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，以整合感染和易感人群之间的复杂，改变联系，以对疾病暴发的大小和范围做出简单的预测。接触时传播的疾病动力学动力学的一个重要特征是，接触模式会随着症状的感染和进展而发生变化：受感染的人由于疾病而削弱了与正常社区的接触（例如，太病了无法上学或工作），但会增加与其他人群的接触，例如医疗保健工作者或家庭中的看护人。西非最近发生的埃博拉病毒爆发提供了一个鲜明的例子，但是还有许多其他埃博拉病毒爆发，例如，在世界各地的艾滋病毒艾滋病浪潮中以及刚果偏远地区的新埃博拉病毒爆发中。尽管关于不断发展的接触网络结构和感染状况如何影响疾病暴发的问题似乎是基本的，但理论治疗非常有限。该项目的主要目的是通过开发数学结果和工具来帮助解决这一知识差距，以简单地近似这些复杂的动态。这项研究将引起学术界内外的许多个人和组织的关注，包括疾病控制中心，生物医学高级研发局以及刚果公共卫生部民主共和国。将通过年度访问俄亥俄州立大学的年度访问和正在进行的国际合作，为金沙萨公共卫生学院的博士生提供培训；美国研究生还将接受跨学科研究的培训。本项目将开发一个近似近似的马尔可夫计算（AMC）框架，用于近似于不断发展的大型，多层网络的复杂随机sir型疾病模型，其中每个层都对应于相互作用类型（也对应于一种相互作用类型（也称为多重或多元重点网络）。 AMC的想法是，对于大型随机配置网络，尽管平均有一些网络功能（例如，触点数量或下降/激活率），但通常可以构建Markov跳跃过程，以跟踪原始网络上的爆发动态。如果两个过程共享相同的平均场限制方程，它们也共享相同的限制参数。因此，马尔可夫过程也可以用于一致的参数估计。一方面，AMC与贝叶斯推论中流行的ABC方法具有一些概念上的相似之处，尽管与ABC不同，它明确近似于SIR感兴趣过程的可能性。另一方面，AMC也可以看作是随机复合系统的准平衡近似值的概括。除了开发相关的理论结果外，研究人员还计划开发软件，将AMC应用于非洲和其他地方的联系流行病的真实现场数据。希望在该项目中开发的方法将改善传染病的预测建模，并为关键接触流行病特征（例如入侵概率，持久性和爆发规模）提供基本的见解。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估来评估的支持。