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。在人类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，这些变化不断地对环境和社会经济地位产生负面影响。这反过来可以迫使农业做法适应或改变应对措施，为人畜共患病的传播途径创造一个动态环境。在冈比亚，农林复合农业正在利用农业种植和养护相结合的做法来缓解单方面粮食生产和自然保护做法之间的紧张关系，并协同他们的努力。然而，这种情况的一个意想不到的后果可能是牲畜和人更接近疾病携带者(即媒介)，导致交叉污染和人畜共患疾病病例的增加。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。走向这些支柱的“帕累托最优”必须是卫生研究人员的目标。人畜共患病的驱动因素和现有的各种农业实践是复杂的，理解它们的相互作用需要一种整体的方法，这可以通过促进协作、多学科、跨学科和跨学科的工作文化来实现。通过定量和定性相结合的方法，将积累大量证据，根据英国研究和创新公司倡导的战略和核心技能，了解人畜共患病是如何在农业景观中传播的。有必要对这些证据进行翻译，1)向政策制定者传播和解释这些证据，以制定以证据为基础的政策；2)将其用于方便用户的工具，提高农学家本身的认识；如果他们不认识到健康景观对健康生活的重要性，就不可能实现长期、可持续的变化。同样，审查现有的人畜共患病病原体基因组序列将使冈比亚的人畜共患病情况开始量化。这些数据的空白将通过两种景观类型的初级数据收集来填补：农业景观和半自然景观。将从小型野生动物、家养动物和人类中收集样本并进行测序，对于已确定的一组高风险病原体，将使用生物信息学和分子流行病学方法建立参与传播的所有当地行为者的联系网络。将采用参与性建模和人种学方法，以更好地了解人类如何与网络互动。在系统生物学和建模方面的主要专家的支持下，社会生态建模方法将在两个景观和理论上的未来情景中预测人畜共患病风险，目的是为疫情防备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.