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

利用异构数据的机制模型进行统计推断：改善传染病的控制

• 批准号: MR/J01432X/1
• 负责人: Anton Camacho
• 金额: $ 32.02万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FJ01432X%2F1
• 关键词: Statistical; inference; mechanistic; models; heterogeneous

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.

每年都会发生流感大流行，造成社区大量患病、许多人过早死亡、卫生服务严重中断以及重大经济损失。这是在广泛接种疫苗的情况下发生的。尽管该疫苗含有三种毒株，但在疫情爆发前不可能预测哪一种毒株（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）。除其他因素外，流感疫情的规模由人群的免疫水平决定（正因为如此，人们才如此担心大流行，因为新型病毒往往与现有病毒株非常不同，因此人群的免疫水平很低）。2009年出现的新型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、严重程度和人群免疫力的数据。尽管如此，该病毒还是让公共卫生官员和数学建模人员都感到惊讶，因为在2010/11年冬季，尽管人口免疫力明显很高，但仍观察到一场重大的流行病。未来几年可能发生的事情同样是未知的。这种病毒的出现和现有的丰富数据为更好地了解一种新型流感病毒进入人群后的动态提供了独特的机会。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进化。这些模型将使用最先进的统计技术来拟合流行病学数据的范围，这些统计技术将在传染病动力学和统计推断领域具有普遍适用性。该统计框架将阐明人口对随后漂移变异的有效保护水平，并为下一代预测工具铺平道路。这些调查对于提高疫苗接种等公共卫生措施的有效性和确定应优先考虑哪些数据以帮助建立季节性流感和大流行性流感的预测模型至关重要。这个多学科项目涉及许多不同的利益攸关方，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、形式化生物机制的数学建模者、开发严格而有力的方法使模型面对数据的统计学家，以及最后提出模型必须解决的问题的公共卫生专家。预计该项目将有助于改善这一引人注目领域的公共卫生政策，开发使模型与数据相适应的新方法，并为主要申请人成为数学流行病学领域的公认领导者提供理想的培训场所。

项目成果

Health scores in Flusurvey participants: findings from the 2012-13 influenza season

Flusurvey 参与者的健康评分：2012-13 流感季节的调查结果

• DOI: 10.1016/s0140-6736(13)62447-2
• 发表时间: 2013
• 期刊: The Lancet
• 作者: Adler A

Potential for large outbreaks of Ebola virus disease.

• DOI: 10.1016/j.epidem.2014.09.003
• 发表时间: 2014-12
• 期刊: EPIDEMICS
• 影响因子: 3.8
• 作者: Camacho, A.;Kucharski, A. J.;Funk, S.;Breman, J.;Piot, P.;Edmunds, W. J.
• 通讯作者: Edmunds, W. J.

Comparative analysis of dengue and Zika outbreaks reveals differences by setting and virus

登革热和寨卡疫情的比较分析揭示了不同环境和病毒的差异

• DOI: 10.1101/043265
• 发表时间: 2016
• 作者: Funk S

Extending the elderly- and risk-group programme of vaccination against seasonal influenza in England and Wales: a cost-effectiveness study.

• DOI: 10.1186/s12916-015-0452-y
• 发表时间: 2015-10-13
• 期刊: BMC medicine
• 影响因子: 9.3
• 作者: Baguelin M;Camacho A;Flasche S;Edmunds WJ
• 通讯作者: Edmunds WJ

Assessing optimal target populations for influenza vaccination programmes: an evidence synthesis and modelling study.

• DOI: 10.1371/journal.pmed.1001527
• 发表时间: 2013-10
• 期刊: PLoS medicine
• 影响因子: 15.8
• 作者: Baguelin M;Flasche S;Camacho A;Demiris N;Miller E;Edmunds WJ
• 通讯作者: Edmunds WJ