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System Dynamics from Individual Interactions: A process algebra approach to epidemiology

个体相互作用的系统动力学：流行病学的过程代数方法

基本信息

批准号：
EP/E006280/1
负责人：
Carron Shankland
金额：
$ 43.77万
依托单位：
University of Stirling
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2007
资助国家：
英国
起止时间：
2007 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FE006280%2F1
关键词：
System Dynamics Individual Interactions process

项目摘要

Disease can be viewed as a threat or as a tool. Modern society has become vulnerable to wide spreading epidemics, but we also use diseases to control pests in crops as a way of avoiding the use of chemicals. Clearly it is important to be able to understand the way the epidemic works: How much of the population will be infected? Does the behaviour of individuals change the spread of the disease? How long will it take before the disease dies out? What is the most effective way to control the disease?Testing experimentally is not an option: there are ethical problems with infecting people with diseases just to see what happens, therefore we use mathematical models. These help us predict the shape of epidemics and to evaluate methods of control. In this project theoretical computer science techniques known as process algebras will be used to model diseases. The unique benefits of this approach are threefold. Firstly, it is possible to describe the behaviour of individuals directly. Secondly, those individuals can be rigorously combined to give the behaviour of the system as a whole. Thirdly, the system can be formally investigated to establish features of the system dynamics, allowing us to answer the sort of questions posed above. This approach, known as individual-based, is particularly important because in reality we can measure facts about individuals, but our questions about epidemics all come from the population level. The ability to move rigorously between different levels of abstraction (individual to population) when describing disease spread gives us completely new ways of thinking about epidemiology.Our group is the foremost in the world in this work, but we are at the start of a long term research programme. Having built up domain expertise and techniques and tools for describing and investigating simple disease systems in previous work, we are now in a position to consider more complex epidemiological phenomena, the particular modelling features required for these, and further methods of investigation.In this project we will build and investigate process algebra models of specific biological features associated with epidemiology. These are: fluctuating populations (Adding births and deaths), interaction and transmission (If I sneeze on you, will you get my flu? What about the others in the room?), control (How many of the population need to be vaccinated to protect the whole population from the disease?), and contest between individuals (If I don't have enough food will that make me more susceptible to disease?). These features have been chosen as core to the representation of population and epidemiological models and together give a more realistic and rounded model of disease.Exploration of more complex biological systems will require more complex models. Process algebra is expressive enough to describe these systems; however, such descriptions may be clumsy and hard to understand. We will develop new language constructs to allow population models to be more simply expressed, yielding more easily constructed and understood models. Once the model is constructed we have a range of formal techniques to investigate its behaviour, and to compare with other existing models in the literature. We will develop those investigative techniques further, based on the needs of epidemiological systems.Finally, although we will concentrate on epidemiology, the features and techniques developed will be applicable to other areas of biology, and to computer science. For example, instead of viewing an individual as a person or an animal, we could view an individual as a single cell or a complex molecule. In the computer science arena, we can use epidemiological models to think about performance modelling, and also malware (computer viruses, worms etc). This general applicability makes our work particularly exciting.

疾病可以被视为一种威胁，也可以被视为一种工具。现代社会已经变得容易受到广泛传播的流行病的影响，但我们也使用疾病来控制作物中的害虫，以避免使用化学品。显然，能够理解这一流行病的运作方式是很重要的：有多少人口将被感染？个人的行为会改变疾病的传播吗？这种病要多久才会消失？控制这种疾病最有效的方法是什么？实验测试不是一种选择：让人们感染疾病只是为了看看会发生什么，这存在伦理问题，因此我们使用数学模型。这些帮助我们预测流行病的形式和评估控制方法。在这个项目中，被称为过程代数的理论计算机科学技术将被用来模拟疾病。这种方法的独特好处有三方面。首先，可以直接描述个人的行为。第二，这些个体可以被严格地组合起来，以给出整个系统的行为。第三，可以对系统进行形式上的研究，以建立系统动力学的特征，从而使我们能够回答上面提出的这类问题。这种方法被称为基于个体的方法，特别重要，因为在现实中我们可以衡量关于个体的事实，但我们关于流行病的问题都来自于人口水平。在描述疾病传播时，能够在不同的抽象层次（从个体到人群）之间严格移动，这给了我们全新的流行病学思维方式。我们的团队在这项工作中处于世界领先地位，但我们正处于长期研究计划的开始阶段。在以前的工作中，我们已经建立了描述和研究简单疾病系统的领域专业知识和技术和工具，现在我们可以考虑更复杂的流行病学现象，这些现象所需的特定建模功能，以及进一步的调查方法。这些是：波动的人口（增加出生和死亡），相互作用和传播（如果我对你打喷嚏，你会得到我的流感？）房间里的其他人呢？）控制（有多少人口需要接种疫苗，以保护整个人口免受疾病？），以及个体之间的竞争（如果我没有足够的食物，会不会让我更容易生病？）。这些特征已被选为人口和流行病学模型的核心表示，并共同给出了一个更现实和全面的疾病模型。探索更复杂的生物系统将需要更复杂的模型。进程代数足以描述这些系统;然而，这样的描述可能是笨拙的，难以理解的。我们将开发新的语言结构，使人口模型更简单地表达，产生更容易构建和理解的模型。一旦模型构建，我们有一系列的正式技术来调查其行为，并与其他现有的模型在文献中进行比较。我们将根据流行病学系统的需要进一步发展这些调查技术。最后，虽然我们将集中在流行病学上，但所开发的功能和技术将适用于生物学的其他领域和计算机科学。例如，我们可以将个体视为单个细胞或复杂分子，而不是将个体视为人或动物。在计算机科学竞技场中，我们可以使用流行病学模型来考虑性能建模，以及恶意软件（计算机病毒，蠕虫等）。这种普遍适用性使我们的工作特别令人兴奋。