Dynamics and control of infectious disease epidemics: scaling from within-host to population-level models

传染病流行的动态和控制：从宿主内部模型扩展到群体水平模型

• 批准号: 2099853
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2099853
• 关键词: Dynamics; control; infectious; disease; epidemics

The threat posed by infectious disease epidemics, as evidenced by the 2014-2016 Ebola outbreak in West Africa, is a huge ongoing concern. Mathematical models are increasingly being used to predict the progression of epidemics and to plan interventions. Commonly used epidemiological models typically assume each infectious host is equally infectious. In reality, both infectiousness and symptom expression will vary over the course of an infection.In this project, we will develop methods for nesting within-host pathogen dynamics inside population-level models. We will investigate whether including within-host dynamics in models affects predictions as to the effectiveness of control interventions. Differences between the predictions of different models are likely to be of interest to policy-makers, who seek accurate forecasts.This project will be split into four parts, which are summarised below.1. Linking different approaches for including variable infectiousness in epidemic models. Time-dependent infectiousness may be incorporated into an ordinary differential equation (ODE) compartmental model by including multiple infectious compartments, through which a host progresses and between which their infectiousness varies. An alternative approach is to use an integro-differential equation (IDE) model. We aim to link rigorously these two approaches for the first time, by considering the limit in which the number of infectious compartments in the ODE model tends to infinity.2. The effect of variability in symptom expression on surveillance. Infectious disease surveillance is usually modelled as an observation process. In other words, some proportion of infectious hosts are assumed to be detected, with the detection probability of each host assumed to be the same. In reality, however, the detection probability changes over the course of an infection. We will use the ODE model described in 1 above to investigate the effect of this variability on model predictions of observed epidemic dynamics. We will also develop new models in which surveillance is modelled as a dynamic process, involving not only detection but also control of infectious hosts.3. Linking stochastic and deterministic models. Whilst deterministic epidemic models are fast to numerically solve, real-world epidemiological systems are inherently stochastic. Deterministic equations, which describe not only the mean behaviour over many epidemics but also the variability, may be derived from a stochastic model through the use of moment closure techniques. We aim to develop novel moment closure techniques and apply them to stochastic epidemiological models incorporating variable infectiousness. In this way, we can see how our models developed in 1 and 2 above compare to those that include stochasticity.4. Application to control of Ebola outbreaks. The level of infectiousness and symptom expression both vary significantly during the course of an Ebola infection. Previously, the simple SEIR compartmental model has been used to model Ebola spread and control. However, variations in infectiousness and symptoms within a host will affect, for example, when an infection is likely to be detected and how many secondary infections are likely to be generated before this time. We will perform a literature search to find data describing temporal variations in both infectiousness and symptom expression during an Ebola infection, and use this data to parametrise the ODE model described in 1. This model may then be used by policy-makers to test different proposed control interventions.This project falls within the EPSRC Mathematical Biology research area, within the Mathematical Sciences and Healthcare Technologies themes.Possible collaborators1. Dr Kit Yates, University of Bath2. Dr Oliver Morgan, World Health Organization3. Jonathan Polonsky, World Health Organization

2014-2016年西非埃博拉疫情证明，传染病流行构成的威胁是一个持续的巨大问题。数学模型正越来越多地用于预测流行病的发展和规划干预措施。常用的流行病学模型通常假设每个感染性宿主具有相同的传染性。在这个项目中，我们将开发在种群水平模型中嵌套宿主内病原体动态的方法。我们将调查是否包括在模型中的主机动态影响预测的控制干预措施的有效性。不同模型的预测之间的差异可能会引起寻求准确预测的政策制定者的兴趣。本项目将分为四个部分，总结如下。在流行病模型中包括可变传染性的不同方法的连接。时间依赖性传染性可以通过包括多个传染性隔室而并入常微分方程（ODE）隔室模型中，宿主通过所述多个传染性隔室进展并且它们的传染性在所述多个传染性隔室之间变化。另一种方法是使用积分微分方程（IDE）模型。我们的目标是通过考虑ODE模型中传染区室数量趋于无穷大的极限，首次将这两种方法严格联系起来。症状表达的变异性对监测的影响。传染病监测通常被模拟为一个观察过程。换句话说，假设一定比例的感染性宿主被检测到，并且假设每个宿主的检测概率相同。然而，实际上，检测概率在感染过程中会发生变化。我们将使用上面1中描述的ODE模型来研究这种可变性对观察到的流行病动态的模型预测的影响。我们还将开发新的模式，其中监测被建模为一个动态过程，不仅涉及检测，而且还涉及控制传染性宿主。连接随机和确定性模型。虽然确定性流行病模型可以快速数值求解，但现实世界的流行病系统本质上是随机的。确定性方程，它不仅描述了许多流行病的平均行为，但也可变性，可以从一个随机模型，通过使用矩封闭技术。我们的目标是开发新的矩封闭技术，并将其应用到随机流行病学模型，将变量传染性。通过这种方式，我们可以看到我们在上面的1和2中开发的模型与包括随机性的模型相比如何。应用于控制埃博拉疫情。在埃博拉病毒感染过程中，传染性和症状表现的水平都有很大变化。此前，简单的SEIR房室模型已被用于模拟埃博拉病毒的传播和控制。然而，宿主内感染性和症状的变化将影响例如何时可能检测到感染以及在此之前可能产生多少继发感染。我们将进行文献检索，以找到描述埃博拉感染期间传染性和症状表达的时间变化的数据，并使用这些数据来参数化1中描述的ODE模型。这个模型可以被政策制定者用来测试不同的控制干预措施。这个项目福尔斯EPSRC数学生物学研究领域，属于数学科学和医疗保健技术主题。Kit Yates博士，2002年大学。奥利弗摩根博士，世界卫生组织3。Jonathan Polonsky，世界卫生组织