Modelling leptospirosis at the animal-human interface in island populations

钩端螺旋体病是一种对动物和人类都有影响的细菌传染病。据估计，世界各地每年有100万例人类病例和5.8万人死亡。大多数病例发生在热带和亚热带地区，太平洋岛屿报告的发病率较高。细菌通过受感染动物的尿液传播到环境中，人类可能通过直接接触动物或通过受污染的水和土壤感染。理解钩端螺旋体病在宿主之间传播的传播途径是一项挑战，因为有多种动物可被感染(例如啮齿动物、牲畜和狗)，以及传播的环境和社会驱动因素(例如洪水、温度和贫困)。该项目旨在将一种新的传播模式与历史疾病监测和环境数据相结合，以斐济为案例研究钩端螺旋体病如何在宿主之间传播。我将使用一个数学模拟模型来解释不同的动物和人类是如何感染的，它们是如何将疾病传播给彼此的，以及导致疫情爆发的风险因素。然后我会将其与历史数据相匹配，以便它对现实世界的决策有用。随后，将进一步开发这一模型，以探索如何最好地控制钩端螺旋体病，以及制作一种预测未来爆发的工具。在斐济采用这一办法的基础上，有可能将这一工具和模式推广到其他环境，如太平洋其他岛屿，甚至其他受影响国家。这个项目将使我在数学和统计建模方面获得定量技能。我将学习如何操纵大数据，并将新模型与历史健康监测和遥感数据相结合。此外，我还将有机会在现场和会议上与国际研究人员和利益攸关方互动。据认为，未来全球钩端螺旋体病的病例数量将会增加。据预测，气候变化将增加洪水等极端天气事件的可能性，同时不断扩大的种群规模将需要更集约化的农业，因此受影响的种群将生活在离动物更近的地方。了解钩端螺旋体病是如何在宿主之间传播的，是规划和实施有效卫生干预措施的关键。希望该项目将开始允许受钩端螺旋体病影响的国家的政府预测何时发生疫情，并在了解其传播方式的基础上，采取措施防止这些疫情的爆发。关键词：数学模型、传染病、钩端螺旋体病、定量分析、环境变化、全球健康、全球健康。

Leptospirosis is a bacterial infectious disease which affects both animals and humans. There are an estimated one million human cases a year and 58,000 deaths which occur all over the world. The majority of cases occur in tropical and sub-tropical regions, with high incidence reported in Pacific Islands. The bacteria are passed into the environment in the urine of infected animals, and humans can become infected either by direct contact with animals, or through contaminated water and soil. Understanding the transmission pathways by which leptospirosis is passed between hosts is a challenge, as there are multiple animals that can be infected (e.g. rodents, livestock and dogs), as well as environmental and social drivers of transmission (such as flooding, temperature and poverty). This project aims to combine a new transmission model with historical disease surveillance and environmental data to characterise how leptospirosis is passed between hosts, using Fiji as a case study. I will use a mathematical simulation model to explain how different animals and humans become infected, how they pass the disease to each other, and the risk factors that give rise to outbreaks. I will then fit this to historical data so it is useful for real world decision-making. Subsequently, this model will be further developed to explore how best to control leptospirosis, as well as make a tool to predict future outbreaks. Building on this approach in Fiji, it may be possible to extend this tool and model to other settings, such as other islands in the Pacific and even other affected countries. This project will allow me to gain quantitative skills in mathematical and statistical modelling. I will learn how to manipulate big data and combine new models with historical health surveillance and remote sensing data. In addition, I will have the opportunity to interact with international researchers and stakeholders in the field and at conference.It is thought that the number of cases of leptospirosis will increase globally in the future. Climate change is predicted to increase the likelihood of extreme weather events, such as flooding, whilst increasing population sizes will require more intensive farming, and so affected populations are going to be living closer to their animals. Understanding how leptospirosis is passed between hosts is key to planning and implementing effective health interventions. The hope is that this project will begin to allow the governments of countries affected by leptospirosis to predict when outbreaks will occur and, in understanding how it is transmitted, take steps to prevent these outbreaks. Key words: Mathematical modelling, infectious disease, leptospirosis, quantitative analysis, environmental change, global health, planetary health.