Epidemic and pandemic preparedness with rapid outbreak response rely on timely, trustworthy evidence. Mathematical models are crucial for supporting timely and reliable evidence generation for public health decision-making with models spanning approaches from compartmental and metapopulation models to detailed agent-based simulations. Yet, the accompanying software ecosystem remains fragmented across model types, spatial resolutions, and computational targets, making models harder to compare, extend, and deploy at scale. Here we present MEmilio, a modular, high-performance framework for epidemic simulation that harmonizes the specification and execution of diverse dynamic epidemiological models within a unified and harmonized architecture. MEmilio couples an efficient C++ simulation core with coherent model descriptions and a user-friendly Python interface, enabling workflows that run on laptops as well as high-performance computing systems. Standardized representations of space, demography, and mobility support straightforward adaptations in resolution and population size, facilitating systematic inter-model comparisons and ensemble studies. The framework integrates readily with established tools for uncertainty quantification and parameter inference, supporting a broad range of applications from scenario exploration to calibration. Finally, strict software-engineering practices, including extensive unit and continuous integration testing, promote robustness and minimize the risk of errors as the framework evolves. By unifying implementations across modeling paradigms, MEmilio aims to lower barriers to reuse and generalize models, enable principled comparisons of implicit assumptions, and accelerate the development of novel approaches that strengthen modeling-based outbreak preparedness.

The increasing number of Earth observation data necessitates for advanced automated evaluation. Autoencoders (AE), which are deep neural networks, have been successfully applied to change detection on optical images. Here, we present an investigation of the applicability of three different convolutional AE methods for change detection on time series of SAR images. During the evaluation, the so-called joint AE approach is proved to be more precise and less sensitive to changes in brightness, thus designating less false positives. Moreover, the joint AE method indicates three noticeable and conspicuous regions.

Almost all humankind depends on satellite technology orbiting the Earth. However, space operations are increasingly endangered by the rapidly growing number of orbiting objects and debris. To avoid collisions with debris particles and to keep the space infrastructure alive, it is mandatory to monitor cooperative and uncooperative resident space objects. For this purpose, we are developing a software system called Backbone Catalogue of Relational Debris Information (BACARDI). It provides a database with orbit information of resident space objects and related products like ephemerides, which enables us to derive close approaches and provide collision warnings. The challenge is to detect, identify and track new resident space objects that were recently launched, created by fragmentations and collisions or recently observed due to improved sensor technologies. Typically, passive optical telescopes observe objects for a relatively short time. A short time series of observation is called a tracklet. To derive the full orbit of an object, at least a pair of tracklets is needed. In case the tracklets cannot be associated to an already known object, it should therefore be identified which tracklets belong to the same object. The algorithm performing this task is called the Tracklet-Tracklet Correlation (TTC). Our aim is to integrate the TTC into BACARDI. Therefore, we have to consider that the individual algorithm meets different requirements than the overall software. On the one hand, BACARDI is a software system that needs to be easily maintainable, extendable and usable and is thus written in Python. On the other hand, the TTC has to be computationally fast, which is why it is written in C++. To fulfil these opposing requirements, we present a successful linkage of a fast C++ based TTC to a Python-based BACARDI. To perform this task, we used the programming language Cython. The Cython syntax is a superset of the Python syntax and provides additional rules to address C-like performance. This allows us to introduce wrapper code with C++ performance and a Python interface. In this paper we show our course of action to re-engineer a prototype C++ code into a high-quality stand-alone Python-package. To ensure high quality, sustainability and long-term maintainability, our steps include the transformation to an object-oriented C++ code, the utilization of well-known C++ libraries and introducing a software engineering process. Our software engineering process includes, but is not limited to, a predetermined communication and management structure, change management process and automated quality control. This is mostly implemented by GitLab functionality, which is also used to analyze and demonstrate advantages of the software engineering process. Moreover, we prove that it is highly useful to combine C++ code with Python by comparing the performance of a Cython-wrapped implementation and a pure Python implementation of the same algorithm, both in serial and in parallel. All of this is demonstrated on a large data set of real observations provided by SMARTnet. Our procedure can serve as a template for everyone who faces a similar challenge.

Background Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. Conclusions Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.

Non-pharmaceutical interventions (NPIs) are important to mitigate the spread of infectious diseases as long as no vaccination or outstanding medical treatments are available. We assess the effectiveness of the sets of non-pharmaceutical interventions that were in place during the course of the Coronavirus disease 2019 (Covid-19) pandemic in Germany. Our results are based on hybrid models, combining SIR-type models on local scales with spatial resolution. In order to account for the age-dependence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we include realistic prepandemic and recently recorded contact patterns between age groups. The implementation of non-pharmaceutical interventions will occur on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. In order to account for spatial heterogeneity, we use a graph approach and we include high-quality information on commuting activities combined with traveling information from social networks. The remaining uncertainty will be accounted for by a large number of randomized simulation runs. Based on the derived factors for the effectiveness of different non-pharmaceutical interventions over the past months, we provide different forecast scenarios for the upcoming time.

For Space Situational Awareness, the German Aerospace Center (DLR) develops the software system "Backbone Catalogue of Relational Debris Information" (BACARDI), which allows for keeping track of resident space objects. BACARDI's key features are automated processing services which produce orbit information and products like collision warnings. We present how we applied new methods of software analytics to the BACARDI project. BACARDI is an example of a complex software system with large development effort carried out by a team of various specialists. Our goal is to design and implement an efficient software development process, balancing the explorative character of a research project and operational requirements (i.e. tailored from official standards in the aerospace domain). Therefore, we established a software development process for the project where we focus on software quality. We applied methods to structure, communicate, and utilize the diverse skills, knowledge, and experience in the team concisely and precisely. After one year of practical utilization, we analyzed the process based on the repository data. By analyzing these data, we assess and prove the effects of the introduced process on the development of a software, which is used in the aerospace domain.

Adhesion of polymers and proteins to substrates plays a crucial role in many technological applications and biological processes. A prominent example is the von Willebrand factor (VWF) protein, which is essential in blood clotting as it mediates adhesion of blood platelets to the site of injury at high shear rates. VWF is activated by flow and is able to bind efficiently to damaged vessel walls even under extreme flow-stress conditions; however, its adhesion is reversible when the flow strength is significantly reduced or the flow is ceased. Motivated by the properties and behavior of VWF in flow, we investigate adhesion of shear-activated polymers to a planar wall in flow and whether the adhesion is reversible under flow stasis. The main ingredients of the polymer model are cohesive inter-monomer interactions, a catch bond with the adhesive surface, and the shear activation/deactivation of polymer adhesion correlated with its stretching in flow. The cohesive interactions within the polymer maintain a globular conformation under low shear stresses and allow polymer stretching if a critical shear rate is exceeded, which is directly associated with its activation for adhesion. Our results show that polymer adhesion at high shear rates is significantly stabilized by catch bonds, while at the same time they also permit polymer dissociation from a surface at low or no flow stresses. In addition, the activation/deactivation mechanism for adhesion plays a crucial role in the reversibility of its adhesion. These observations help us better understand the adhesive behavior of VWF in flow and interpret its adhesion malfunctioning in VWF-related diseases.

The protein von Willebrand factor (VWF) is essential in primary hemostasis, as it mediates platelet adhesion to vessel walls. VWF retains its compact (globule-like) shape in equilibrium due to internal molecular associations, but is able to stretch when a high enough shear stress is applied. Even though the shear-flow sensitivity of VWF conformation is well accepted, the behavior of VWF under realistic blood flow conditions remains poorly understood. We perform mesoscopic numerical simulations together with microfluidic experiments in order to characterize VWF behavior in blood flow for a wide range of flow-rate and hematocrit conditions. In particular, our results demonstrate that the compact shape of VWF is important for its migration (or margination) toward vessel walls and that VWF stretches primarily in a near-wall region in blood flow making its adhesion possible. Our results show that VWF is a highly optimized protein in terms of its size and internal associations which are necessary to achieve its vital function. A better understanding of the relevant mechanisms for VWF behavior in microcirculation provides a further step toward the elucidation of the role of mutations in various VWF-related diseases.