Modelling leptospirosis at the animal-human interface in island populations

钩端螺旋体病是一种细菌性传染病，影响动物和人类。据估计，全世界每年有100万人感染艾滋病，58，000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进入环境，人类可以通过直接接触动物或通过受污染的水和土壤感染。了解钩端螺旋体病在宿主之间传播的传播途径是一项挑战，因为有多种动物可以被感染（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）。该项目旨在将联合收割机与历史疾病监测和环境数据相结合，以斐济为案例研究，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被感染的，它们是如何相互传播疾病的，以及引起疾病爆发的风险因素。然后，我将把它与历史数据相匹配，以便它对真实的世界决策有用。随后，该模型将进一步发展，以探索如何最好地控制钩端螺旋体病，以及使一个工具，以预测未来的爆发。以斐济的这一做法为基础，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。该项目将使我获得数学和统计建模方面的定量技能。我将学习如何操作大数据，并将联合收割机新模型与历史健康监测和遥感数据相结合。此外，我将有机会与国际研究人员和利益相关者在该领域和会议上互动。据认为，钩端螺旋体病的病例数将在未来全球范围内增加。据预测，气候变化将增加极端天气事件的可能性，如洪水，而人口规模的增加将需要更密集的农业，因此受影响的人口将与他们的动物住得更近。了解钩端螺旋体病如何在宿主之间传播是规划和实施有效卫生干预措施的关键。希望这个项目将开始，使受钩端螺旋体病影响的国家的政府能够预测何时爆发，并了解它是如何传播的，采取措施防止这些爆发。关键词：数学建模，传染病，钩端螺旋体病，定量分析，环境变化，全球健康，行星健康。

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.