Forecasting ESKAPE infections through a time-varying auto-adaptive algorithm using laboratory-based surveillance data

Brunella Posteraro, Gualtiero Ricciardi, Maurizio Sanguinetti, Riccardo Torelli, Francesco Paroni Sterbini, Grazia Angela Morandotti, Antonio Ballarin, Giuseppe Demartis, Simona Gervasi, Fabrizio Panzarella, Patrizia Posteraro, Kristian A. Gervasi Vidal

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Mathematical or statistical tools are capable to provide a valid help to improve surveillance systems for healthcare and non-healthcare-associated bacterial infections. The aim of this work is to evaluate the time-varying auto-adaptive (TVA) algorithm-based use of clinical microbiology laboratory database to forecast medically important drug-resistant bacterial infections.
Original languageEnglish
Pages (from-to)634-634
Number of pages1
JournalBMC Infectious Diseases
Volume14
DOIs
Publication statusPublished - 2014

Keywords

  • Forecasting

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