Characterizing the dynamics of rubella relative to measles: the role of stochasticity

TL;DR

This study uses stochastic models to analyze and compare the recurrent dynamics of rubella and measles, revealing how stochasticity influences their patterns and implications for vaccination strategies.

Contribution

It provides a systematic analysis of stochastic effects on rubella and measles dynamics, highlighting differences and informing public health approaches.

Findings

Rubella exhibits diverse recurrent patterns influenced by stochasticity.

Measles and rubella dynamics differ significantly due to demographic stochasticity.

The analysis informs vaccination and birth rate strategies for disease control.

Abstract

Rubella is a completely immunizing and mild infection in children. Understanding its behavior is of considerable public health importance because of Congenital Rubella Syndrome, which results from infection with rubella during early pregnancy and may entail a variety of birth defects. The recurrent dynamics of rubella are relatively poorly resolved, and appear to show considerable diversity globally. Here, we investigate the behavior of a stochastic seasonally forced susceptible-infected-recovered model to characterize the determinants of these dynamics and illustrate patterns by comparison with measles. We perform a systematic analysis of spectra of stochastic fluctuations around stable attractors of the corresponding deterministic model and compare them with spectra from full stochastic simulations in large populations. This approach allows us to quantify the effects of demographic stochasticity and to give a coherent picture of measles and rubella dynamics, explaining essential differences in the recurrent patterns exhibited by these diseases. We discuss the implications of our findings in the context of vaccination and changing birth rates as well as the persistence of these two childhood infections.