BMC Medical Informatics and Decision Making (Jul 2003)

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

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

DOI
https://doi.org/10.1186/1472-6947-3-9
Journal volume & issue
Vol. 3, no. 1
p. 9

Abstract

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Abstract 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.