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.
Ballarin, A., Posteraro, B., Demartis, G., Gervasi, S., Panzarella, F., Torelli, R., Paroni Sterbini, F., Morandotti, G. A., Posteraro, P., Ricciardi, G., Gervasi Vidal, K., Sanguinetti, M., Forecasting ESKAPE infections through a time-varying auto-adaptive algorithm using laboratory-based surveillance data, <<BMC INFECTIOUS DISEASES>>, 2014; 14 (1): 634-634. [doi:10.1186/s12879-014-0634-9] [http://hdl.handle.net/10807/65997]
Forecasting ESKAPE infections through a time-varying auto-adaptive algorithm using laboratory-based surveillance data
Posteraro, Brunella;Torelli, Riccardo;Paroni Sterbini, Francesco;Morandotti, Grazia Angela;Ricciardi, Gualtiero;Sanguinetti, Maurizio
2014
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.
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