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

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

Research output: Contribution to journalArticle

3 Citations (Scopus)


BackgroundMathematical 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.MethodsUsing TVA algorithm, six distinct time series were modelled, each one representing the number of episodes per single `ESKAPE¿ ( E nterococcus faecium, S taphylococcus aureus, K lebsiella pneumoniae, A cinetobacter baumannii, P seudomonas aeruginosa and E nterobacter species) infecting pathogen, that had occurred monthly between 2002 and 2011 calendar years at the Università Cattolica del Sacro Cuore general hospital.ResultsMonthly moving averaged numbers of observed and forecasted ESKAPE infectious episodes were found to show a complete overlapping of their respective smoothed time series curves. Overall good forecast accuracy was observed, with percentages ranging from 82.14% for E. faecium infections to 90.36% for S. aureus infections.ConclusionsOur approach may regularly provide physicians with forecasted bacterial infection rates to alert them about the spread of antibiotic-resistant bacterial species, especially when clinical microbiological results of patients¿ specimens are delayed.
Original languageEnglish
Pages (from-to)634-634
Number of pages1
JournalBMC Infectious Diseases
Publication statusPublished - 2014




Dive into the research topics of 'Forecasting ESKAPE infections through a time-varying auto-adaptive algorithm using laboratory-based surveillance data'. Together they form a unique fingerprint.

Cite this