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Introduction: The study presents the results of the genomic surveillance of invasive meningococcal disease (IMD) in the Czech Republic for the period of 2015–2017. Material and methods: The study set includes all available IMD isolates recovered in the Czech Republic and referred to the National Reference Laboratory for Meningococcal Infections in 2015–2017, a total of 89 Neissseria meningitidis isolates—from 2015 (n = 20), 2016 (n = 27), and from 2017 (n = 42). All isolates were studied by whole genome sequencing (WGS). Results: Serogroup B (MenB) was the most common, followed by serogroups C, W, and Y. Altogether 17 clonal complexes were identified, the most common of which was hypervirulent complex cc11, followed by complexes cc32, cc41/44, cc269, and cc865. Over the three study years, hypervirulent cc11 (MenC) showed an upward trend. The WGS method showed two clearly differentiated clusters of N. meningitidis C: P1.5,2:F3-3:ST-11 (cc11). The first cluster is represented by nine isolates, all of which are from 2017. The second cluster consisted of five isolates from 2016 and eight isolates from 2017. Their genetic discordance is illustrated by the changing nadA allele and subsequently by the variance in BAST type. Clonal complex cc269 (MenB) also increased over the time frame. WGS identified the presence of MenB vaccine antigen genes in all B and non-B isolates of N. meningitidis. Altogether 49 different Bexsero antigen sequence types (BAST) were identified and 10 combinations of these have not been previously described in the PubMLST database. Conclusions: The genomic surveillance of IMD in the Czech Republic provides data needed to update immunisation guidelines for this disease. WGS showed a higher discrimination power and provided more accurate data on molecular characteristics and genetic relationships among invasive N. meningitidis isolates. [ABSTRACT FROM AUTHOR]

Legionella spp. are considered an important cause of potentially preventable morbidity and mortality, making environmental surveillance a crucial component of risk assessment plans. In this work, 20,319 water samples were collected in 3,983 environmental surveys during a 16-year period by ARPA, the Regional Agency for Environmental Protection, Friuli Venezia Giulia, and the results were studied to better understand the diffusion mechanisms of Legionella. The data showed a strong seasonal signal, a prevalence of L. pneumophila serogroup 2–15 in most environments (63% of positive samples), a prevalence of L. pneumophila serogroup 1 in swimming pool-associated environments (82% of positive samples), a persistent presence of Legionella in hospitals and a recurrent presence of Legionella in other facilities such as hotels, possibly years after interventions, highlighting the difficulty of eradicating the bacteria. Retrospective spatio-temporal analyses on geocoded historical data were carried out with SaTScan using an ordinal model with risk as a covariate to identify potential clusters with an excess of cases in the higher-risk categories. Although no outbreaks occurred during the period of study, such analyses identified spatially restricted zones with unusual contamination, which sometimes were also areas in which several surveys triggered by notifications of clinical cases were performed. Simulations of periodic prospective analyses permitted the assessment of the efficacy of the method in early detection of such clusters. The proposed method may be a useful tool in environmental surveillance, prevention and control of Legionella. [ABSTRACT FROM AUTHOR]