Studying infection-related diseases, such as those associated with cardiological surgical interventions, often requires the acquisition and analysis of heterogeneous data, including bioimages, microbiological data, and blood analytes. Multidisciplinary collaboration among clinicians and specialists is also essential for effective data management and to develop strategies for the prevention and treatment of infections. Acquiring and analyzing data for clinical studies is a vital approach to preventing infectious diseases. In this context, the REDCap platform provides a comprehensive solution for collecting, managing, and analyzing clinical and hospitalization information. REDCap enables data entry validation, automated reporting, and data integration, which facilitates data management and ensures data quality.This contribution describes a REDCap-based method to support predictive clinical studies on infective endocarditis. The application aims to advance clinical research on this disease, improving understanding and fostering the development of effective treatment strategies, while assisting clinicians in defining cardiac infection prevention methods.

Epidemiological models are relevant to study and analyze clinical as well as environmental and behavioural data, useful to support health studies. The target is to perform epidemiological analysis producing fast and reliable data access useful to guide prevention and curing processes. This is currently true in pandemic emergency as the current Covid-19 context. Epidemiological models should support in the early identification of pandemic phenomena and in making available data set for studying more accurate drug-based strategy for vaccines or virus containment.In this contribution we present an epidemiology database which integrates different types of clinical data to support research, follow-up and patient monitoring. The idea starts from an hospital databases cooperation integration where virus available data have been integrated to support statistical based studies. Starting from an available database containing 5 years data of infection related viruses (such as HPC, hepatitis) and patient anonymous data, the proposed system provide an integrated data access able to (i) extracting data filtered by means of clinical hypothesis based on patient profiles, environment and drugs and (ii) allowing to build large scale geographical data mappings in order to study correlations among chronic infection diseases and their relations with upcoming pandemic phenomena. Even if the application is in its infancy, the application is relevant with high very important applications.

SUMMARY: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface. AVAILABILITY AND IMPLEMENTATION: Source code, binaries and documentation are available on http://rega.kuleuven.be/cev/regadb. RegaDB is written in the Java programming language, using a web-service-oriented architecture.