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This paper presents the components and integrated outcome of a system that aims to achieve early detection, monitoring and mitigation of pandemic outbreaks. The architecture of the platform aims at providing a number of pandemic-response-related services, on a modular basis, that allows for the easy customization of the platform to address user's needs per case. This customization is achieved through its ability to deploy only the necessary, loosely coupled services and tools for each case, and by providing a common authentication, data storage and data exchange infrastructure. This way, the platform can provide the necessary services without the burden of additional services that are not of use in the current deployment (e.g., predictive models for pathogens that are not endemic to the deployment area). All the decisions taken for the communication and integration of the tools that compose the platform adhere to this basic principle. The tools presented here as well as their integration is part of the project STAMINA. [ABSTRACT FROM AUTHOR]

Pandemic crises are disruptive events that imply a threat to the health of citizens, and also to public safety. In order to provide an adequate response, Law Enforcement Agencies (LEAs) organizations up to now had to adapt their structures, staffing conditions and competencies to the exceptional circumstances. At the same time, pandemics, such as COVID-19 that is currently a real scenario, require from LEAs to test their capabilities and thus to further identify their own gaps and get to know themselves better. The complexity of this kind of phenomena requires a coordinated and multidisciplinary response through Information Technology (IT) tools to mitigate the effects of pandemics. In this sense, our participation in the European H2020 STAMINA project: "Demonstration of intelligent decision support for pandemic crisis prediction and management within and across European borders" brings added value to our daily work as LEAs. The project implements a set of tools whose goal is twofold: improvement of management of information in all phases of the pandemic as well as improvement of response and coordination among all first responders involved in a pandemic. STAMINA attempts to achieve this through the combination of a number of IT tools ranging from Predictive models and Early Warning systems to Real-time Social Media Analytics and a Common Operational Picture (COP) platform that acts as the main interface for real-time situation assessment and coordinated responses of the involved LEAs. [ABSTRACT FROM AUTHOR]

• Addressing automatic re-identification of pulmonary nodules with siamese networks. • Pretrained siamese networks allow successfully rank similarity between nodules. • Best settings with binary cross entropy, fully connected, and features from early layers. • Prior image registration is avoided providing computationally faster results. • Accurate detection of nodule growth embedding a nodule re-identification network. Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient need to be individually inspected, looking for possible cancerogenous nodules. Radiologists mainly focus their attention in nodule size, density, and growth to assess the existence of malignancy. In this study, we present a novel method based on a 3D siamese neural network, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration. The network was integrated into a two-stage automatic pipeline to detect, match, and predict nodule growth given pairs of CT scans. Results on an independent test set reported a nodule detection sensitivity of 94.7%, an accuracy for temporal nodule matching of 88.8%, and a sensitivity of 92.0% with a precision of 88.4% for nodule growth detection. [ABSTRACT FROM AUTHOR]