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Approximating Dynamics of Stochastic Contact Networks: Ebola Model

随机接触网络的近似动力学：埃博拉模型

基本信息

批准号：
1853587
负责人：
Grzegorz Rempala
金额：
$ 34.99万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2023-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1853587&HistoricalAwards=false
关键词：
Approximating Dynamics Stochastic Contact Networks

项目摘要

This project will develop mathematical and statistical tools to integrate complex, changing connections between infected and susceptible individuals to make simple predictions regarding size and scope of disease outbreaks. An important feature of the dynamics of diseases transmitted on contact is that the contact patterns change in response to infection and progression of symptoms: infected individuals curtail contacts with their regular community due to illness (e.g. being too sick to go to school or work) but increase their contacts with other segments of the population, such as healthcare workers or caretakers in the home. The recent Ebola outbreak in West Africa provides a stark example, but there have been many others, for instance in the waves of HIV-AIDS epidemics in various parts of the world as well as new, smaller outbreaks of Ebola in remote parts of the Congo. Although the question of how evolving contact network structure and infection status affect disease outbreaks seems basic, theoretical treatments have been very limited. The main objective of this project is to help address this knowledge gap by developing mathematical results and tools for simple approximation of these complicated dynamics. The research will be of interest to many individuals and organizations inside and outside of academia including the Centers for Disease Control, the Biomedical Advanced Research and Development Authority, and the Democratic Republic of the Congo Ministry of Public Health. Training will be provided for a PhD student from the Kinshasa School of Public Health via annual monthly visits to Ohio State University and ongoing international collaboration; a US graduate student will also be trained in interdisciplinary research.This project will develop a general Approximate Markovian Computation (AMC) framework for approximating complex stochastic SIR-type disease models on evolving, large, multi-layer networks in which each layer corresponds to an interaction type (also referred to as multiplex or multi-relational network). The idea of AMC is that for a large random configuration network one may often construct Markov jump process closely tracking the outbreak dynamics on the original network despite averaging out some network features (e.g. number of contacts or drop/activation rates). If both processes share the same mean field limiting equations, they also share the same set of limiting parameters; hence the Markov process may also be used for consistent parameter estimation. On the one hand, AMC bears some conceptual resemblance to the popular ABC method in Bayesian inference, although, unlike ABC, it explicitly approximates the likelihood of the SIR process of interest. On the other hand, AMC may also be viewed as a generalization of quasi-equilibrium approximations for stochastic complex systems. In addition to developing relevant theoretical results, the investigators plan to develop software to apply AMC to real field data from contact epidemics in Africa and elsewhere. It is hoped that methods developed in this project will improve predictive modeling for infectious diseases and provide essential insight into key contact epidemic features such as invasion probability, persistence, and outbreak size.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.

该项目将开发数学和统计工具，以整合感染者和易感者之间复杂、不断变化的联系，从而对疾病爆发的规模和范围做出简单的预测。通过接触传播的疾病的一个重要特征是，接触模式会随着感染和症状的进展而改变：受感染者由于疾病（例如病得太重而不能上学或工作）而减少与常规社区的接触，但增加与其他人群的接触，例如卫生保健工作者或家庭护理人员。最近在西非爆发的埃博拉疫情就是一个明显的例子，但还有许多其他例子，例如世界各地的艾滋病毒/艾滋病流行浪潮，以及刚果偏远地区新爆发的规模较小的埃博拉疫情。尽管不断演变的接触网络结构和感染状态如何影响疾病爆发的问题似乎是基本的，但理论上的治疗方法非常有限。该项目的主要目标是通过开发数学结果和工具来帮助解决这一知识差距，以便对这些复杂的动态进行简单的近似。这项研究将引起学术界内外许多个人和组织的兴趣，包括疾病控制中心、生物医学高级研究与发展管理局和刚果民主共和国公共卫生部。将通过每年每月访问俄亥俄州州立大学和持续的国际合作，为金沙萨公共卫生学院的一名博士生提供培训;一名美国研究生也将接受跨学科研究的培训。该项目将开发一个通用的近似马尔可夫计算（AMC）框架，用于在不断发展的，大型的，多层网络，其中每一层对应于交互类型（也称为多路复用或多关系网络）。AMC的思想是，对于一个大型随机配置网络，人们通常可以构建马尔可夫跳跃过程，密切跟踪原始网络上的爆发动态，尽管平均了一些网络特征（例如联系人数量或丢弃/激活率）。如果两个过程共享相同的平均场极限方程，它们也共享相同的极限参数集;因此马尔可夫过程也可以用于一致的参数估计。一方面，AMC与贝叶斯推理中流行的ABC方法在概念上有一些相似之处，尽管与ABC不同，它明确地近似了SIR过程的可能性。另一方面，AMC也可以被看作是随机复杂系统的拟平衡近似的推广。除了开发相关的理论结果，研究人员计划开发软件，将AMC应用于非洲和其他地方接触性流行病的真实的现场数据。希望该项目中开发的方法将改善传染病的预测建模，并提供对关键接触流行病特征（如入侵概率、持续性和爆发规模）的重要见解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。