Background and Objective: Respiratory Syncytial Virus (RSV) constitutes a public health concern in the European Union and the European Economic Area (EU/EEA). The recent authorisation of new immunisation products has led member states to evaluate which immunisation strategy to adopt in their national health programs. Methods: This work utilises EpiGraph, a high-fidelity agent-based simulator, to evaluate the long-term epidemiological impact of RSV immunisation strategies in Italy for the 2025/2026 season. The simulator's social model has been extended to include demographic features like pregnant individuals and newborns to reflect the populations eligible for maternal vaccination (MV) and long-acting monoclonal antibodies (la-mAbs). The paper compares five distinct scenarios: high (90%) and low (20%) coverage levels for both MV and la-mAbs against a non-universal baseline. The model was calibrated using Italian census data and validated against historical age-specific hospitalisation incidence. Results: The simulation results indicate that while new RSV infection waves are likely regardless of the intervention, significant differences in public health outcomes emerge between the various immunisation strategies. la-mAbs provides an estimated 77% effectiveness against infection and 84.6% against severe disease at the time of administration, while MV shows approximately 51% effectiveness against infection and 71.3% against hospitalisation in neonates. The most substantial reductions in hospitalisations were observed in children under 1 month old in scenarios with high immunisation coverage.Conclusions: This research provides a comparative framework for EU/EEA policy-makers to assess which universal protection strategy most effectively reduces the paediatric disease burden.

In High-Performance Computing (HPC), parallel systems are used not only to executelarge-scale applications but also to run many mid-sized applications concurrently.The concurrent execution of applications on supercomputers requires careful resourcemanagement to avoid wasting resources, such as compute nodes or storage. To addressthese issues, Resource and Job Management Systems (RJMS) have been employed fordecades to allocate resources to applications. In this framework, applications competefor access to the available resources, with allocation being done statically beforeapplication execution. This approach is inherently suboptimal, as decisions are difficultto change over time, and applications access resources in a best-effort manner. In this paper, we propose a new approach in which a centralised component aggregates a per-applicationglobal view and makes application-level allocation decisions during execution, through scalable monitoringenabling dynamic resource management. By allowing adaptivepayloads, this approach reduces time-to-solution, facilitates I/O management,simplifies parallel job configuration via dynamic behaviour, and ultimately enhancesoverall system efficiency.

Parallel applications process large amounts of data, leading to intensive parallel I/O operations. These operations can exhibit different levels of complexity, including, among others, multiple I/O access patterns, data staging, and contention risks. Therefore, in order to exploit high-performance computing (HPC) systems efficiently and optimize the I/O performance, it is crucial to consider the I/O behaviour of the HPC applications. In this work, we have developed a framework that reproduces the I/O access pattern of real applications in a simulated environment provided by ElastiSim, a batch-system simulator for rigid, malleable, and evolving workloads. The simulated applications are generated based on I/O traces captured from real applications provided by the HPC Input/Output (HPCIO) analysis repository. The HPCIO analysis database includes traces combined with information about real applications' performance across different parallel I/O libraries and layers of the I/O stack. We have conducted detailed case studies of real-world applications' traces to demonstrate how the proposed modeling framework can provide insights into the performance characteristics of I/O applications, including the I/O congestion analysis based on the application's I/O access pattern.

Emerging hardware constraints are pushing workloads to become more composite. This transition involves new jobs where the HPC I/O systems are shared among multiple and concurrent jobs. This can generate load imbalances and contention in the end-to-end I/O paths, thus degrading the I/O system performance and the workloads. Recognizing this context, we define a simulation-based framework that alleviates resource contention in applications and ultimately allows us to design contention avoidance strategies. Specifically, by capturing behavior system-wide and extracting phases and characteristics of various performance metrics, we can mitigate contention by delaying the launch of applications. This framework leverages frequency domain analysis of performance metrics alongside clustering methods and is coupled with a comprehensive model of an HPC system implemented using Extended Stochastic Symmetric Nets.

Some data-intensive applications perform periodic read and write operations, which can lead to slowdowns and contention when multiple applications are running simultaneously on a shared filesystem. In high-performance computing (HPC), malleability refers to the ability to dynamically adjust job resources at runtime to optimize performance, reduce execution times, and improve efficiency. This paper presents a decentralized probabilistic framework designed to reduce I/O contention in data-intensive malleable applications. A performance model is used to obtain the application maximum I/O performance, and an algorithm is used to detect when there is contention when running multiple applications. In such cases, malleability is applied to a subset of running applications to mitigate performance issues. We tested the model on a parallel, data-intensive, agent-based epidemiological simulator that performs periodic write operations. The results show that our model efficiently reduces contention, particularly during the most data-intensive critical stages of the execution, improving I/O throughput up to a 15

Diffuse optical tomography uses near-infrared light for 3D brain imaging. We simulated errors due to inaccurate baseline parameters and developed a deep learning technique that mitigated these errors, enhancing contrast and localization of activation changes. (c) 2025 The Author(s)

Diffuse optical tomography (DOT) uses near-infrared light to image spatially varying optical parameters in biological tissues. In functional brain imaging, DOT uses a perturbation model to estimate the changes in optical parameters, corresponding to changes in measured data due to brain activity. The perturbation model typically uses approximate baseline optical parameters of the different brain compartments, since the actual baseline optical parameters are unknown. We simulated the effects of these approximate baseline optical parameters using parameter variations earlier reported in literature, and brain atlases from four adult subjects. We report the errors in estimated activation contrast, localization, and area when incorrect baseline values were used. Further, we developed a post-processing technique based on deep learning methods that can reduce the effects due to inaccurate baseline optical parameters. The method improved imaging of brain activation changes in the presence of such errors.

Agent-based epidemiological simulators have been proven to be one of the most successful tools for the analysis of COVID-19 propagation. The ability of these tools to reproduce the behavior and interactions of each single individual leads to accurate and detailed results, which can be used to model fine-grained health-related policies like selective vaccination campaigns or immunity waning. One characteristic of these tools is the large amount of input data and computational resources that they require. This relies on the development of parallel algorithms and methodologies for generating, accessing, and processing large volumes of data from multiple data sources. This work presents a parallel workflow for extending the social modeling of EpiGraph, an agent-based simulator. We have included two novel parallel social generation stages that generate a detailed and realistic social model and one new visualization stage. This work also presents a description of the algorithms used in each stage, different optimization techniques that permit to reduce the application convergence time, and a practical evaluation of large workloads on HPC systems. Results show that this contribution can be efficiently executed in parallel architectures and the results allow to increase the simulation detail level, representing a significant advance in the simulator scenario modeling. As a summary of results, the first contribution of this paper is the development of two models (a spatial and a social one) that assign geographical and socioeconomic indicators to each simulated individual (i.e., agents), reproducing the real social distribution of the city of Madrid. The second contribution presents an improved parallel and distributed algorithm that executes the two aforementioned models using different parallelization strategies and preserving the load balance.

This survey paper explores the different applications of workflows in computational environmental science. Workflows are crucial in streamlining complex computational processes, enabling researchers to manage and analyze large-scale environmental data effectively. The paper reviews existing literature, methodologies, and tools associated with workflow applications in environmental science, highlighting their impact on research efficiency, reproducibility, and collaboration. By examining case studies and emerging trends, this survey aims to provide insights into the current landscape of workflow applications within the computational environmental science domain.

With the increase of complex scientific simulations driven by workflows and heterogeneous workload profiles, managing system resources effectively is essential for improving performance and system throughput, especially due to trends like heterogeneous HPC and deeply integrated systems with on-chip accelerators. For optimal resource utilization, dynamic resource allocation can improve productivity across all system and application levels, by adapting the applications' configurations to the system's resources. In this context, malleable jobs, which can change resources at runtime, can increase the system throughput and resource utilization while bringing various advantages for HPC users (e.g., shorter waiting time). Malleability has received much attention recently, even though it has been an active research area for more than two decades. This article presents the state-of-the-art of malleable implementations in HPC systems, targeting mainly malleability in compute and I/O resources. Based on our experiences, we state our current concerns and list future opportunities for research.

Background: Collaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. Methods: We compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. Results: By collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. Conclusions: We observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts.

Introducción: Los modelos epidemiológicos han demostrado ser cruciales para apoyar la toma de decisiones de las autoridades sanitarias durante la pandemia de COVID-19, así como concienciar al público en general de las distintas medidas adoptadas por las autoridades (distanciamiento social, uso de mascarilla, vacunación, etc.). Objetivos: Describir la metodología para integrar diferentes fuentes de datos para generar una única serie temporal que proporciona tasas de incidencia reales de COVID-19 en España. Metodología: Esta serie considera tanto los casos notificados como los no notificados, es decir, aquellos que no han sido registrados por las autoridades sanitarias. Resultados: Este trabajo describe también cómo la información generada en este proyecto ha sido tratada y almacenada, presenta los datos de estimación de la incidencia real obtenidos, así como los organismos y equipos de investigación que la utilizan, además de los distintos canales de comunicación que han sido empleados para difundirla (página web, compartición de resultados con las autoridades sanitarias, y repositorio). Conclusión: Este trabajo integra información proveniente de múltiples fuentes de datos para el análisis y la predicción de la incidencia de la COVID-19. A través de un enfoque multidisciplinar, se ha logrado plantear respuesta a la problemática en la estimación de la incidencia real de casos de COVID-19.

Introduction: Epidemiological models have proven to be crucial in supporting the decision-making of health authorities during the COVID-19 pandemic as well as raising awareness among the general public of the different measures adopted by authorities (social distancing, mask usage, vaccination, etc.). Objectives: This work describes the methodology to integrate different data sources to generate a single time series that provides real incidence rates of COVID-19 in Spain. Methodology: This series considers both reported and non-notified cases, that is, those that have not been registered by health authorities. Results: This work also describes how the information generated in this project has been treated and stored, it presents the estimated real incidence data obtained, as well as the organizations and research teams that use it, and the different communication channels that have been used to disseminate it (webpage, sharing results with health authorities, and repository). Conclusion: This work integrates information from multiple data sources for the analysis and prediction of the incidence of COVID-19. Through a multidisciplinary approach, it has been possible to propose a response to the problem of estimating the real incidence of COVID-19 cases.

I/O bandwidth is a critical resource in an HPC cluster. As with all shared resources, its availability is impacted significantly by the users and the applications they execute. Without proper restrictions, jobs consuming more prominent portions of the I/O bandwidth can severely affect other jobs by notably prolonging their runtime. In such a context, applications that perform asynchronous I/O bring unique properties that allow for the reduction of such effects. That is, by limiting the bandwidth to the required one to perform the I/O in the background of the compute phases, I/O bursts can be flattened without significantly prolonging the application time, if at all. Hence, the bandwidth consumption of such applications is limited to what they need, sparing much of the system bandwidth to other applications. At the same time, these applications achieve higher parallel efficiency due to the overlapping of different resources (e.g., compute and I/O). This paper shows these aspects and demonstrates our approach to finding the required bandwidth for applications that use asynchronous I/O. Moreover, we apply it automatically using an MPI implementation of a bandwidth limitation approach at the application level. We validate our approach with several experiments on a large production cluster and show the impact of our approach on the application behavior and its importance for the system throughput.

The development of adaptive scheduling algorithms that take advantage of malleability has become a crucial area of research in many large-scale projects. Malleable workloads can improve the system’s performance but, at the same time, provide an extra dimension to the scheduling problem. This paper proposes an adaptive, performance-based job scheduling method that emphasizes the backfilling concept with malleability. The proposed method performs the malleability operations only when the estimated execution time of the involved applications is better than or equal to the execution time on the allocated resources without reconfiguration. The reconfiguration feasibility is determined by performance models considering the application scalability and reconfiguration overheads. Different policies for implementing malleability are presented, each targeting a specific workload in terms of job size and scalability. The comprehensive evaluation shows an improvement in the slowdown up to 49% compared to the non-adaptive baseline scheduling algorithm.

Current high-performance interconnection networks for high-performance computing and data-center systems incorporate mechanisms to prevent congestion from degrading network performance. Specifically, the popular InfiniBand specification defines a mechanism to reduce the injection rate of the traffic flows contributing to congestion. However, the efficiency of this mechanism depends on the values configured for certain parameters, that may be suitable for some congestion situations but not for others. Therefore, we think that these parameters should be reconfigured dynamically, based on accurate and updated information about the actual status of congestion. For that purpose, we have combined a light-weight platform monitoring tool (LIMITLESS) with the InfiniBand control software (OpenSM), so that the former provides the latter with enhanced knowledge about congestion to appropriately reconfigure the parameters driving the behavior of the congestion-control mechanism. Experiments performed in a real InfiniBand-based cluster confirm that this approach significantly reduces the number of wrong reactions to the congestion-control mechanism.