Machine learning for tracing pathogens in the food chain

用于追踪食物链中病原体的机器学习

• 批准号: 2897056
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2897056
• 关键词: Machine; learning; tracing; pathogens; food

"Salmonella enterica is a leading cause of human gastroenteritis worldwide, with non-typhoidal Salmonella estimated to account for ~1 billion infections and ~150,000 deaths annually. This gastrointestinal pathogen therefore represents a major public health concern, necessitating real-time epidemiological monitoring and follow-up. Outbreak investigations, however, are often confounded by the complexity of international food-trade networks which distribute zoonotic food-borne pathogens across the globe. This project aims to address this gap by utilising machine learning (ML) to predict the geographical source of gastrointestinal pathogens directly from genomic surveillance data, allowing for improved public health response and more rapid outbreak resolution. Public health agencies, such as the US Centers for Disease Control (CDC), UK Health Security Agency (UKHSA) and Public Health Agency of Canada (PHAC), routinely apply whole genome sequencing (WGS) to clinically identified cases of Salmonella alongside collecting relevant metadata. WGS contains contextual genetic information on geographical origin, of particular relevance to foodborne pathogens due to their presence in complex foodstuffs consisting of ingredients from multiple sources. However, traditional methods for inferring geographical origin from WGS demand extensive expertise and high computational costs while not scaling effectively. We recently established that ML is an effective tool for the geographical source attribution of Salmonella Enteritidis (PMID: 37042517), the most prominent cause of foodborne illness in the UK. This studentship will build upon this methodological foundation, working alongside public health specialists from the UKHSA, PHAC and CDC to synthesis international genomic datasets for the top three Salmonella species. Outcomes will result in rapid and accurate geographical source attribution models suitable for immediate integration with public health agencies to enhance existing disease management responses. This will be achieved via three primary objectives: a) Co-produce knowledge required for effective source attribution In order to deliver a robust decision-support tool for epidemiologists, automated predictions should be both accurate and understandable by end-users. The student will regularly meet with expert stakeholders in UKHSA, CDC and PHAC and ensure the predictive ML frameworks developed in objectives (b) and (c) contain information relevant to epidemiological follow-up and end-user interpretation. b) Investigate the phylogeographical signal in Salmonella genomes The student will collate genomic surveillance datasets of Salmonella enterica serovars Enteritidis, Typhimurium and Newport provided to the project by the UKHSA, CDC and PHAC, and will perform an analysis of phylogeographical signal (i.e. how clustered the genomic data are by geographical origin). This will provide actionable insights into Salmonella species, allowing for the flagging of regionally restricted clones highly-suitable for ML classification as well as problematic international clones which require enhanced classification methods in (c). c) Optimise source attribution models for Salmonella species Using the samples collected in (b), the student will build source attribution models using hierarchical ML and deep-learning frameworks to predict the geographical sources of outbreaks. These models will use state-of-the-art explainable ML approaches to facilitate rapid, targeted outbreak responses. "

“肠沙门氏菌是全世界人类胃肠炎的主要原因，据估计，非伤寒沙门氏菌每年导致约 10 亿感染和约 150,000 人死亡。因此，这种胃肠道病原体是一个重大的公共卫生问题，需要实时流行病学监测和后续行动。然而，疫情调查常常因分发人畜共患病的国际食品贸易网络的复杂性而感到困惑。 全球范围内的食源性病原体。该项目旨在通过利用机器学习 (ML) 直接根据基因组监测数据预测胃肠道病原体的地理来源来弥补这一差距，从而改善公共卫生反应并更快地解决疫情问题。公共卫生机构，例如美国疾病控制中心 (CDC)、英国卫生安全局 (UKHSA) 和加拿大公共卫生局 (PHAC)，通常会 将全基因组测序 (WGS) 应用于临床鉴定的沙门氏菌病例，同时收集相关元数据。全基因组测序包含地理起源的背景遗传信息，与食源性病原体特别相关，因为它们存在于由多种来源的成分组成的复杂食品中。然而，从全基因组测序推断地理起源的传统方法需要广泛的专业知识和高昂的计算成本，而且无法有效扩展。我们最近成立了 ML 是肠炎沙门氏菌 (PMID: 37042517) 地理来源归属的有效工具，肠炎沙门氏菌是英国食源性疾病的最主要原因。该奖学金将建立在这一方法论基础上，与 UKHSA、PHAC 和 CDC 的公共卫生专家合作，合成前三种沙门氏菌的国际基因组数据集。结果将导致快速、准确的地理来源 适合立即与公共卫生机构整合的归因模型，以加强现有的疾病管理应对措施。这将通过三个主要目标来实现： a) 共同产生有效来源归因所需的知识 为了为流行病学家提供强大的决策支持工具，自动预测应该准确且易于最终用户理解。学生将定期与 UKHSA、CDC 和 PHAC 的专家利益相关者会面，并确保预测性 目标 (b) 和 (c) 中开发的机器学习框架包含与流行病学后续行动和最终用户解释相关的信息。 b) 研究沙门氏菌基因组中的系统发育地理学信号 学生将整理由 UKHSA、CDC 和 PHAC 提供给该项目的肠炎沙门氏菌血清型 Enteritidis、Typhimurium 和 Newport 的基因组监测数据集，并对 系统地理学信号（即基因组数据如何按地理起源聚类）。这将为沙门氏菌物种提供可操作的见解，允许标记非常适合机器学习分类的区域限制克隆，以及需要在（c）中增强分类方法的有问题的国际克隆。 c) 优化沙门氏菌物种的来源归因模型 使用 (b) 中收集的样本，学生将建立来源归因 使用分层机器学习和深度学习框架的模型来预测疫情的地理来源。这些模型将使用最先进的可解释的机器学习方法来促进快速、有针对性的疫情应对。 ”