Collaborative nowcasting of COVID-19 hospitalization incidences in Germany. - In: PLoS Computational Biology, ISSN 1553-7358, Bd. 19 (2023), 8, e1011394, S. 1-25
Real-time surveillance is a crucial element in the response to infectious disease outbreaks. However, the interpretation of incidence data is often hampered by delays occurring at various stages of data gathering and reporting. As a result, recent values are biased downward, which obscures current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhancing situational awareness. In this paper, we present a preregistered real-time assessment of eight nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences during the COVID-19 pandemic. This indicator played an important role in the management of the outbreak in Germany and was linked to levels of non-pharmaceutical interventions via certain thresholds. Due to its definition, in which hospitalization counts are aggregated by the date of case report rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this study, all methods were applied from 22 November 2021 to 29 April 2022, with probabilistic nowcasts produced each day for the current and 28 preceding days. Nowcasts at the national, state, and age-group levels were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall, the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. The accompanying prediction intervals were also too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. For the most recent days, which are often considered the most relevant in practice, a mean ensemble of the submitted nowcasts performed best. We conclude by providing some lessons learned on the definition of nowcasting targets and practical challenges.
Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations. - In: eLife, ISSN 2050-084X, Bd. 12 (2023), e81916, S. 1-23, insges. 23 S.
Generalized modeling of photoluminescence transients. - In: Physica status solidi, ISSN 1521-3951, Bd. 260 (2023), 1, 2200339, S. 1-12
Time-resolved photoluminescence (TRPL) measurements and the extraction of meaningful parameters involve four key ingredients: a suitable sample such as a semiconductor double heterostructure, a state-of-the-art measurement setup, a kinetic model appropriate for the description of the sample behavior, and a general analysis method to extract the model parameters of interest from the measured TRPL transients. Until now, the last ingredient is limited to single curve fits, which are mostly based on simple models and least-squares fits. These are often insufficient for the parameter extraction in real-world applications. The goal of this article is to give the community a universal method for the analysis of TRPL measurements, which accounts for the Poisson distribution of photon counting events. The method can be used to fit multiple TRPL transients simultaneously using general kinematic models, but should also be used for single transient fits. To demonstrate this approach, multiple TRPL transients of a GaAs/AlGaAs heterostructure are fitted simultaneously using coupled rate equations. It is shown that the simultaneous fits of several TRPL traces supplemented by systematic error estimations allow for a more meaningful and more robust parameter determination. The statistical methods also quantify the quality of the description by the underlying physical model.
Microfluidically-assisted isolation and characterization of Achromobacter spanius from soils for microbial degradation of synthetic polymers and organic solvents. - In: Environments, ISSN 2076-3298, Bd. 9 (2022), 12, 147, S. 1-17
A micro segmented-flow approach was utilized for the isolation soil bacteria that can degrade synthetic polymers as polyethylene glycols (PEG) and polyacrylamide (PAM). We had been able to obtain many strains; among them, five Achromobacter spanius strains from soil samples of specific sampling sites that were connected with ancient human impacts. In addition to the characterization of community responses and isolating single strains, this microfluidic approach allowed for investigation of the susceptibility of Achromobacter spanius strains against three synthetic polymers, including PEG, PAM, and Polyvinylpyrrolidone (PVP) and two organic solvents known as 1,4-dioxane and diglyme. The small stepwise variation of effector concentrations in 500 nL droplets provides a detailed reflection of the concentration-dependent response of bacterial growth and endogenous autofluorescence activity. As a result, all five strains can use PEG600 as carbon source. Furthermore, all strains showed similar dose-response characteristics in 1,4-dioxane and diglyme. However, significantly different PAM- and PVP-tolerances were found for these strains. Samples from the surface soil of prehistorical rampart areas supplied a strain capable of degradation of PEG, PVP, and PAM. This study demonstrates on the one hand, the potential of microsegment flow for miniaturized dose-response screening studies and its ability to detect novel strains, and on the other hand, two of five isolated Achromobacter spanius strains may be useful in providing optimal growth conditions in bioremediation and biodegradation processes.
National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021. - In: Communications medicine, ISSN 2730-664X, Bd. 2 (2022), 136, S. 1-17
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.
Analysis of safety-critical cloud architectures with multi-trajectory simulation. - In: 2022 Annual Reliability and Maintainability Symposium (RAMS), (2022), insges. 7 S.
Dynamic safety-critical systems require model-based techniques and tools for their systems design. The paper presents a stochastic Petri net model of an industrial safetycritical cloud server architecture for train control. Its reliability has to be evaluated to assess tradeoffs in architecture and level of fault tolerance. Simulation methods are too slow for such rare-event problems, while numerical analysis techniques suffer from the state-space explosion problem. The paper extends a recently developed multi-trajectory simulation algorithm combining elements of simulation and numerical analysis such that it increases the accuracy of rare-event simulations within a given computation time budget. Simulation experiments have been carried out with a prototype tool.
How much testing and social distancing is required to control COVID-19? : some insight based on an age-differentiated compartmental model. - In: SIAM journal on control and optimization, ISSN 1095-7138, Bd. 60 (2022), 2, S. S145-S169
In this paper, we provide insights on how much testing and social distancing is required to control COVID-19. To this end, we develop a compartmental model that accounts for key aspects of the disease: incubation time, age-dependent symptom severity, and testing and hospitalization delays; the model's parameters are chosen based on medical evidence, and, for concreteness, adapted to the German situation. Then, optimal mass-testing and age-dependent social distancing policies are determined by solving optimal control problems both in open loop and within a model predictive control framework. We aim to minimize testing and/or social distancing until herd immunity sets in under a constraint on the number of available intensive care units. We find that an early and short lockdown is inevitable but can be slowly relaxed over the following months.
A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave. - In: Nature Communications, ISSN 2041-1723, Bd. 12 (2021), 5173, S. 1-16
Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October-19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.
How to coordinate vaccination and social distancing to mitigate SARS-CoV-2 outbreaks. - In: SIAM journal on applied dynamical systems, ISSN 1536-0040, Bd. 20 (2021), 2, S. 1135-1157
Most countries have started vaccinating people against COVID-19. However, due to limited production capacities and logistical challenges it will take months/years until herd immunity is achieved. Therefore, vaccination and social distancing have to be coordinated. In this paper, we provide some insight on this topic using optimization-based control on an age-differentiated compartmental model. For real-life decision-making, we investigate the impact of the planning horizon on the optimal vaccination/social distancing strategy. We find that in order to reduce social distancing in the long run, without overburdening the health care system, it is essential to vaccinate the people with the highest contact rates first. That is also the case if the objective is to minimize fatalities provided that the social distancing measures are sufficiently strict. However, for short-term planning it is optimal to focus on the high-risk group.
Admissible kernels for RKHS embedding of probability distributions. - In: Statistical papers, ISSN 1613-9798, Bd. 62 (2021), 3, S. 1499-1518
Similarity measurement of two probability distributions is important in many applications of statistics. Embedding such distributions into a reproducing kernel Hilbert space (RKHS) has many favorable properties. The choice of the reproducing kernel is crucial in the approach. We study this question by considering the similarity of two distributions of the same class. In particular, we investigate when the RKHS embedding is "admissible" in the sense that the distance between the embeddings should become smaller when the expectations are getting closer or when the variance is increasing to infinity. We give conditions on the widely-used translation-invariant reproducing kernels to be admissible. We also extend the study to multivariate non-symmetric Gaussian distributions.