Modeling the long term dynamics of pre-vaccination pertussis

TL;DR

This study compares different stochastic models of pertussis immunity to understand long-term prevalence patterns, using power spectra analysis to match model predictions with historical pre-vaccination data.

Contribution

It introduces a comparative analysis of five minimal models of pertussis immunity dynamics, highlighting how different assumptions affect long-term prevalence patterns.

Findings

Models show distinct spectral signatures that can be matched with historical data.

Immunity waning and boosting significantly influence long-term disease dynamics.

Power spectra can discriminate between different immunity assumptions in pertussis models.

Abstract

The dynamics of strongly immunizing childhood infections is still not well understood. Although reports of successful modeling of several incidence data records can be found in the literature, the key determinants of the observed temporal patterns have not 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 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 for pertussis, the power spectra show quantitative and even qualitative differences that 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 data sets.