The spatio-temporal analysis of infectious diseases

传染病时空分析

• 批准号: 340888-2007
• 负责人: Deardon, Robert
• 金额: $ 0.87万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=362669
• 关键词: spatio; temporal; analysis; infectious; diseases

Infectious diseases are of great importance across society. Epidemic outbreaks of diseases such as measles, SARS, influenza or foot-and-mouth disease, can pose direct threats to public health and/or have serious economic effects. In order to control such diseases, it is vital to understand how the disease spreads geographically over time, and what factors lead to some individuals becoming infected, and some not. To this end, a new class of mathematical models,  individual-level models (ILMs) for infectious diseases, has been developed. These models are intuitive, flexible, and have been shown to accurately describe the patterns of previously observed epidemics over space and time. However, ILMs are relatively new and so there are many infectious disease problems for which specific models do not yet exist.    A process known as statistical inference is used to derive a model that best fits the observed data. Computationally intensive techniques are used to carry out this process. However, these intensive techniques can take a long time to run if we have a lot of data, or lots of missing values in the data which we wish to estimate; both are often the case with disease epidemics.    The purpose of this research is therefore to do the following: 1) extend ILMs so they can model more infectious diseases, more accurately; 2) improve the statistical inference process to make it more efficient; 3) apply new models to data collected about various infectious diseases to answer biologically interesting questions about those diseases (e.g. "who are most at risk from the disease?" or "how do we best control this disease?"); 4) 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; and 5) develop computer software that non-statisticians can use easily.    These developments can then be used to further our understanding of infectious diseases, and through this, our ability to control them or alleviate unnecessarily severe outcomes.

传染病对整个社会都很重要。麻疹、SARS、流感或口蹄疫等疾病的流行性爆发可对公共卫生构成直接威胁和/或产生严重的经济影响。为了控制这些疾病，必须了解疾病如何随着时间的推移在地理上传播，以及哪些因素导致一些人感染，而另一些人则没有感染。为此，一类新的数学模型，传染病的个体水平模型（ILM），已经开发出来。这些模型直观，灵活，并已被证明可以准确地描述先前观察到的流行病在空间和时间上的模式。然而，ILM是相对较新的，因此有许多传染病的问题，具体的模型还不存在。 一个被称为统计推断的过程被用来推导出一个最适合观测数据的模型。计算密集型技术用于执行此过程。然而，如果我们有很多数据，或者我们希望估计的数据中有很多缺失值，这些密集型技术可能需要很长时间才能运行;这两种情况通常都是疾病流行的情况。 因此，本研究的目的是：1）扩展ILM，使其能够更准确地模拟更多的传染病; 2）改进统计推断过程，使其更有效; 3）将新模型应用于收集的有关各种传染病的数据，以回答有关这些疾病的生物学有趣的问题（例如，“谁最有可能患上这种疾病？“或“我们如何最好地控制这种疾病？");4）利用计算机模拟和推理，以确定未来应收集哪种传染病数据，以最有效地提供有关感染过程的信息; 5）开发非统计人员可以轻松使用的计算机软件。 这些发展可以用来进一步了解传染病，并通过这一点，我们控制它们或减轻不必要的严重后果的能力。