevant pandemic-related issues. First, we examine the interdepency among infections in different age groups, concentrating on school children. In this context, we derive the setting under which parameter estimates are independent of the (unknown) case-detection ratio, which plays an important role in COVID-19 surveillance data. Second, we model the incidence of hospitalisations, for which data is only available with a temporal delay. We illustrate how correcting for this reporting delay through a nowcasting procedure can be naturally incorporated into the GAM framework as an offset term. Third, we propose a multinomial model for the weekly occupancy of intensive care units (ICU), where we distinguish between the number of COVID-19 patients, other patients and vacant beds. With these three examples, we aim to showcase the practical and "off-the-shelf" applicability of GAMs to gain new insights from real-world data.

Accounting for phase variability is a critical challenge in functional data analysis. To separate it from amplitude variation, functional data are registered, i.e., their observed domains are deformed elastically so that the resulting functions are aligned with template functions. At present, most available registration approaches are limited to datasets of complete and densely measured curves with Gaussian noise. However, many real-world functional data sets are not Gaussian and contain incomplete curves, in which the underlying process is not recorded over its entire domain. In this work, we extend and refine a framework for joint likelihood-based registration and latent Gaussian process-based generalized functional principal component analysis that is able to handle incomplete curves. Our approach is accompanied by sophisticated open-source software, allowing for its application in diverse non-Gaussian data settings and a public code repository to reproduce all results. We register data from a seismological application comprising spatially indexed, incomplete ground velocity time series with a highly volatile Gamma structure. We describe, implement and evaluate the approach for such incomplete non-Gaussian functional data and compare it to existing routines., Comment: Excluding the Appendix (i.e. the second half of the submitted pdf): 41 pages, 8 figures Including the Appendix: 74 pages, 32 figures Submitted to Journal of Computational and Graphical Statistics

Election poll reporting often focuses on mean values and only subordinately discusses the underlying uncertainty. Subsequent interpretations are too often phrased as certain. Moreover, media coverage rarely adequately takes into account the differences between now- and forecasts. These challenges were ubiquitous in the context of the 2016 and 2020 U.S. presidential elections, but are also present in multi-party systems like Germany. We discuss potential sources of bias in nowcasting and forecasting and review the current standards in the visual presentation of survey-based nowcasts. Concepts are presented to attenuate the issue of falsely perceived accuracy. We discuss multiple visual presentation techniques for central aspects in poll reporting. One key idea is the use of Probabilities of Events instead of party shares. The presented ideas offer modern and improved ways to communicate (changes in) the electoral mood for the general media.

Common election poll reporting is often misleading as sample uncertainty is addressed insufficiently or not covered at all. Furthermore, main interest usually lies beyond the simple party shares. For a more comprehensive opinion poll and election coverage, we propose shifting the focus towards the reporting of survey-based probabilities for specific events of interest. We present such an approach for multi-party electoral systems, focusing on probabilities of coalition majorities. A Monte Carlo approach based on a Bayesian Multinomial-Dirichlet model is used for estimation. Probabilities are estimated, assuming the election was held today (''now-cast''), not accounting for potential shifts in the electorate until election day ''fore-cast''. Since our method is based on the posterior distribution of party shares, the approach can be used to answer a variety of questions related to the outcome of an election. We also introduce visualization techniques that facilitate a more adequate depiction of relevant quantities as well as respective uncertainties. The benefits of our approach are discussed by application to the German federal elections in 2013 and 2017. An open source implementation of our methods is freely available in the R package coalitions.

Background: Variables in epidemiological observational studies are commonly subject to measurement error and misclassification, but the impact of such errors is frequently not appreciated or ignored. As part of the STRengthening Analytical Thinking for Observational Studies (STRATOS) Initiative, a Task Group on measurement error and misclassification (TG4) seeks to describe the scope of this problem and the analysis methods currently in use to address measurement error. Methods: TG4 conducted a literature survey of four types of research studies that are typically impacted by exposure measurement error: 1) dietary intake cohort studies, 2) dietary intake population surveys, 3) physical activity cohort studies, and 4) air pollution cohort studies. The survey was conducted to understand current practice for acknowledging and addressing measurement error. Results: The survey revealed that while researchers were generally aware that measurement error affected their studies, very few adjusted their analysis for the error. Most articles provided incomplete discussion of the potential effects of measurement error on their results. Regression calibration was the most widely used method of adjustment. Conclusions: Even in areas of epidemiology where measurement error is a known problem, the dominant current practice is to ignore errors in analyses. Methods to correct for measurement error are available but require additional data to inform the error structure. There is a great need to incorporate such data collection within study designs and improve the analytical approach. Increased efforts by investigators, editors and reviewers are also needed to improve presentation of research when data are subject to error., Comment: 41 pages, including 4 tables and supplementary material