Statistical Methods for Complex Infectious Disease Systems

复杂传染病系统的统计方法

• 批准号: RGPIN-2021-04292
• 负责人: Pokharel, Gyanendra
• 金额: $ 1.31万
• 依托单位: University of Winnipeg
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759098
• 关键词: Statistical; Methods; Complex; Infectious; Disease

Studies of infectious diseases are increasingly important in Canada and around the world. Outbreaks of diseases such as COVID-19, Ebola, SARS, or foot-and-mouth disease pose direct threats to public health and agriculture industries, and/or have serious economic effects. To quickly control such disease outbreaks, it is important to understand how the disease spreads over time and what factors lead some individuals to become infected and some not. To address these issues, several individual-level mathematical models for infectious disease transmission have been developed. These models have intuitive and flexible characteristics and have been shown to accurately describe the patterns of disease spread dynamics over time and space. The parameter estimation of such models is in general carried out in a Bayesian framework, using techniques such as Markov chain Monte Carlo (MCMC), which explores the posterior distribution. The Bayesian approach offers many advantages, such as the easy incorporation of missing data information as latent variables. However, the parameter estimation from these models in a Bayesian framework are computationally intensive, requiring substantial computer time to carry out. This is especially apparent in cases with large amounts of data, a substantial proportion of missing data, or inaccuracies in the data that we wish to account for. All of these cases typically apply with infectious disease data sets. To decide how best to control an infectious disease during the course of an outbreak, results are needed quickly and updates needed often as new data are available. The purpose of this research program is therefore to: i)develop individual-level models that more accurately and reliably model disease transmission as it occurs in real settings; ii)improve the computationally intensive statistical inference process to make it more efficient, for example using Gaussian process emulation and epidemic classification techniques; iii)apply new models to data collected about various infectious diseases to answer biologically interesting questions about those diseases; iv)use computer simulation and inference to determine what sort of infectious disease data should be collected in the future to provide information most efficiently about the infection process. This proposal emphasizes that statisticians play an important role in solving problems in the analysis of large scale infectious disease data. It will also provide opportunities for training highly qualified personnel at all levels. This training has three components: methodology, computation, and analysis of real life data. The outcomes of this research program are expected to aid stakeholders in health care services, and to aid in making proper decisions based on quick and correct statistical inferences in other areas of applications such plant and animal disease.

在加拿大和世界各地，传染病研究日益重要。COVID-19、埃博拉、SARS或口蹄疫等疾病的爆发对公共卫生和农业构成直接威胁，并/或产生严重的经济影响。为了迅速控制这种疾病的爆发，重要的是要了解疾病是如何随着时间的推移传播的，以及哪些因素导致一些人被感染，而另一些人没有被感染。为了解决这些问题，已经开发了几个传染病传播的个人层面数学模型。这些模型具有直观和灵活的特点，并已被证明可以准确地描述疾病随时间和空间的传播动态模式。这些模型的参数估计通常在贝叶斯框架中进行，使用马尔可夫链蒙特卡罗（MCMC）等技术，探索后验分布。贝叶斯方法提供了许多优点，例如很容易将缺失的数据信息合并为潜在变量。然而，在贝叶斯框架中，这些模型的参数估计计算量很大，需要大量的计算机时间来执行。这在数据量大、缺失数据的比例很大或我们希望解释的数据不准确的情况下尤其明显。所有这些案例通常适用于传染病数据集。为了决定如何在疫情爆发期间最好地控制传染病，需要迅速获得结果，并在获得新数据时经常进行更新。因此，该研究计划的目的是：i)开发个人层面的模型，更准确、更可靠地模拟真实环境中发生的疾病传播；ii)改进计算密集型统计推断过程，使其更有效，例如使用高斯过程仿真和流行病分类技术；（三）将新模型应用于收集到的关于各种传染病的数据，以回答关于这些疾病的生物学上有趣的问题；Iv)利用计算机模拟和推理来确定今后应收集何种传染病数据，以最有效地提供有关感染过程的信息。该建议强调统计学家在解决大规模传染病数据分析中的问题方面发挥重要作用。它还将提供培训各级高度合格人员的机会。这个培训有三个组成部分：方法论、计算和对现实生活数据的分析。这项研究计划的成果有望帮助医疗保健服务的利益相关者，并有助于在植物和动物疾病等其他应用领域根据快速和正确的统计推断做出适当的决定。