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

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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