Designing agricultural landscapes to limit zoonotic disease risk in The Gambia

在一个快速变化的世界中，疾病从野生动物向人类的传播（人畜共患传播）正在成为全球卫生的一个关键部分。在人为引起的土地利用变化和气候变化的背景下，这一点尤其重要，因为它们不断对环境和社会经济地位产生消极影响。这反过来又可以迫使农业实践适应或改变，为人畜共患疾病的传播途径创造一个动态环境。在冈比亚，农林业，即结合树木种植和养护的农业做法正在被用来缓解单方面粮食生产和自然养护做法之间的紧张关系，并使它们的努力协同起来。然而，这种情况的一个意想不到的后果可能是牲畜和人类更接近疾病携带者（即病媒），导致交叉污染和人畜共患疾病病例数量增加。农业促进粮食生产和安全，自然保护促进气候恢复，疾病预防和控制促进人口健康，这是繁荣社会的三大支柱。朝着这些支柱的“帕累托最优”迈进，必须成为一个健康研究人员的目标。人畜共患病的驱动因素和现有的各种农业实践是复杂的，理解它们之间的相互作用需要一种全面的方法，这可以通过培养一种合作、多学科、跨学科和跨学科工作的文化来实现。通过定量和定性方法的结合，将收集大量证据，使人们了解人畜共患疾病是如何在农业景观中传播的，这符合英国研究与创新倡导的战略和核心技能。翻译这些证据是必要的，因为1)将其传播和解释给政策制定者，以制定基于证据的政策；2)在用户友好的工具中使用它，这将提高农学家自己的认识；如果他们不了解健康景观对健康生活的重要性，就无法实现长期、可持续的变革。为了开始将冈比亚的人畜共患病和农业景观纳入背景，将对文献进行审查，以整理记录冈比亚农业实践及其演变以及人畜共患病病例的证据。同样，对人畜共患病原体的现有基因组序列的回顾将使冈比亚的人畜共患景观开始被量化。这些数据的空白将通过在两种景观类型（农业和半自然）进行的主要数据收集来填补。将从小型野生动物、家畜和人身上收集样本并对其进行测序，对于一组已确定的高风险病原体，将使用生物信息学和分子流行病学方法建立涉及传播的所有当地行为者的接触网络。参与式建模和人种学方法将被用来更好地理解人类如何与网络互动。在系统生物学和建模领域领先专家的支持下，社会生态建模方法将预测两种情况下的人畜共患病风险以及理论上的未来情景，目的是为疫情防范和缓解战略提供信息。

In a rapidly changing world, the spread of diseases from wildlife to humans (zoonotic transmission) is becoming an key part of global health. This is particularly important within the context of human-induced land use change and climate change, which continually have a negative impact on environments and socio-economic status. This in turn can force agricultural practices to adapt or change in response, creating a dynamic setting for zoonotic transmission pathways.In The Gambia, agroforestry, the practice of agriculture incorporating tree cultivation and conservation is being used to relieve tension between unilateral food production and nature conservation practices, and synergise their endeavours. Nevertheless, an unintended consequence of this could be increased proximity of livestock and humans to carriers of disease (i.e., vectors), resulting in cross-contamination and increased numbers of zoonotic disease cases. Agriculture for food production and security, nature conservation for climate restoration and diseases prevention and control for a healthy population are three key pillars of a thriving society. Moving towards the "pareto optimum" of these pillars must be a goal for one health researchers.The drivers of zoonoses and the variety of agricultural practices existing are complex, and understanding their interaction requires a holistic approach which can be achieved by fostering a culture of collaboration, multi-, trans-, and inter-disciplinary work. Through a combination of quantitative and qualitative methods, a body of evidence will be amassed that provides an understanding how zoonotic disease transmission occurs across agricultural landscapes, in line with the strategy and core skills advocated by UK Research and Innovation. Translation of this evidence will be necessary, for 1) its dissemination and interpretation to policy makers to create evidence-based policy; and 2) its use in user-friendly tools that will increase awareness in agriculturalists themselves; without their appreciation of the importance of healthy landscapes for healthy lives, long-term, sustainable change will not be achieved.To begin contextualising the zoonotic and agricultural landscape of The Gambia, a review of the literature will be performed to collate the evidence documenting The Gambia's agricultural practices and their evolution as well as cases of zoonotic disease. Similarly, a review of the available genome sequences of zoonotic pathogens will allow The Gambia's zoonotic landscape to start being quantified. Gaps in these data will be filled through primary data collection taking place across two landscape types: agricultural and seminatural. Samples will be collected and sequenced from small bodied wild animals, domestic animals, and humans and for a group of high-risk pathogens identified, bioinformatic and molecular epidemiological approaches will be used to create contact networks of all local actors involved in transmission. Participatory modelling and ethnographic methods will be employed to better understand how humans interact with the network. With the support of leading experts in systems biology and modelling, a socio-ecological modelling approach will predict zoonotic risks across the two landscapes and in theoretical future scenarios, with the aim of informing outbreak preparedness and mitigation strategies.