Statistical inference with mechanistic models on heterogeneous data: improving the control of infectious diseases

流感疫情每年都会发生，导致社区出现大量疾病，许多人过早死亡，卫生服务受到严重干扰，造成重大经济损失。这是在广泛接种疫苗的情况下发生的。虽然疫苗包含三种毒株，但不可能在疫情之前说出哪种毒株(如果有的话)会传播，以及由此产生的疫情会有多严重。这意味着公共卫生当局、医生和医院很难进行规划，导致效率严重低下(例如不必要地取消选择性手术等)。流感疫情的大小除其他因素外，还取决于人群中的免疫水平(正是由于这个原因，人们如此害怕大流行，因为新病毒往往与现有毒株非常不同，因此人群中的免疫水平很低)。2009年出现的一种新型H1N1(猪流感)病毒一直受到密切监测和研究。英国拥有世界上最好的流感监测系统之一，并积累了大量关于这种病毒的传播、严重性和人口免疫力的数据。尽管如此，这种病毒还是让公共卫生官员和数学建模人员感到惊讶，因为在2010/11年冬季观察到了一场重大疫情，尽管人口免疫力显然很高。同样，未来几年可能会发生什么也是未知的。这种病毒的出现和丰富的现有数据为更好地了解新的流感病毒在进入人类人口后的动态提供了一个独特的机会。我们打算开发和测试一些不同的数学模型，以更好地了解流感在人群中的动态和演变。这些模型将使用最先进的统计技术来适应流行病学数据的范围，这将在传染病动力学和统计推断领域具有普遍适用性。统计框架将阐明人口中针对随后漂移的变异的有效保护水平，并为下一代预测工具铺平道路。这些调查对提高疫苗接种等公共卫生措施的有效性至关重要，并确定应该优先考虑哪些数据，以帮助建立季节性和大流行流感的预测模型。这个多学科项目涉及许多不同的利益相关者，包括收集数据的机构、疾病传播和宿主-病原体相互作用方面的专家、将生物机制形式化的数学建模人员、开发严格而稳健的方法来对抗模型与数据的统计学家，以及提出模型必须解决的问题的公共卫生专家。据设想，该项目将有助于改善这一备受瞩目的领域的公共卫生政策，开发新的模型与数据匹配的方法，并为主要申请者提供一个理想的培训场所，使其成为数学流行病学的既定领导者。

Influenza epidemics occur every year, resulting in large amounts of illness in the community, many early deaths, major disruption to the health services, and significant economic losses. This is despite widespread vaccination. Although the vaccine contains three strains, it is not possible to say ahead of the epidemic which (if any) of the strains will circulate, and how severe will the resultant epidemic be. This means that planning by public health authorities, physicians, and hospitals is difficult, resulting in significant inefficiencies (such as the unnecessary cancelling elective surgeries, etc). The size of an influenza epidemic is governed, amongst other things, by the level of immunity in the population (it is for this reason that pandemics are so feared, as the novel virus tends to be very different from existing strains, and so the level of immunity in the population is low). The emergence of a novel H1N1 (swine flu) virus in 2009 has been closely monitored and studied. The UK has one of the best influenza surveillance systems in the world, and has amassed a great deal of data on the spread, severity, and population immunity to this virus. Despite this, the virus has surprised public health officials and mathematical modellers alike, as a significant epidemic was observed during the winter of 2010/11 despite apparently high levels of population immunity. What may happen in the coming years is equally unknown. The emergence of this virus and the wealth of data available provide an unique opportunity to better understand the dynamics of a new influenza virus following its introduction into the human population. We intend to develop and test a number of different mathematical models to build a better picture of the dynamics and evolution of influenza in the population. The models will be fitted to the range of epidemiological data using state-of-the art statistical techniques, which will have general applicability within the fields of infectious disease dynamics and statistical inference. The statistical framework will shed light on the effective level of protection in the population against subsequent drifted variants, and pave the way for the next generation of predictive tools. These investigation are critical to improve the effectiveness of public health measures, like vaccination, and determine which data should be prioritised to help make predictive models of seasonal and pandemic influenza.This multi-disciplinary project involves many different stakeholders, including the bodies that are collecting the data, experts in disease transmission and host-pathogen interactions, mathematical modellers who formalize biological mechanisms, statisticians who develop rigorous and robust methods to confront models to data, and finally, public health experts who ask the questions that the model must address. It is envisaged that the project will help improve public health policy in this high-profile area, develop new methods for fitting models to data, and provide an ideal training ground for the lead applicant to become an established leader in mathematical epidemiology.