Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination

摘要 继天花之后，包括麻疹、脊髓灰质炎和风疹在内的其他几种传染病也成为目标 为了根除。然而，消除这些后一种感染已被证明具有挑战性。虽然大规模疫苗接种 在美洲大部分地区，麻疹疫苗接种率同样很高， 继续在世界一些地区造成大规模流行病。此外，这些流行病可能会传播到其他地区。 由于全球连通性高和当地疫苗覆盖率下降（例如，最近麻疹发病率上升）， 美国的案例）。这些观察结果表明，目前对疾病在复杂环境中持续存在的理解， 人口系统仍然不完善，必须加以改进，以有效消除感染， 麻疹。为了提高对疾病持续性的理解，拟议的工作将开发模型-贝叶斯 推理系统使用数学建模和统计方法来识别空间，时间， 在大规模疫苗接种时代，人口因素导致麻疹持续传播。一 将测试一系列假设的传播机制，包括i）疫苗接种率，ii） 人口统计学（如出生率和年龄结构），iii）接触模式，iv）空间连通性和迁移， 和/或V）母体免疫力的强度。此外，该项目将根据以下方面测试潜在的干预措施： 确定的传播机制以及对未来麻疹流行动态的预测， 为消灭麻疹工作提供信息。通过利用详细的麻疹监测数据， 建模和贝叶斯推理方法，所提出的工作将产生新的理解麻疹 研究现代人群中的传播动态，并提供模型指导的干预战略。项目成果 还可为其他需要根除的感染（如风疹）的控制战略提供信息。此外该 这里开发的模型推理系统可以适用于研究广泛的（重新）出现的传染病 疾病

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.