Antimicrobial resistance at the human-animal interface: filling in knowledge gaps where surveillance is scarce

抗菌素耐药性(AMR)和医学上重要药物的疗效下降是一个全球性的医疗紧急情况。AMR是一个典型的健康问题，影响着人、动物和环境的健康。作为一个健康问题，解决AMR需要跨多个部门、不同学科以及公共和私人机构的合作。在世界范围内，人类医药中使用的许多抗生素也被用于食用动物生产，不仅用于治疗患病的动物，还用于预防疾病、治疗接触者(过敏反应)和促进生长。人们普遍认为，这种往往监管不善的使用是AMR的一个重要驱动因素/压力，需要制定更清晰的宏观到微观图景，了解这种耐药性在特定地点是如何发展的。在发展中国家，大多数抗菌素耐药性(AMR)出现在畜人界面特别重要的社区环境中。但在我们将抗菌药物使用(AMU)和AMR联系起来的知识方面存在着显著的差距。该项目旨在开发一个数据框架，以低估关于动物健康、已知疾病流行和治疗以及药物可获得性的观察数据与导致AMR和抗病热点出现的经常未观察到的过程之间的联系。AMR是一种生物现象，可以出现在人类、动物和环境驱动因素和调节因素的相互作用中。该项目将寻求通过利用来自抗生素来源、分销网络、家庭决策和驱动抗菌素敏感性(耐药性)的已知生物过程的相对廉价信息来估计社区产生的抗生素耐药性(CGR)。为了弥合重大的数据差距和不确定性，该项目将利用人工智能和机器学习来改进AMR的预测建模，合理和合法地使用抗菌剂和抗生素组合，以及未来的研究方向。该项目将建立在盖茨资助的项目SEBI下管理的专业知识和数据的基础上，以支持(对牲畜的)循证干预。SEBI动员和应用数据和证据，帮助畜牧业社区进行更好的投资，改善低收入和中等收入国家小农的生计。该项目收集了关于疾病流行的系统数据，包括那些通常使用抗菌剂治疗的疾病，例如乳房炎。该项目最初将重点放在埃塞俄比亚的流行率数据上，并将利用尼日利亚的数据构建预测能力，使用将作为该项目的一部分开发和试验的自动化工具。学生将对解释AMR的人类和生物过程有端到端的理解，包括抗菌素敏感性测试(AST)方面的专业知识，以及用于抗菌素敏感性测试的全基因组测序。他们还将通过与SEBI团队、Roslin和GAAFS研究人员的互动以及与案例合作伙伴的一段时间的实习来发展各种研究、专业和项目管理技能

Antimicrobial resistance (AMR) and the falling efficacy of medically important drugs is a global medical emergency. AMR is the quintessential One Health problem, affecting the health of people, their animals, and the environment. As a One Health problem, addressing AMR demands collaborations across multiple sectors, a diversity of disciplines, and public and private institutions. Worldwide, many antibiotics used in human medicine are also used in food-animal production, not only for treating sick animals but also for disease prevention, treatment of in-contacts (metaphylaxis) and growth promotion. There is general agreement that this often poorly regulated use is a significant driver/pressure of AMR and there is a need to develop a clearer macro to micro picture of how this resistance develops in specific locations. In the developing world, the majority of antimicrobial resistance (AMR) arises in the community setting where the livestock-human interface is particularly important. But there are significant gaps in our knowledge linking antimicrobial use (AMU) and AMR. This project aims to develop a data framework for understating the links between observed data on animal health, known disease prevalence and treatments and drug availability and the frequently unobserved processes that lead to the emergence of AMR and disease resistance hotspots. AMR is a biological phenomenon that can emerge from the interplay of human, animal and environmental drivers and conditioners. The project will seek to estimate community-generated antibiotic resistance (CGR) by exploiting relatively cheap information from antibiotic sources, distribution networks, household decision making and the known biological processes that drive antimicrobial susceptibility (to resistance). In seeking to bridge significant data gaps and uncertainties the project will draw on Artificial Intelligence and Machine Learning to improve predictive modelling of AMR, the rational and legal use of antimicrobials and antibiotic combinations, as well as future research directions.The project will build on expertise and data curated under the Gates-funded project SEBI Supporting Evidence Based Interventions (for livestock). SEBI mobilises and applies data and evidence to help the livestock community make better investments that improve livelihoods for smallholders in low and middle-income countries. The project has collected systematic data on disease prevalence including those normally treated with antimicrobials e.g. mastitis. The project would initially focus on prevalence data for Ethiopia and will use this to construct predictive capabilities with Nigerian data, using automation tools that will be developed and trialled as part of this project. The student will develop an end-to-end understanding of the human and biological processes that explain AMR including expertise in antimicrobial sensitivity testing (AST) and the other is whole-genome sequencing for antimicrobial sensitivity testing. They will also develop a variety of research, professional and project management skills through interaction with the SEBI team, Roslin and GAAFS researchers plus a period of internship with the CASE partners