Dynamic zoonotic disease modelling for environmental change

环境变化的动态人畜共患疾病模型

• 批准号: 1945368
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1945368
• 关键词: Dynamic; zoonotic; disease; modelling; environmental

Human infectious diseases are a significant threat to global human health and economies (e.g., Ebola, SARS), with the majority of infectious diseases having an animal source (zoonotic) (Jones et al. 2008 Nature 451:990). Despite their public health relevance, many important diseases have not been systematically studied from a quantitative perspective, limiting our understanding of how spillovers of zoonotic infectious diseases into the human population are impacted by global and local environmental stressors (Whitmee et al. 2015 Lancet 386:1973). Furthermore, for most diseases little is known about how climate change, anthropogenic landscape alteration and changing populations will impact on future infectious disease outbreaks (Hotez & Kamath 2009 PLoS Negl Trop Dis 3:e412). There is therefore an urgent need for a more interdisciplinary approach integrating computational modelling, ecology and health towards a holistic understanding of disease dynamics. Computational modelling can play a significant role in prediction of potential threats and evaluation of intervention strategies, yet effective modelling of zoonotic diseases requires an interdisciplinary breadth that is seldom achieved. In this project, we bring together leading computational, ecological and epidemiological expertise to develop a new integrated framework of disease modelling for the important endemic Lassa fever (LF) disease in West Africa. Lassa fever virus is the cause of one of the most prevalent viral haemorrhagic fevers in the region. Serology-based techniques estimate between 100,000 to 300,000 LF cases per year (compare to Ebola 30,000 total cases since 1970), with a fatality rate that ranges between 1% and 69% depending on the setting. Despite its importance, relatively few studies have tackled the problem of modelling the epidemiological dynamics of LF on a large scale (Redding et al. 2016 Methods in Ecol. & Evol 7:646). As in most zoonotic diseases, much of the complexity arises from the interplay of disease dynamics in reservoir (animal) populations and the human population, as well as the spillover between the reservoir and humans. All of these processes happen concurrently in a spatially heterogeneous and dynamic environment; understanding the origin of spatially localised phenomena such as hotspots is essential to predict effects of environmental change and evaluate intervention strategies. Our ambition in this PhD project is to combine ecological models of animal reservoir, spillover and human disease spread in a single probabilistic modelling framework which will enable full uncertainty quantification and prediction. Importantly, our approach will leverage recent developments in the statistical computing community (Schnoerr et al. 2016 Nature Comms 7) to retain a higher level of mechanistic detail that is currently possible within epidemiological models. This will enable us to provide model-based predictions of responses to a changing environment, and to evaluate the impact of intervention strategies in plausible future scenarios. This project will work in close collaboration with the Institute of Global Health at UCL and partners at The Centre for Disease control for Nigeria and the west African hub of the African Union to embed the research into policy and priority setting within the stakeholder communities across west Africa.

人类传染病是对全球人类健康和经济的重大威胁（例如，埃博拉、SARS），大多数传染病具有动物来源（人畜共患病）（Jones等人，2008 Nature 451：990）。尽管它们与公共卫生相关，但许多重要疾病尚未从定量角度进行系统研究，限制了我们对人畜共患传染病对人群的溢出如何受到全球和当地环境压力影响的理解（Whitmee et al. 2015 Lancet 386：1973）。此外，对于大多数疾病，人们对气候变化、人为景观改变和人口变化将如何影响未来的传染病爆发知之甚少（Hotez & Kamath 2009 PLoS Negl Trop Dis 3：e412）。因此，迫切需要一种更加跨学科的方法，将计算建模、生态学和健康结合起来，以全面了解疾病动态。计算建模可以在预测潜在威胁和评估干预战略方面发挥重要作用，但有效的人畜共患病建模需要很少实现的跨学科广度。在该项目中，我们汇集了领先的计算、生态和流行病学专业知识，为西非重要的地方性拉沙热（LF）疾病开发了一个新的疾病建模综合框架。拉沙热病毒是该区域最流行的病毒性出血热之一的病因。基于血清学的技术估计每年有100，000至300，000例LF病例（相比之下，自1970年以来埃博拉病例总数为30，000例），死亡率在1%至69%之间。尽管其重要性，但相对较少的研究解决了大规模LF流行病学动态建模的问题（Redding et al. 2016 Methods in Ecol. & Evol 7：646）。与大多数人畜共患疾病一样，其复杂性主要来自宿主（动物）种群和人类种群中疾病动态的相互作用，以及宿主和人类之间的溢出效应。所有这些过程都同时发生在一个空间异质性和动态的环境中;了解空间局部化现象的起源，如热点，对于预测环境变化的影响和评估干预策略至关重要。在这个博士项目中，我们的目标是将动物水库，溢出和人类疾病传播的联合收割机生态模型结合在一个概率建模框架中，这将使完全的不确定性量化和预测成为可能。重要的是，我们的方法将利用统计计算界的最新发展（Schnoerr et al. 2016 Nature Comms 7），以保留目前流行病学模型中可能存在的更高水平的机制细节。这将使我们能够提供基于模型的预测对不断变化的环境的反应，并评估干预策略在合理的未来情景中的影响。该项目将与伦敦大学学院全球卫生研究所以及尼日利亚疾病控制中心和非洲联盟西非中心的合作伙伴密切合作，将研究嵌入到整个西非利益相关者社区的政策和优先事项制定中。