Reproducibility is the ability to reproduce a study using the same data and protocol. Many disciplines face challenges with reproducibility raising concerns about the reliability of science. Fields relying on data analysis, such as the social sciences, health economics, and epidemiology, are also affected. A central factor in this issue is data analysis strategy. Effective analysis involves numerous choices: study design, theoretical model, assumptions, judgment criteria, parameter settings, and data quality. The complexity of statistical analysis and programming can also limit reproducibility. Among complex analysis, state sequence analysis (SSA) enables the exploration of temporal patterns and transitions over time. We developed and evaluated SSAW (State Sequence Analysis Workflow), a scientific workflow which simplifies and standardizes SSA (https://github.com/bakrimmadi/SSA_workflow2). SSAW automates key steps of SSA, ranging from data transformation, dissimilarity selection, and hyperparameter tuning to clustering and visualization while maintaining full documentation of each stage. Built on Snakemake, SSAW ensures reproducibility through isolated Conda environments and supports both non-expert and expert users. The quality of the workflow was assessed based on several criteria, such as modularity, reproducibility, sustainability, and transparency, but also by evaluating SSAW’s compliance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. We showed that SSAW complies with the FAIR principles and other complementary workflow quality criteria. To assess its applicability, we applied SSAW to a publicly available sequence dataset which followed 712 individuals aged 16–19 monthly from September 1993 to June 1999, aiming to identify subgroups most at risk of long-term unemployment. Using SSAW, we obtain clear visualizations that facilitate the interpretation of trajectories and clusters. The results were consistent with those reported in the published analysis. SSAW not only streamlines the entire analysis but also introduces an innovative strategy to guide clustering decisions based on statistical coherence and stability. SSAW is the first FAIR-compliant framework for SSA and it significantly improved the reproducibility and traceability of analysis choices, bridging the gap between advanced methodological requirements and the need for transparent and reproducible practices. By combining methodological innovation with FAIR principles, SSAW makes trajectory analysis more transparent, reusable, and accessible across disciplines.

Decision-making during critical outbreak management may require standard strategies, but also more creative ones. Our goal was to characterize the expert decision processes that take place during critical situations, where rule-based strategies and usual procedures cannot be satisfactorily applied. More specifically, we focused on the strategies experts use to deal with epidemiological problems, depending on the complexity of the situation. To this end, we carried out a simulated outbreak alert, to place two experts in a situation of epidemiological problem management, based on usual practice but also conducive to implementing creative solutions. To analyze the data, we considered not only the relevance of the solutions proposed by the experts, but also the four creativity criteria defined by Torrance (fluency, flexibility, elaboration and originality). Results allowed us to identify similarities but also differences between the solutions proposed by the experts, depending on their level of experience in this area.

In late 2019, an epidemic of SARS-CoV-2 broke out in central China. Within a few months, this new virus had spread right across the globe, officially being classified as a pandemic on 11 March 2020. In France, which was also being affected by the virus, the government applied specific epidemiological management strategies and introduced unprecedented public health measures. This article describes the outbreak management system that was applied within the French military and, more specifically, analyzes an outbreak of COVID-19 that occurred on board a nuclear aircraft carrier. We applied the AcciMap systemic analysis approach to understand the course of events that led to the outbreak and identify the relevant human and organizational failures. Results highlight causal factors at several levels of the outbreak management system. They reveal problems with the benchmarks used for diagnosis and decision-making, and underscore the importance of good communication between different levels. We discuss ways of improving epidemiological management in military context.

Following publication of the original article [1], the authors reported that one of the authors' names is spelled incorrectly.

Infective endocarditis is a severe disease with high mortality. Despite a global trend towards an increase in staphylococcal aetiologies, in older patients and a decrease in viridans streptococci, we have observed in recent studies great epidemiologic disparities between countries. In order to evaluate these differences among Mediterranean countries, we performed a PubMed search of infective endocarditis case series for each country. Data were available for 13 of the 18 Mediterranean countries. Despite great differences in diagnostic strategies, we could classify countries into three groups. In northern countries, patients are older (>50 years old), have a high rate of prosthetic valves or cardiac electronic implantable devices and the main causative agent is Staphylococcus aureus. In southern countries, patients are younger (<40 years old), rheumatic heart disease remains a major risk factor (45-93%), viridans streptococci are the main pathogens, zoonotic and arthropod-borne agents are frequent and blood culture-negative endocarditis remains highly prevalent. Eastern Mediterranean countries exhibit an intermediate situation: patients are 45 to 60 years old, the incidence of rheumatic heart disease ranges from 8% to 66%, viridans streptococci play a predominant role and zoonotic and arthropod-borne diseases, in particular brucellosis, are identified in up to 12% of cases.

This study focused on the application of Torrance framework about creative thinking in a complex professional context: the management and control of an outbreak by experts in epidemiology and public health. We argue that building accurate responses in this context depends on the complexity of situations, such as 'epidemiologic problems' experts have to deal with. Thus, depending on the problem's complexity, experts could possibly adapt their problem solving strategies, using either 'standard' strategies or more 'creative' ones. Our goal was to characterize expert decision processes developed during critical situation (where rule-based strategies and usual procedures could be not satisfyingly applied) with regard to creativity criteria described by Torrance (fluency, flexibility, elaboration and originality). We carried out a simulated outbreak alert to study creative processes during experts problem-solving activities. This simulation was intended to put specialists in a context of epidemiological problem management, based on possible real practice but conducive to implement creative solutions. The analysis carried out on the observations allowed us to identify a total of 14 different themes, with 148 ideas expressed by the participants. The participants have therefore actively contributed to the elaboration of ideas as well as to the mutual enrichment and implementation of ideas. However, the number of evocated topics and ideas and their level of elaborations appears higher when epidemiologists are more experienced in their domain. Thus, creative thinking appears to be an important aspect of the epidemiological alert management and related to experience in this area.

•This work investigates how experts develop strategies to address uncertainty during the management of an outbreak with the help of an early warning system.•We confirm the level of uncertainty and quantify mechanisms involved in outbreak management.•We detail tools and systems used to support experts in their coping strategies.•We propose that surveillance systems include different features to provide relevant information that can help users reduce uncertainty.

Background Most studies of epidemic detection focus on their start and rarely on the whole signal or the end of the epidemic. In some cases, it may be necessary to retrospectively identify outbreak signals from surveillance data. Our study aims at evaluating the ability of change point analysis (CPA) methods to locate the whole disease outbreak signal. We will compare our approach with the results coming from experts’ signal inspections, considered as the gold standard method. Methods We simulated 840 time series, each of which includes an epidemic-free baseline (7 options) and a type of epidemic (4 options). We tested the ability of 4 CPA methods (Max-likelihood, Kruskall-Wallis, Kernel, Bayesian) methods and expert inspection to identify the simulated outbreaks. We evaluated the performances using metrics including delay, accuracy, bias, sensitivity, specificity and Bayesian probability of correct classification (PCC). Results A minimum of 15 h was required for experts for analyzing the 840 curves and a maximum of 25 min for a CPA algorithm. The Kernel algorithm was the most effective overall in terms of accuracy, bias and global decision (PCC = 0.904), compared to PCC of 0.848 for human expert review. Conclusions For the aim of retrospectively identifying the start and end of a disease outbreak, in the absence of human resources available to do this work, we recommend using the Kernel change point model. And in case of experts’ availability, we also suggest to supplement the Human expertise with a CPA, especially when the signal noise difference is below 0.

The naturalistic decision-making (NDM) approach deals with how humans make decisions in natural settings, especially professional situations, which can be difficult to reproduce in experimental laboratory studies. NDM explores collaboration and cooperation both between humans, and between humans and systems, as well as situations of diagnosis, planning, supervision, and control processes. Basically, decision researchers focus on the analysis of humans at work and their interactions with systems in context, including both environmental and social dimensions. Since the movement was founded, more than 20 years ago, several models of decision making have been developed, each offering a fresh view on how humans perform complex cognitive functions to accomplish situated activities. This very different way of studying human interactions in modern work environments has given rise to a worldwide research community sharing the same issues and a common theoretical background, and relying on ecological models of decision making (Brehmer 1992; Endsley 1995; Hutchins 1995; Rasmussen et al. 1994); models of intelligence, perception, and action as mental models (Johnson-Laird 1983); and activity theory (Engestrom 1999; Kuutti 1996; Nardi 1996). Looking further back, the field of naturalistic decision making can be seen to have its historical roots in the ground-breaking work of Vygotsky and Leont’ev (Leont’ev 1978; Leont’ev and Luria 1968), and even Piaget’s intelligence theory (Piaget 1972, 1977). The NDM approach, which is predicated upon a strong relationship between application fields, research, and models of complex cognitive tasks, is responsible for a now well-established definition of decision making (Klein et al. 1993): ‘‘eight important factors characterize decision making in naturalistic settings, but frequently are ignored in decision-making research. It is not likely that all eight factors will be at their most difficult levels in any one setting, but often several of these factors will complicate the decision task.

Unlike usual surveillance systems, the ASTER system must provide a unified monitoring of several military populations exposed to different biological and chemical threats, and the surveillance of each population must be tailored to meet its specific risk profile. For coping with these requirements, we have developed a formal surveillance system model we have used for designing the system architecture and the webservices collaborations. The system currently covers populations in desertic areas as well as in Amazonian Forest. This versatility allowed a quick and easy system tailoring for the recent French Deployments in Jordanian Refugees Camps or in Mali.

Digital tools cannot be separated from their users, the activities for which they are built, and their utilisation context. They require from professionals a learning of how to work with systems that are more or less easy to use, and a modification, sometimes complete, of their practices and organisations. It is why this chapter introduces a human factor approach of medical informatics, and specifically a work analysis approach, which is a methodological approach aiming to collect essential data in order to describe the psychological, physical, social, technical and economic conditions within which an operator performs a set of tasks or activities that constituted his work. After presenting the overall context of computerisation pressure associated with the evolution of health work systems and the possible risks and stakes it brings, this chapter describes the concepts of ergonomics and human factors for medical informatics projects. It specifically focuses on work situation analysis, user-centered design with a practical example concerning the implementation of a CPO system, and concludes with a broader approach opening the concept of medical socio-technical system.

En 2004, le service de santé des armées (SSA), pour aider ses personnels médicaux à obtenir rapidement de l’information avant d’envoyer des forces en opération, a développé un système de veille sanitaire de défense accessible à partir d’un intranet. Or, dans cette organisation, aucun métier de la médiation n’existe pour permettre l’accès des usagers aux ressources électroniques du système, à part celui de bibliothécaire et de conservateur de musée. Pour résoudre cette problématique, quatre nouveaux métiers de la médiation des ressources documentaires ont émergé : le médiateur de documents, de contenu, de conception/diffusion et des usages. Le médiateur de documents est un cyberdocumentaliste qui collecte les informations par les outils de type pull/push et fils RSS. Il les analyse et ne transmet que ceux indispensables au médiateur de contenu. Ce médiateur, de compétence médicale et militaire, rédige, à partir de ces informations, le contenu du système. Le médiateur des usages, psychologue-ergonome, intervient dans l’aide à la conception des interfaces en proposant des recommandations d’organisation au médiateur de conception/diffusion. Ce dernier se décline en 3 acteurs : ingénieur système d’information, technicien système-réseau et webmestre, qui travaillent en concertation. Ceux-ci conçoivent, maintiennent le système et diffusent les contenus aux usagers.

Introduction: We examined the process of decision making related to diagnosis in paramedic teams in an international competition. Method: Observation of 28 paramedic teams in selected task was compared with objective medical evaluation of their performance in the whole competition. Results and discussion: The real process of examining the patient and establishing the diagnosis by the paramedics is not in accordance with the prescribed procedures. Paramedics show a tendency to make assumptions about the case from early steps of dealing with it, which has a strong influence on the subsequent process of examination of the patient and establishing diagnosis.

La surveillance épidémiologique dans l’OTAN est née en 1995 en Bosnie-Herzégovine avec la mise en oeuvre du système EpiNATO. Ce système, souffrant de nombreux défauts le rendant peu efficace au regard de ses objectifs, est actuellement le seul système de surveillance épidémiologique propre à l’OTAN. La surveillance épidémiologique de l’Alliance repose donc en grande partie sur les systèmes nationaux. Le Deployment Health Surveillance Capability (DHSC) est un service créé à Munich en 2010 à partir d’une initiative franco-allemande et dont l’objectif est de devenir le centre d’épidémiologie des déploiements de l’OTAN. Ses missions actuelles sont de réécrire la doctrine de la surveillance épidémiologique des déploiements (AMedP-21), de moderniser le système EpiNATO pour le rendre plus efficace et adapté aux formes actuelles de déploiement et de développer l’interopérabilité du système français ASTER (Alerte et Surveillance en Temps Réel) dans l’optique d’une utilisation par l’OTAN lors des futures opérations.

We conducted an exploratory study of a complex and dynamic medical activity, namely the collective management of an epidemiological alert situation. With a view to improving our knowledge of how this activity is managed, we set up simulated situations of epidemiological alerts. A multidisciplinary medical team was assisted by a decision-support system called ASTER and we recorded a set of systematised observations of human–human and human–machine interactions. Participants were physicians belonging to the Department of Epidemiology at the French Army's Institute of Tropical Medicine. After presenting the epidemiological domain and our theoretical approach, we describe the simulated situation and the communication dataset we collected and analysed, applying the EORCA method. Finally, in our discussion of the results, we suggest how communication could be enhanced between technology-mediated teams in complex and dynamic situations.

Résumé Nous proposons une méthode, eorca (Event Oriented Representation for Collaborative Activities), d’observation systématisée et de formalisation des tâches. Elle permet de mettre en évidence l’ensemble des actions individuelles et collectives mises en œuvre par les membres d’une équipe médicale lors d’une résolution de situation. Les caractéristiques fondamentales pour employer avec efficacité eorca sont les suivantes : une situation de supervision de situation complexe contrainte temporellement et partiellement contrôlée par les opérateurs, un espace commun regroupant les acteurs en un collectif de base et des systèmes assistant les opérateurs. L’objectif de cet article est de présenter cette méthode et d’en illustrer son application dans le cadre de l’étude, en simulation, de la gestion d’une alerte épidémiologique précoce par une équipe de médecins spécialistes.

Motivation -- This presentation introduces a near-real time outbreak surveillance system, ASTER, which assists physicians in the resolution the management of the outbreak early warning in French military deployment. Research approach . Our approach is to show that ASTER could be described as a joint cognitive system between actors belonging to a specific socio-technical network, a surveillance network and an artificial decision-supported system. Findings/Design -- Two simulations of an outbreak management have been set up. Observations of epidemiologists (analysis network) were conducted during simulated scenarii involving natural and intentional outbreaks within French Forces deployed for the first scenario, in Djibouti, and for the second one, in Tchad. Originality/Value -- The results of these studies highlight the central role of the building of a shared problem representation. This representation appears mainly to result from cooperative activities during decision making processes which are strongly supported by the main system, ASTER, but also by a panel of other decision-support systems and non-computerized and more classical artefacts.