The importance of antibiotic-resistance phenomenon and its worldwide spread have given rise to the activation of numerous surveillance systems. To make the data collected by these systems homogeneous and interpretable and to facilitate comparison between the various countries, a European surveillance network was created in 2000 which in 2010 assumed institutional characteristics becoming the European network European Antimicrobial Resisitance Surveillance Network (EARS-Net) coordinated by the ECDC.</p><p>The aim of this work was to evaluate the antibiotic-resistance profile analyzing the whole genome of 24 A. baumannii strains isolated from different departments of Alessandria (Italy) hospital, in the Covid-19 era. It was a relevant project for determining the genomic proximity between strains isolated during the period considered, especially to trace their origin.</p><p>Methods. The isolation of A. baumannii strains took place in the period of the Sars-CoV-2 pandemic. Genomic DNA was extracted from over-night cultures in Mueller-Hinton Broth, using the DNeasy UltraClean Microbial kit. The DNA was then quantified and shotgun libraries built using the Nextera XT DNA Library Prep kit. After product purification, the libraries were normalized and sequenced using MiSeq. The obtained sequences were bioinformatically analysed using different softwares: TrimmoMatic, PhRed, SPAdes, CheckM, Mash, Prodigal, EggNOG, FiloFlan, and RGI.</p><p>Results. Here, we propose a new work-flow in order to map epidemic clusters at the hospital level. Furthermore, the proposed parameter of Mash Index, and the relative trashold of 0.0052, is reliable and validable in determining the similarity between strains and in monitoring their diffusion.