The Meningococcal Antigen Typing System (MATS) was developed to identify meningococcus group B strains with a high likelihood of being covered by the 4CMenB vaccine, but is limited by the requirement for viable isolates from culture-confirmed cases. We examined if antigen genotyping could complement MATS in predicting strain coverage by the 4CMenB vaccine. From a panel of 3912 MATS-typed invasive meningococcal disease isolates collected in England and Wales in 2007-2008, 2014-2015 and 2015-2016, and in 16 other countries in 2000-2015, 3481 isolates were also characterized by antigen genotyping. Individual associations between antigen genotypes and MATS coverage for each 4CMenB component were used to define a genetic MATS (gMATS). gMATS estimates were compared with England and Wales human complement serum bactericidal assay (hSBA) data and vaccine effectiveness (VE) data from England. Overall, 81% of the strain panel had genetically predictable MATS coverage, with 92% accuracy and highly concordant results across national panels (Lin's accuracy coefficient, 0.98; root-mean-square deviation, 6%). England and Wales strain coverage estimates were 72-73% by genotyping (66-73% by MATS), underestimating hSBA values after four vaccine doses (88%) and VE after two doses (83%). The gMATS predicted strain coverage in other countries was 58-88%. gMATS can replace MATS in predicting 4CMenB strain coverage in four out of five cases, without requiring a cultivable isolate, and is open to further improvement. Both methods underestimated VE in England. Strain coverage predictions in other countries matched or exceeded England and Wales estimates.

Background: The total incidence of invasive meningococcal disease (IMD) in Europe has been declining in recent years; however, a rising incidence due to serogroup W (MenW), predominantly sequence type 11 (ST-11), clonal complex 11 (cc11), was reported in some European countries. Aim: The aim of this study was to compile the most recent laboratory surveillance data on MenW IMD from several European countries to assess recent trends in Europe. Methods: In this observational, retrospective study, IMD surveillance data collected from 2013–17 by national reference laboratories and surveillance units from 13 European countries were analysed using descriptive statistics. Results: The overall incidence of IMD has been stable during the study period. Incidence of MenW IMD per 100,000 population (2013: 0.03; 2014: 0.05; 2015: 0.08; 2016: 0.11; 2017: 0.11) and the proportion of this serogroup among all invasive cases (2013: 5% (116/2,216); 2014: 9% (161/1,761); 2015: 13% (271/2,074); 2016: 17% (388/2,222); 2017: 19% (393/2,112)) continuously increased. The most affected countries were England, the Netherlands, Switzerland and Sweden. MenW was more frequent in older age groups (≥ 45 years), while the proportion in children (< 15 years) was lower than in other age groups. Of the culture-confirmed MenW IMD cases, 80% (615/767) were caused by hypervirulent cc11. Conclusion: During the years 2013–17, an increase in MenW IMD, mainly caused by MenW cc11, was observed in the majority of European countries. Given the unpredictable nature of meningococcal spread and the epidemiological potential of cc11, European countries may consider preventive strategies adapted to their contexts.

Serogroup B meningococcal (MenB) isolates currently account for approximately 90% of invasive meningococcal disease (IMD) in Greece with ST-162 clonal complex predominating. The potential of a multicomponent meningococcal B vaccine (4CMenB) recently licensed in Europe was investigated in order to find whether the aforementioned vaccine will cover the MenB strains circulating in Greece. A panel of 148 serogroup B invasive meningococcal strains was characterized by multilocus sequence typing (MLST) and PorA subtyping. Vaccine components were typed by sequencing for factor H-binding protein (fHbp), Neisserial Heparin Binding Antigen (NHBA) and Neisseria adhesin A (NadA). Their expression was explored by Meningococcal Antigen Typing System (MATS).

Differential diagnosis between bacterial and viral meningitis is crucial. In our study, to differentiate bacterial vs. viral meningitis, three machine learning (ML) algorithms (multiple logistic regression (MLR), random forest (RF), and naïve-Bayes (NB)) were applied for the two age groups (0–14 and >14 years) of patients with meningitis by both conventional (culture) and molecular (PCR) methods. Cerebrospinal fluid (CSF) neutrophils, CSF lymphocytes, neutrophil-to-lymphocyte ratio (NLR), blood albumin, blood C-reactive protein (CRP), glucose, blood soluble urokinase-type plasminogen activator receptor (suPAR), and CSF lymphocytes-to-blood CRP ratio (LCR) were used as predictors for the ML algorithms. The performance of the ML algorithms was evaluated through a cross-validation procedure, and optimal predictions of the type of meningitis were above 95% for viral and 78% for bacterial meningitis. Overall, MLR and RF yielded the best performance when using CSF neutrophils, CSF lymphocytes, NLR, albumin, glucose, gender, and CRP. Also, our results reconfirm the high diagnostic accuracy of NLR in the differential diagnosis between bacterial and viral meningitis.