# Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $491,641

## Abstract

Abstract
Following smallpox, several other infectious diseases including measles, polio and rubella have been targeted
for eradication. However, elimination of these latter infections has proven challenging. While mass vaccination
has halted endemic measles transmission in most of the Americas, with similar high vaccination rates, measles
continues to cause large epidemics in some parts of the world. Further, these epidemics can spread to other
regions due to high global connectivity and reduced local vaccine coverage (e.g. the recent increases in measles
cases in the US). These observations indicate that current understanding of disease persistence in complex
population systems remains incomplete and must be improved to effect eradication of infections such as
measles. To improve this understanding of disease persistence, the proposed work will develop model-Bayesian
inference systems using mathematical modeling and statistical methods to identify the spatial, temporal, and
demographical factors contributing to the persistent transmission of measles in the mass vaccination era. A
range of hypothesized transmission mechanisms will be tested, including changes in i) vaccination rate, ii)
demographics (e.g. birthrates and age structures), iii) contact pattern, iv) spatial connectivity and migration,
and/or v) strength of maternal immunity. Further, the project will test potential intervention measures based on
the identified transmission mechanisms as well as generate predictions of future measles epidemic dynamics to
inform measles elimination efforts. By leveraging detailed measles surveillance data, infectious disease
modeling, and Bayesian inference methods, the proposed work will yield new understanding of measles
transmission dynamics in modern populations and provide model-guided intervention strategies. Project findings
may also inform control strategies for other infections targeted for eradication (e.g. rubella). In addition, the
model-inference systems developed here can be adapted to study a broad range of (re)emerging infectious
diseases.

## Key facts

- **NIH application ID:** 9960433
- **Project number:** 5R01AI145883-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Wan Yang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $491,641
- **Award type:** 5
- **Project period:** 2019-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9960433

## Citation

> US National Institutes of Health, RePORTER application 9960433, Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination (5R01AI145883-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9960433. Licensed CC0.

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