title: Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines.
abstract: BACKGROUND
  Estimating the effectiveness of meningococcal vaccines with high accuracy and precision can be challenging due to the low incidence of the invasive disease, which ranges between 0.5 and 1 cases per 100,000 in Europe and North America. Vaccine effectiveness (VE) is usually estimated with a screening method that combines in one formula the proportion of meningococcal disease cases that have been vaccinated and the proportion of vaccinated in the overall population. Due to the small number of cases, initial point estimates are affected by large uncertainties and several years may be required to estimate VE with a small confidence interval.
  
  
  METHODS
  We used a Monte Carlo maximum likelihood (MCML) approach to estimate the effectiveness of meningococcal vaccines, based on stochastic simulations of a dynamic model for meningococcal transmission and vaccination. We calibrated the model to describe two immunization campaigns: the campaign against MenC in England and the Bexsero campaign that started in the UK in September 2015. First, the MCML method provided estimates for both the direct and indirect effects of the MenC vaccine that were validated against results published in the literature. Then, we assessed the performance of the MCML method in terms of time gain with respect to the screening method under different assumptions of VE for Bexsero.
  
  
  RESULTS
  MCML estimates of VE for the MenC immunization campaign are in good agreement with results based on the screening method and carriage studies, yet characterized by smaller confidence intervals and obtained using only incidence data collected within 2 years of scheduled vaccination. Also, we show that the MCML method could provide a fast and accurate estimate of the effectiveness of Bexsero, with a time gain, with respect to the screening method, that could range from 2 to 15 years, depending on the value of VE measured from field data.
  
  
  CONCLUSIONS
  Results indicate that inference methods based on dynamic computational models can be successfully used to quantify in near real time the effectiveness of immunization campaigns against Neisseria meningitidis. Such an approach could represent an important tool to complement and support traditional observational studies, in the initial phase of a campaign.
authors.affiliation: Department of Physics and INFN, University of Turin, via Giuria 1, Turin, 10125, Italy. lorenzo.argante@isi.it.|ISI Foundation, via Alassio 11/C, Turin, 10126, Italy. lorenzo.argante@isi.it.|GSK Vaccines, Siena, Italy. lorenzo.argante@isi.it.
authors.forename: Lorenzo
authors.initials: L
authors.lastname: Argante
authors.affiliation: ISI Foundation, via Alassio 11/C, Turin, 10126, Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Tizzoni
authors.affiliation: GSK Vaccines, Siena, Italy.
authors.forename: Duccio
authors.initials: D
authors.lastname: Medini
mesh_terms: D003198:Computer Simulation; D004739:England; D005060:Europe; D006801:Humans; D017589:Immunization Programs / Q000706:statistics & numerical data*; D015994:Incidence; D008589:Meningococcal Infections / Q000517:prevention & control*; D022401:Meningococcal Vaccines / Q000627:therapeutic use*; D009010:Monte Carlo Method; D009345:Neisseria meningitidis; D009656:North America; D014611:Vaccination / Q000379:methods
chemical_list: D022401:Meningococcal Vaccines
keywords: Bexsero; Computational dynamic models; Invasive meningococcal disease; Meningococcal carriage; Monte Carlo maximum likelihood; Neisseria meningitidis; Vaccine effectiveness