Statistical inference and planning for complex infectious disease systems

传染病在整个社会都非常重要。大流行性H1N1流感、埃博拉、SARS或口蹄疫等疾病的爆发对公众健康构成直接威胁和/或产生严重的经济影响。这些疾病甚至可以被用作武器，作为生物或农业恐怖主义行为引入。*为了控制这种疾病，至关重要的是了解疾病是如何随着时间的推移传播的，以及是什么因素导致一些人受到感染，另一些人没有受到感染。为此，近年来发展了一系列基于个人或个人水平的传染病传播数学模型。这些模型直观、灵活，并已被证明能够准确地描述先前观察到的流行病在空间和时间上的模式。最重要的是，它们允许将有关种群中个体的信息，如空间位置、他们在哪里工作、他们的遗传信息等包括在模型中。这与经典的传染病传播模型形成了鲜明对比，后者假设人口中的每个人都是一样的，每个人都平等地与其他所有人接触。*统计推断是一个过程，用于推导和评估根据观察数据得出的模型。将从现实世界收集的数据纳入我们的模型是有利的，因为它产生的模型更能反映现实。因此，我们可以对从我们的模型得出的结论有更好的信心。*计算密集型技术被用于执行这一推理过程。然而，当应用于这些复杂的个体水平模型时，这些密集的技术可能会花费计算机很长时间来执行，特别是如果我们有大量数据、大量缺失数据或我们希望解释的数据中的不准确；所有这些都是典型的传染病数据集。如果很快就需要结果，这可能是一个大问题。例如，我们可能希望决定在疾病爆发期间如何最好地控制疾病，因此要求尽快进行数据分析。*因此，这项研究的目的是做以下工作：1)开发这些个体水平的模型，以便它们能够更准确和可靠地模拟现实生活中发生的疾病传播(例如，通过使用收集的关于病毒或细菌本身的遗传信息)；2)改进计算密集的统计推断过程，使其更有效；3)利用这些模型帮助设计动物疾病传播实验，其目的是在受控环境中收集信息数据，以帮助了解疾病传播以及如何治疗和控制疾病。*这些发展然后可以用来加深我们对传染病的理解，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。**

Infectious diseases are of great importance across society. Outbreaks of diseases such as pandemic H1N1 influenza, Ebola, SARS, or foot-and-mouth disease pose direct threats to public health and/or have serious economic effects. There diseases can even by used as weapons, introduced as acts of bio- or agro-terrorism.****In order to control such diseases, it is vital to understand how the disease spreads over time and what factors lead to some individuals becoming infected and some not. To this end, a series of mathematical 'individual-based' or 'individual-level' models for infectious disease transmission has been developed in recent years. Such models are intuitive, flexible, and have been shown to accurately describe the patterns of previously observed epidemics over space and time. Of key importance is the fact that they allow information about the individuals in the population, such as spatial location, where they work, their genetic information, etc., to be included in the model. This is in contrast to classic infectious disease transmission models that assume that everybody in the population is the same, and everybody in the population comes into contact with everybody else equally often.***Statistical inference is a process used to derive and assess models that are informed by observed data. It is advantageous to incorporate data collected from the real world into our models, as it produces models that better reflect reality. Thus, we can have better confidence in conclusions drawn from our models.***Computationally intensive techniques are used to carry out this inference process. However, when applied to these complex individual-level models, these intensive techniques can take the computer a long time to carry out, especially if we have a lot of data, lots of missing data, or inaccuracies in the data that we wish to account for; all are typically the case with infectious disease data sets. This can be a major problem if results are needed very quickly. For example, we may wish to decide how best to control a disease during the course of an outbreak, and so require data analysis to be carried out as quickly as possible.***The purpose of this research is therefore to do the following: 1) develop these individual-level models so they can more accurately and reliably model disease transmission as it occurs in real life (e.g., by using genetic information collected on the virus or bacteria itself); 2) improve the computationally intensive statistical inference process to make it more efficient; 3) use these models to help design animal disease transmission experiments, the purpose of which is to collect informative data in a controlled environment to aid understanding disease transmission and how it might be treated and controlled.***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.**