Disease Persistence and Population Dynamics: Modeling Measles under Mass Vaccination

摘要 继天花之后，还针对其他几种传染病，包括麻疹、小儿麻痹症和风疹。 为了根除。然而，事实证明，消除这些后一种感染具有挑战性。虽然大规模接种疫苗 已经阻止了地方性麻疹在美洲大部分地区的传播，疫苗接种率与麻疹类似 继续在世界一些地区造成大规模疫情。此外，这些流行病可能会传播到其他 由于全球连通性高和当地疫苗覆盖率降低而导致的区域(例如最近麻疹的增加 美国的病例)。这些观察表明，目前对疾病持续性的理解复杂 人口系统仍然不完善，必须加以改进，以根除感染，如 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、时间和 在大规模接种疫苗时代，导致麻疹持续传播的人口因素。一个 将测试假设的传播机制范围，包括i)疫苗接种率、ii) 人口统计(如出生率和年龄结构)，三)接触模式，四)空间连通性和迁徙， 和/或v)母体免疫力强。此外，该项目将测试潜在的干预措施，其基础是 已确定的传播机制以及对未来麻疹流行动态的预测 通知消除麻疹的工作。通过利用详细的麻疹监测数据，传染病 建模和贝叶斯推理方法，拟议的工作将产生对麻疹的新理解 在现代人群中的传播动态，并提供模型引导的干预策略。项目成果 还可为其他旨在根除的感染(如风疹)的控制战略提供信息。此外， 这里开发的模型推理系统可以适用于研究广泛的(重新)出现的传染性疾病 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.