Spatio-temporal dynamics of water and faecal borne pathogens in livestock and wildlife populations in Laikipia, Kenya.

肯尼亚莱基皮亚牲畜和野生动物种群中水和粪便传播的病原体的时空动态。

• 批准号: 2865560
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2865560
• 关键词: Spatio; temporal; dynamics; water; faecal

Emerging infectious diseases present a global challenge to human and animal health. Most emerging diseases arise from transmission of disease agents from wildlife to livestock. However, transmission moves both ways, with established diseases moving back into wildlife leading to conservation crises and then back to livestock transformed and in different locations. Direct transmission (i.e. without vectors) of novel pathogens, including COVID19, from wildlife into livestock and humans is the primary source of pandemics. This project will evaluate how increasingly ephemeral surface water availability and high-density animal aggregations during periods of scarcity impacts on animal health and disease transmission. By asking the following questions:What is the prevalence of certain identified understudied pathogens in both wild and domestic ungulates across the study area?How does the prevalence of the target diseases differ across spatially and temporally across properties and seasons?What are potential spatial and temporal hotspots for transmission of the studied diseases?How to diseases differ genetically across species and space? How do these understudied pathogens affect wildlife and livestock?How past changes in water availability relates to human disease outbreaks globally and how this can predict future outbreaks under different climate scenarios?This project employs a One Health approach will involve the use of many disciplines included but not limited to modelling, epidemiology, molecular biology, immunology, parasitology, and ecology. Fieldwork will be undertaken in Laikipia Kenya to collect water and faecal samples from different properties across different seasons. These will be molecularly screened via polymerase chain reaction (PCR) to identify the presence of target pathogens. Bayesian modelling will be used to identify changes in pathogen abundance across properties that differ in the amount and types of water sources available as well as differing interactions between wildlife and livestock as well as across seasons to identify how changing water and resource availability between the wet and dry seasons influence pathogen presence. Predictive models will allow for us to predict the potential presence of pathogens based on this spatial and temporal data allowing for the identification of potential hotspots of transmission (e.g around limited sources of water) and potential time periods of concern (e.g periods of drought where animals aggregate around remaining limited resources). PCR products will then be sequenced allowing for different species and subspecies to be identified. Networks will then be created that identify different species and haplotypes of pathogens and which animal species, locations, and times of year they are found in. This information will help increase understanding of transmission dynamics and which pathogens are present in certain areas and species providing evidence for or against cross species transmissions. Whilst there is often extensive research on how diseases and pathogens affect human health, animal health impacts are often neglected. Especially in wildlife many diseases are considered to be asymptomatic despite evidence supporting this being low. Therefore, we can begin to understand how animal health is potentially negatively affected by these pathogens by using enzyme-linked immunosorbent assays (ELISAs) aimed at identifying levels of inflammatory biomarkers in faeces. This could identify pathogens that are causing underlying health impacts in wild animals and as a result having impacts on host fitness. Finally, this project will undertake a meta-analysis looking at global historical changes in water availability and global human diarrhoeal outbreaks. Modelling will allow patterns between water availability and diarrhoeal outbreaks to be identified and future predictions of diarrhoeal outbreaks in human communities will be able to be predicted based on future rainfal

新兴的传染病对人类和动物健康提出了全球挑战。大多数新兴疾病是由从野生动植物到牲畜传播的。但是，传播的两种方式移动，建立的疾病回到野生动植物中，导致保护危机，然后回到牲畜转变的牲畜和不同的位置。从野生动植物到牲畜和人类的新型病原体的直接传播（即无载体）是大流行病的主要来源。该项目将评估短暂的地表水利用率和高密度动物聚集在稀缺时期对动物健康和疾病传播的影响。通过提出以下问题：在整个研究区域中，野生和家庭未耕种的某些已识别出的病原体的流行率是什么？目标疾病的患病率在空间和时间上在跨时间和季节之间在空间和时间上有何差异？潜在的空间和颞型热点在研究中疾病的传播疾病跨种类和空间有何不同？这些经过研究的病原体如何影响野生动植物和牲畜？过去的水可用性与人类疾病爆发的过去如何与全球爆发有关？如何在不同的气候场景下预测未来的爆发？该项目采用一种健康方法将涉及一种健康方法，将涉及使用许多学科，但不限于建模，流行病学，分子生物学，免疫学，副学，以及生态学，生态学，生态学。肯尼亚的莱基皮亚将进行实地调查，以收集不同季节不同特性的水和粪便样品。这些将通过聚合酶链反应（PCR）进行分子筛选，以鉴定靶病原体的存在。贝叶斯建模将用于确定在可用的水源的数量和类型上以及野生动植物与牲畜之间以及整个季节之间的不同相互作用的不同性质的病原体丰度变化，以确定潮湿和干旱季节之间的水和资源可用性如何影响病原体的存在。预测模型将使我们能够根据这种空间和时间数据来预测病原体的潜在存在，从而允许鉴定潜在的传播热点（例如，围绕有限的水源围绕有限的水源）和潜在的关注时间（例如，干旱的时期动物围绕剩余资源汇总的动物汇总）。然后，将对PCR产品进行测序，以识别不同的物种和亚种。然后将创建网络，以识别病原体的不同物种和单倍型，以及它们一年中发现的动物，位置和时间。此信息将有助于提高对传播动态的理解，以及在某些地区和物种中存在哪些病原体，从而提供或反对跨物种传播的证据。尽管经常对疾病和病原体如何影响人类健康的广泛研究，但经常忽略动物健康的影响。尤其是在野生动植物中，尽管有证据支持这一点，但许多疾病被认为是无症状的。因此，我们可以通过使用旨在识别粪便中炎症性生物标志物水平的酶联免疫吸附测定法（ELISA）（ELISA）开始理解这些病原体如何对动物健康产生负面影响。这可以确定导致野生动物对健康影响的病原体，从而对宿主健身产生影响。最后，该项目将进行一项荟萃分析，以研究全球供水和全球人类腹泻暴发的历史变化。建模将允许确定水的供应和腹泻爆发之间的模式，并可以根据未来的Rainfal预测人类社区腹泻暴发的未来预测