national pediatric immunization schedule in 1979 because of concerns over the reactogenicity of the whole-cell vaccine (Gangarosa et al., 1998; Romanus et al., 1987). After a 17-year hiatus, the acellular pertussis vaccine was added to the immunization schedule in 1996 (Carlsson and Trollfors, 2009). Analyses of age-stratified incidence reports highlighted both a sharp decline in the incidence and a marked increase in the age distribution of pertussis cases as a result of the resumption of immunization against pertussis (Rohani et al., 2010). Importantly, Swedish data also illustrate the concept of community immunity.
A pattern similar to that seen in Sweden has been observed in England and Wales, where declines in the uptake of MMR after controversy instigated by a subsequently retracted paper questioning the vaccine’s safety were associated with a rise in measles notifications and a shift in the incidence of measles toward younger age groups (Jansen et al., 2003).
Predicting Changes to Community Immunity
As outlined in the commissioned paper (see Appendix D), a variety of designs may be used to compare the safety of alternative schedules. It is, unfortunately, difficult predict the long-term population-level consequences of disease transmission as a result of changes to the immunization schedule. It is possible, however, to use mathematical and computational models to predict the impacts of changes in the administration of any one specific vaccine on the incidence of the infectious disease affected by that vaccine. This process involves three distinct steps: model formulation, parameterization, and model validation. These and other elements of the models are described below.
The development of a disease-specific transmission model begins with determination of the model structure and key processes, which are informed by the known immunology and epidemiology of the system. For instance, a loss of immunity may be a necessary ingredient for a model of pertussis transmission, whereas a latent carrier stage may be appropriate for varicella (Anderson and May, 1992; Keeling and Rohani, 2008). The model also needs to explicitly consider age-dependent heterogeneities in contact rates, susceptibility to complications, and reporting.
A number of age-specific models have been proposed for many of the key childhood infections, including measles (Anderson and May, 1992; Schenzle, 1984), pertussis (Hethcote, 1998; Rohani et al., 2010), Streptococcus pneumoniae infection (Cobey and Lipsitch, 2012), rubella (Metcalf et al., 2011), and chickenpox (Ferguson et al., 1996).