How weather triggers the emergence of bats from their subterranean hibernacula.

Hibernation is one of the most important behaviours of bats of the temperate zone. During winter, when little food or liquid water is available, hibernation in torpor lowers metabolic costs. However, the timing of emergence from hibernation is crucial for the resumption of the reproductive process in spring. Here, we investigate the spring emergence of six bat species or pairs of bat species of the genera Myotis and Plecotus at five hibernation sites in Central Europe over 5 years. Using generalized additive Poisson models (GAPMs), we analyze the influence of weather conditions (air and soil temperature, atmospheric pressure, atmospheric pressure trend, rain, wind, and cloud cover) as predictors of bat activity and separate these extrinsic triggers from residual motivation to emerge from hibernation (extrinsic factors not studied; intrinsic motivation). Although bats in a subterranean hibernaculum are more or less cut off from the outside world, all species showed weather dependence, albeit to varying degrees, with air temperature outside the hibernaculum having a significant positive effect in all species. The residual, potentially intrinsic motivation of species to emerge from their hibernacula corresponds to their general ecological adaptation, such as trophic specialization and roosting preferences. It allows the definition of three functional groups (high, medium and low residual activity groups) according to the degree of weather dependence of spring activity. A better knowledge of the interplay of extrinsic triggers and residual motivation (e.g., internal zeitgebers) for spring emergence will help to understand the flexibility of a species to adapt to a changing world.

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021.

BACKGROUND: During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. METHODS: We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. RESULTS: We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. CONCLUSIONS: Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.

We compare forecasts of weekly case and death numbers for COVID-19 in Germany and Poland based on 15 different modelling approaches. These cover the period from January to April 2021 and address numbers of cases and deaths one and two weeks into the future, along with the respective uncertainties. We find that combining different forecasts into one forecast can enable better predictions. However, case numbers over longer periods were challenging to predict. Additional data sources, such as information about different versions of the SARS-CoV-2 virus present in the population, might improve forecasts in the future.

On the Use of Aggregate Survey Data for Estimating Regional Major Depressive Disorder Prevalence.

Major depression is a severe mental disorder that is associated with strongly increased mortality. The quantification of its prevalence on regional levels represents an important indicator for public health reporting. In addition to that, it marks a crucial basis for further explorative studies regarding environmental determinants of the condition. However, assessing the distribution of major depression in the population is challenging. The topic is highly sensitive, and national statistical institutions rarely have administrative records on this matter. Published prevalence figures as well as available auxiliary data are typically derived from survey estimates. These are often subject to high uncertainty due to large sampling variances and do not allow for sound regional analysis. We propose a new area-level Poisson mixed model that accounts for measurement errors in auxiliary data to close this gap. We derive the empirical best predictor under the model and present a parametric bootstrap estimator for the mean squared error. A method of moments algorithm for consistent model parameter estimation is developed. Simulation experiments are conducted to show the effectiveness of the approach. The methodology is applied to estimate the major depression prevalence in Germany on regional levels crossed by sex and age groups.

Adjusting selection bias in German health insurance records for regional prevalence estimation.

BACKGROUND: Regional prevalence estimation requires epidemiologic data with substantial local detail. National health surveys may lack in sufficient local observations due to limited resources. Therefore, corresponding prevalence estimates may not capture regional morbidity patterns with the necessary accuracy. Health insurance records represent alternative data sources for this purpose. Fund-specific member populations have more local observations than surveys, which benefits regional prevalence estimation. However, due to national insurance market regulations, insurance membership can be informative for morbidity. Regional fund-specific prevalence proportions are selective in the sense that the morbidity structure of a fund's member population cannot be extrapolated to the national population. This implies a selection bias that marks a major obstacle for statistical inference. We provide a methodology to adjust fund-specific selectivity and perform regional prevalence estimation from health insurance records. The methodology is applied to estimate regional cohort-referenced diabetes mellitus type 2 prevalence in Germany. METHODS: Records of the German Public Health Insurance Company from 2014 and Diagnosis-Related Group Statistics data are combined within a benchmarked multi-level model. The fund-specific selectivity is adjusted in a two-step procedure. Firstly, the conditional expectation of the insurance company's regional prevalence given related inpatient diagnosis frequencies of its members is quantified. Secondly, the regional prevalence is estimated by extrapolating the conditional expectation using corresponding inpatient diagnosis frequencies of the Diagnosis-Related Group Statistics as benchmarks. Model assumptions are validated via Monte Carlo simulation. Variable selection is performed via multivariate methods. The optimal model fit is determined by analysis of variance. 95% confidence intervals for the estimates are constructed via semiparametric bootstrapping. RESULTS: The national diabetes mellitus type 2 prevalence is estimated at 8.70% with a 95% confidence interval of [8.48%, 9.35%]. This indicates an adjustment of the original fund-specific prevalence from - 32.79 to - 25.93%. The estimated disease distribution shows significant morbidity differences between regions, especially between eastern and western Germany. However, the cohort-referenced estimates suggest that these differences can be partially explained by regional demography. CONCLUSIONS: The proposed methodology allows regional prevalence estimation in remarkable detail despite fund-specific selectivity. This enhances and encourages the use of health insurance records for future epidemiologic studies.

Disentangling inhibition-based and retrieval-based aftereffects of distractors: Cognitive versus motor processes.

Selective attention refers to the ability to selectively act upon relevant information at the expense of irrelevant information. Yet, in many experimental tasks, what happens to the representation of the irrelevant information is still debated. Typically, 2 approaches to distractor processing have been suggested, namely distractor inhibition and distractor-based retrieval. However, it is also typical that both processes are hard to disentangle. For instance, in the negative priming literature (for a review Frings, Schneider, & Fox, 2015) this has been a continuous debate since the early 1980s. In the present study, we attempted to prove that both processes exist, but that they reflect distractor processing at different levels of representation. Distractor inhibition impacts stimulus representation, whereas distractor-based retrieval impacts mainly motor processes. We investigated both processes in a distractor-priming task, which enables an independent measurement of both processes. For our argument that both processes impact different levels of distractor representation, we estimated the exponential parameter (τ) and Gaussian components (µ, σ) of the exponential Gaussian reaction-time (RT) distribution, which have previously been used to independently test the effects of cognitive and motor processes (e.g., Moutsopoulou & Waszak, 2012). The distractor-based retrieval effect was evident for the Gaussian component, which is typically discussed as reflecting motor processes, but not for the exponential parameter, whereas the inhibition component was evident for the exponential parameter, which is typically discussed as reflecting cognitive processes, but not for the Gaussian parameter. (PsycINFO Database Record