EURISWEB - Web-based epidemiological surveillance of antibiotic-resistant pneumococci in Day Care Centers

Sara Silva, Rodrigo Gouveia-Oliveira, António Maretzek, João André Carriço, Thorolfur Gudnason, Karl G. Kristinsson, Karl Ekdahl, António Brito-Avô, Alexander Tomasz, Ilda Santos Sanches, Hermínia De Lencastre, Jonas Almeida

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Background: EURIS (European Resistance Intervention Study) was launched as a multinational study in September of 2000 to identify the multitude of complex risk factors that contribute to the high carriage rate of drug resistant Streptococcus pneumoniae strains in children attending Day Care Centers in several European countries. Access to the very large number of data required the development of a web-based infrastructure - EURISWEB - that includes a relational online database, coupled with a query system for data retrieval, and allows integrative storage of demographic, clinical and molecular biology data generated in EURIS. Methods: All components of the system were developed using open source programming tools: data storage management was supported by PostgreSQL, and the hypertext preprocessor to generate the web pages was implemented using PHP. The query system is based on a software agent running in the background specifically developed for EURIS. Results: The website currently contains data related to 13,500 nasopharyngeal samples and over one million measures taken from 5,250 individual children, as well as over one thousand pre-made and user-made queries aggregated into several reports, approximately. It is presently in use by participating researchers from three countries (Iceland, Portugal and Sweden). Conclusion: An operational model centered on a PHP engine builds the interface between the user and the database automatically, allowing an easy maintenance of the system. The query system is also sufficiently adaptable to allow the integration of several advanced data analysis procedures far more demanding than simple queries, eventually including artificial intelligence predictive models.

Original languageEnglish
Article number9
Pages (from-to)1-13
Number of pages13
JournalBMC Medical Informatics and Decision Making
Publication statusPublished - 8 Jul 2003


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