Modelling the long-term dynamics of pre-vaccination pertussis.
The dynamics of strongly immunizing childhood infections is still not well understood. Although reports of successful modelling of several data records can be found in the previous literature, the key determinants of the observed temporal patterns have not yet been clearly identified. In particular, different models of immunity waning and degree of protection applied to disease- and vaccine-induced immunity have been debated in the previous literature on pertussis. Here, we study the effect of disease-acquired immunity on the long-term patterns of pertussis prevalence. We compare five minimal models, all of which are stochastic, seasonally forced, well-mixed models of infection, based on susceptible-infective-recovered dynamics in a closed population. These models reflect different assumptions about the immune response of naive hosts, namely total permanent immunity, immunity waning, immunity waning together with immunity boosting, reinfection of recovered and repeat infection after partial immunity waning. The power spectra of the output prevalence time series characterize the long-term dynamics of the models. For epidemiological parameters consistent with published data, the power spectra show quantitative and even qualitative differences, which can be used to test their assumptions by comparison with ensembles of several-decades-long pre-vaccination data records. We illustrate this strategy on two publicly available historical datasets.
Departamento de Física, Centro de Física da Matéria Condensada, Faculdade de Ciências da Universidade de Lisboa, 1649-003 Lisboa Codex, Portugal. a_rozhnova@cii.fc.ul.pt
Ganna
G
Rozhnova
Ana
A
Nunes
D002648:Child; D003198:Computer Simulation; D004196:Disease Outbreaks / Q000517:prevention & control / Q000706:statistics & numerical data*; D006801:Humans; D018448:Models, Immunological; D015995:Prevalence; D012621:Seasons; D013269:Stochastic Processes; D014917:Whooping Cough / Q000453:epidemiology* / Q000276:immunology* / Q000517:prevention & control