biostudies-literatureAlawieh ANIGMS NIH HHS174-183https://www.ebi.ac.uk/biostudies/studies/S-EPMC4666607biostudies-literatureUnknown3(3)Current concern over the emergence of multidrug-resistant superbugs has renewed interest in approaches that can monitor existing trends in bacterial resistance and make predictions of future trends. Recent advances in bacterial surveillance and the development of online repositories of susceptibility tests across wide geographical areas provide an important new resource, yet there are only limited computational tools for its exploitation. Here we propose a hybrid computational model called BARDmaps for automated analysis of antibacterial susceptibility tests from surveillance records and for performing future predictions. BARDmaps was designed to include a structural computational model that can detect patterns among bacterial resistance changes as well as a behavioural computational modelJournal of global antimicrobial resistanceA computational model to monitor and predict trends in bacterial resistance.PMC4666607R01 GM066861Bizri ARSabra ZZaraket FADavies CAlawieh AWhite RfalseA computational model to monitor and predict trends in bacterial resistance.Current concern over the emergence of multidrug-resistant superbugs has renewed interest in approaches that can monitor existing trends in bacterial resistance and make predictions of future trends. Recent advances in bacterial surveillance and the development of online repositories of susceptibility tests across wide geographical areas provide an important new resource, yet there are only limited computational tools for its exploitation. Here we propose a hybrid computational model called BARDmaps for automated analysis of antibacterial susceptibility tests from surveillance records and for performing future predictions. BARDmaps was designed to include a structural computational model that can detect patterns among bacterial resistance changes as well as a behavioural computational model2015-01-01T00:00:00Z2015 Sep2025-04-03T22:41:41.243Z2019-03-27T02:03:04ZS-EPMC46666072664077510.1016/j.jgar.2015.04.006