Deterministic Models for Waterborne Infections

该项目将对水传播感染进行数学建模、分析和模拟，尽管在预防和干预计划以及大量理论、实验和临床研究方面做出了巨大努力，但水传播感染仍然是一个重大的公共卫生负担。与水传播感染相关的一个特殊挑战是准确预测疫情发展，并有效管理人口和异质性很强的环境中的疾病暴发。该项目将开发创新的数学模型，以深入研究复杂的、不同种类的水传播疾病的动态。项目成果不仅将建立一个坚实的知识基础，以更好地了解水媒感染的传播和传播，而且还将为疾病管理和政策制定提供有用的公共卫生指南。研究生和本科生参与了这项资助的研究。该项目将建立一个基于确定性微分方程式的水传播感染的新数学框架。将引入一种新的、自适应的集群建模方法，该方法将整合和扩展经典的拉格朗日和欧拉方法。这种集群建模技术将优化疾病动力学量化中同质性和异质性之间的平衡，以及疾病暴发管理中个人需求和可用资源之间的平衡。利用这项技术，该项目将在广泛的空间和时间尺度上对水传播感染的动态进行准确和有效的研究。该项目还将使用现实的流行病和人口数据，特别是与霍乱暴发有关的数据，以验证模型预测并为模型改进提供信息。该项目的成功完成将在水传播感染和相关疾病控制策略的定量研究方面带来潜在的变革性进展。该奖项由MPS数学科学部(DMS)通过数学生物学计划和BIO/IOS通过行为系统集群联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will conduct mathematical modeling, analysis and simulation for waterborne infections, which remain a significant public health burden despite tremendous efforts on prevention and intervention programs and a large body of theoretical, experimental, and clinical studies. A particular challenge associated with waterborne infections is to accurately predict the epidemic development and effectively manage the disease outbreaks in populations and environments with strong heterogeneity. The project will develop innovative mathematical models for a deep investigation into the complex, heterogeneous dynamics of waterborne diseases. The project outcome will not only build a solid knowledge base to better understand the transmission and spread of waterborne infections, but also provide useful public health guidelines for disease management and policy-making. Graduate and undergraduate students are involved in the funded research.The project will establish a new mathematical framework for waterborne infections based on deterministic differential equations. A novel, adaptive cluster modeling approach will be introduced that will integrate and extend the classical Lagrangian and Eulerian approaches. This cluster modeling technique will optimize the balance between the homogeneity and heterogeneity in the quantification of disease dynamics as well as the balance between the individual needs and available resources in the management of disease outbreaks. Using this technique, the project will perform an accurate and efficient study on the dynamics of waterborne infections across a wide range of spatial and temporal scales. The project will also employ realistic epidemic and demographic data, particularly those related to cholera outbreaks, to validate model predictions and inform model refinement. Successful completion of this project will lead to a potentially transformative progress in the quantitative study of waterborne infections and related disease control strategies.This award is being funded jointly by the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program and BIO/IOS through the Behavioral Systems Cluster.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.