title: Estimating vaccine coverage from serial trivariate serologic data in the presence of waning immunity.
abstract: BACKGROUND
  Vaccine coverage data are typically collected through vaccine registers and retrospective surveys. Alternatively, cross-sectional serosurveys enable direct estimation of vaccine coverage from antibody prevalence by exploiting correlated seropositivity for multi-antigen vaccines. Here, we extend previous methods by accounting for temporal antibody decline in estimating vaccine coverage for measles-mumps-rubella vaccine using serial serosurvey data.
  
  
  METHODS
  We introduce a Markovian cohort model of antibody waning and boosting applied to dichotomous seropositivity data for measles, mumps, and rubella. Simulation studies are used to test model identifiability and to explore bias induced by previous methods that ignore waning. The cohort model is then fitted to three Australian serosurveys, entailing estimates of vaccine coverage from routine and catch-up vaccination as well as waning rates for each antigen.
  
  
  RESULTS
  The simulation results show that the cohort model is identifiable and qualitatively captures the decline in seropositivity observed in older children. When fitted to all three Australian surveys, the estimated seroconversion and waning parameters are similar to estimates based on recent meta-analyses, whereas the coverage estimates appear consistent with previous Australian survey-based estimates.
  
  
  CONCLUSIONS
  We show that previous methods of estimating coverage from serological data can be improved by fitting a cohort model with waning and boosting processes to serial serosurvey data, furthermore yielding estimates of more parameters of interest such as rates of waning. In settings where serial serosurvey data is available, our method could be duplicated or applied to related questions such as coverage in routine two-dose schedules or from other combination vaccines.
authors.affiliation: From the aSchool of Public Health and Community Medicine, UNSW, Sydney, Australia; bInteruniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium; cCentre for Health Economics Research and Modeling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Campus Drie Eiken, Antwerp, Belgium; dNational Centre for Immunisation Research & Surveillance, Sydney, Australia; and eDiscipline of Paediatrics and School of Public Health, University of Sydney, Sydney, Australia.
authors.forename: James G
authors.initials: JG
authors.lastname: Wood
authors.forename: Nele
authors.initials: N
authors.lastname: Goeyvaerts
authors.forename: C Raina
authors.initials: CR
authors.lastname: MacIntyre
authors.forename: Robert I
authors.initials: RI
authors.lastname: Menzies
authors.forename: Peter B
authors.initials: PB
authors.lastname: McIntyre
authors.forename: Niel
authors.initials: N
authors.lastname: Hens
mesh_terms: D000293:Adolescent; D000914:Antibodies, Viral / Q000276:immunology; D001315:Australia / Q000453:epidemiology; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D004797:Enzyme-Linked Immunosorbent Assay; D006801:Humans; D056724:Immunity, Humoral / Q000276:immunology; D008457:Measles / Q000276:immunology; D022542:Measles-Mumps-Rubella Vaccine / Q000276:immunology*; D009107:Mumps / Q000276:immunology; D011159:Population Surveillance; D012409:Rubella / Q000276:immunology
chemical_list: D000914:Antibodies, Viral; D022542:Measles-Mumps-Rubella Vaccine