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Bridging Statistical Inference and Mechanistic Network Models for HIV/AIDS

连接艾滋病毒/艾滋病的统计推断和机制网络模型

基本信息

批准号：
10651874
负责人：
Jukka-Pekka Onnela
金额：
$ 42.1万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-02 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10651874
关键词：
AIDS prevention Adoption Automobile Driving Bayesian Analysis Behavior Behavior Therapy Behavioral Biological Calibration Cluster randomized trial Communicable Diseases Communities Computer Models Computer software Data Development Dimensions Disease Endowment Epidemic Ethics Evaluation Evolution Family Foundations Goals HIV HIV/AIDS Health Sciences Human Individual Infection Intervention Learning Likelihood Functions Machine Learning Maps Mathematics Methodology Methods Modeling Parameter Estimation Pattern Physics Population Prevention Measures Prevention strategy Probability Process Property Public Health Pythons Research Research Personnel SET Domain Science Specific qualifier value Statistical Methods Statistical Models Structure Time Uncertainty base effective intervention epidemic response high dimensionality implementation intervention indexing innovation insight interest member network models novel therapeutic intervention open source pandemic disease pathogen pre-exposure prophylaxis simulation statistics stem tool treatment adherence treatment strategy

项目摘要

Network models are used to investigate the spread of HIV/AIDS, but rather than assuming that the members of a population of interest are fully mixed, the network approach enables individual-level specification of contact patterns by considering the structure of connections among the members of the population. By representing individuals as nodes and contacts between pairs of individuals as edges, this network depiction enables identification of individuals who drive the epidemic, allows for accurate assessment of study power in cluster- randomized trials, and makes it possible to evaluate the impact of interventions on the individuals themselves, their partners, and the broader network. There are currently two major mathematical paradigms to the modeling of networks: the statistical approach and the mechanistic approach. In the statistical approach, one specifies a model that states the likelihood of observing a given network, whereas in the mechanistic approach one specifies a set of domain-specific mechanistic rules at the level of individual nodes, the actors in the network, that are used to evolve the network over time. Given that mechanistic models directly model individual-level behaviors – modification of which is the foundation of most prevention measures – they are a natural fit for infectious diseases. Another attractive feature of mechanistic models is their scalability as they can be implemented for networks consisting of thousands or even millions of nodes, making it possible to simulate population-wide implementation of interventions. Lack of statistical methods for calibrating these models to empirical data has however impeded their use in real-world settings, a limitation that stems from the fact that there are typically no closed-form likelihood functions available for these models due the exponential increase in the number of ways, as a function of network size, of arriving at a given observed network. We propose to overcome this gap by advancing inferential and model selection methods for mechanistic network models, and by developing a framework for investigating their similarities with statistical network models. We base our approach on approximate Bayesian computation (ABC), a family of methods developed specifically for settings where likelihood functions are intractable or unavailable. Our specific aims are the following. Aim 1: To develop a statistically principled framework for estimating parameter values and their uncertainty for mechanistic network models. Aim 2: To develop a statistically principled method for model choice between two competing mechanistic network models and estimating the uncertainty surrounding this choice. Aim 3: To establish a framework for mapping mechanistic network models to statistical models. We also propose to implement these methods in open source software, using a combination of Python and C/C++, to facilitate their dissemination and adoption. We believe that the research proposed here can help harness mechanistic network models – and with that leverage some of the insights developed in the network science community over the past decade and more – to help eradicate this disease.

网络模型被用来研究HIV/AIDS的传播，而不是假设

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Framework for assessing and easing global COVID-19 travel restrictions.

DOI：
10.1038/s41598-022-10678-y
发表时间：
2022-04-28
期刊：
SCIENTIFIC REPORTS
影响因子：
4.6
作者：
Thien-Minh Le;Raynal, Louis;Talbot, Octavious;Hambridge, Hali;Drovandi, Christopher;Mira, Antonietta;Mengersen, Kerrie;Onnela, Jukka-Pekka
通讯作者：
Onnela, Jukka-Pekka

Examining SARS-CoV-2 Interventions in Residential Colleges Using an Empirical Network.