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

新出现的传染病对人类和动物健康提出了全球性挑战。大多数新出现的疾病都是由病原从野生动物传播到牲畜引起的。然而，传播是双向的，既定疾病重新传播到野生动物中，导致保护危机，然后又传播到不同地点的牲畜身上。包括新冠病毒在内的新型病原体从野生动物直接传播（即没有媒介）到牲畜和人类是流行病的主要来源。该项目将评估稀缺时期日益短暂的地表水供应和高密度动物聚集对动物健康和疾病传播的影响。通过提出以下问题：研究区域内野生和家养有蹄类动物中某些已确定的尚未研究的病原体的流行程度是多少？目标疾病的流行程度在不同属性和季节的空间和时间上有何不同？潜在的空间和时间是什么？所研究疾病的传播热点？疾病在不同物种和空间之间的遗传差异如何？这些未被充分研究的病原体如何影响野生动物和牲畜？过去水资源供应的变化与全球人类疾病爆发有何关系，以及如何预测未来不同气候情况下的疾病爆发？该项目采用“同一个健康”方法，将涉及许多学科的使用，但不限于建模、流行病学、分子生物学、免疫学、寄生虫学和生态学。实地工作将在肯尼亚莱基皮亚进行，从不同季节的不同房产收集水和粪便样本。这些将通过聚合酶链反应（PCR）进行分子筛选，以确定目标病原体的存在。贝叶斯模型将用于确定不同地区病原体丰度的变化，这些变化取决于可用水源的数量和类型，以及野生动物和牲畜之间以及不同季节之间的不同相互作用，以确定湿地和湿地之间的水和资源可用性如何变化。旱季影响病原体的存在。预测模型将使我们能够根据空间和时间数据预测病原体的潜在存在，从而识别潜在的传播热点（例如，在有限的水源周围）和潜在的关注时间段（例如，动物栖息地的干旱时期）围绕剩余的有限资源进行聚合）。然后对 PCR 产物进行测序，以识别不同的物种和亚种。然后将创建网络，识别病原体的不同物种和单倍型，以及发现它们的动物物种、地点和一年中的时间。这些信息将有助于加深对传播动态以及某些地区和物种中存在哪些病原体的了解，从而提供证据支持或反对跨物种传播。虽然人们经常对疾病和病原体如何影响人类健康进行广泛的研究，但对动物健康的影响却常常被忽视。特别是在野生动物中，许多疾病被认为是无症状的，尽管支持这一点的证据很少。因此，我们可以通过使用旨在识别粪便中炎症生物标志物水平的酶联免疫吸附测定（ELISA）来开始了解这些病原体如何对动物健康产生潜在的负面影响。这可以识别对野生动物造成潜在健康影响并从而影响宿主健康的病原体。最后，该项目将进行一项荟萃分析，研究全球可用水量的历史变化和全球人类腹泻的爆发。建模将允许确定水供应和腹泻爆发之间的模式，并且将能够根据未来的降雨量来预测人类社区未来腹泻爆发的预测