Dynamic zoonotic disease modelling for environmental change

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），大多数传染病都有动物源（人畜共患）（Jones et al. 2008 Nature 451:990）。尽管与公共卫生相关，但许多重要疾病尚未从定量角度进行系统研究，这限制了我们对人畜共患传染病对人群的溢出效应如何受到全球和当地环境压力的影响的理解（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暴发知之甚少（Hotez & Kamath 2009 PLoS nel 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，以全面了解疾病动力学。计算建模可以在预测潜在威胁和评估干预策略方面发挥重要作用，但人畜共患疾病的有效建模需要跨学科的广度，而这一点很少实现。在这个项目中，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（Lassa fever， LF）疾病建模制定了一个新的综合框架。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10万至30万例LF病例（相比之下，自1970年以来埃博拉病例总数为3万例），死亡率在1%至69%之间，具体取决于环境。尽管它很重要，但相对较少的研究解决了大规模模拟LF流行病学动态的问题（Redding et al. 2016 Methods in Ecol.）。（创世纪7:646）。与大多数人畜共患疾病一样，这种疾病的复杂性很大程度上源于宿主（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。所有这些过程在一个空间异构和动态的环境中同时发生；了解热点等空间局域现象的起源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生态模型结合在一个单一的概率建模框架中，这将使完全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社区的最新发展（Schnoerr et al. 2016 Nature Comms 7）来保留目前在流行病学模型中可能实现的更高层次的机制细节。这将使我们能够提供基于模型的预测，以应对不断变化的环境，并评估在可能的未来情景中干预策略的影响。该项目将与伦敦大学学院全球卫生研究所、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，将研究纳入西非利益攸关方社区的政策和优先事项确定。

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.