Related Experiment Video
Updated: Mar 19, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Evolution and Use of Dynamic Transmission Models for Measles and Rubella Risk and Policy Analysis
Abstract:
The devastation caused by periodic measles outbreaks motivated efforts over more than a century to mathematically model measles disease and transmission. Following the identification of rubella, which similarly presents with fever and rash and causes congenital rubella syndrome (CRS) in infants born to women first infected with rubella early in pregnancy, modelers also began to characterize rubella disease and transmission. Despite the relatively large literature, no comprehensive review to date provides an overview of dynamic transmission models for measles and rubella developed to support risk and policy analysis. This systematic review of the literature identifies quantitative measles and/or rubella dynamic transmission models and characterizes key insights relevant for prospective modeling efforts. Overall, measles and rubella represent some of the relatively simplest viruses to model due to their ability to impact only humans and the apparent life-long immunity that follows survival of infection and/or protection by vaccination, although complexities arise due to maternal antibodies and heterogeneity in mixing and some models considered potential waning immunity and reinfection. This review finds significant underreporting of measles and rubella infections and widespread recognition of the importance of achieving and maintaining high population immunity to stop and prevent measles and rubella transmission. The significantly lower transmissibility of rubella compared to measles implies that all countries could eliminate rubella and CRS by using combination of measles- and rubella-containing vaccines (MRCVs) as they strive to meet regional measles elimination goals, which leads to the recommendation of changing the formulation of national measles-containing vaccines from measles only to MRCV as the standard of care.
Insights
Mathematical models for measles and rubella transmission are crucial for public health. This review highlights their importance in understanding disease dynamics and informing policy for elimination.
Area of Science:
- Epidemiology and Mathematical Modeling
- Infectious Disease Dynamics
Background:
- Measles and rubella outbreaks have historically driven the development of mathematical transmission models.
- Rubella, causing congenital rubella syndrome (CRS), also prompted modeling efforts due to similar symptoms to measles.
Purpose of the Study:
- To systematically review and synthesize dynamic transmission models for measles and rubella.
- To provide an overview of models supporting risk and policy analysis for these diseases.
Main Methods:
- Systematic literature review identifying quantitative dynamic transmission models for measles and/or rubella.
- Characterization of key insights from these models relevant for future modeling endeavors.
Main Results:
- Measles and rubella are relatively simple to model due to human-only impact and generally lifelong immunity, though complexities like maternal antibodies and waning immunity exist.
- Significant underreporting of infections is noted, alongside a consensus on high population immunity for transmission control.
- Rubella's lower transmissibility suggests potential for elimination via combination vaccines (MRCVs).
Conclusions:
- Dynamic transmission models are essential tools for analyzing measles and rubella risk and policy.
- Achieving and maintaining high population immunity is critical for preventing measles and rubella transmission.
- Transitioning from measles-only vaccines to measles- and rubella-containing vaccines (MRCVs) is recommended for global rubella and CRS elimination.
Related Concept Videos
Steps in Outbreak Investigation
Modeling with Differential Equations
Principles of Disease Surveillance
Statistical Methods for Analyzing Epidemiological Data
Mechanistic Models: Compartment Models in Individual and Population Analysis
Causality in Epidemiology

