Machine learning for tracing pathogens in the food chain

“肠道沙门氏菌是全世界人类肠胃炎的主要原因，据估计，非伤寒沙门氏菌每年造成约10亿人感染，约15万人死亡。因此，这种胃肠道病原体是一个重大的公共卫生问题，需要进行实时流行病学监测和随访。然而，由于国际食品贸易网络在全球范围内传播人畜共患食源性病原体，疫情调查常常受到干扰。该项目旨在通过利用机器学习（ML）直接从基因组监测数据中预测胃肠道病原体的地理来源，从而改善公共卫生反应并更快地解决疫情，从而解决这一差距。美国疾病控制中心（CDC）、英国卫生安全局（UKHSA）和加拿大公共卫生署（PHAC）等公共卫生机构在收集相关元数据的同时，定期将全基因组测序（WGS）应用于临床鉴定的沙门氏菌病例。WGS包含地理来源的背景遗传信息，这与食源性病原体特别相关，因为它们存在于由多种来源成分组成的复杂食品中。然而，从WGS推断地理来源的传统方法需要大量的专业知识和高计算成本，而且不能有效地扩展。我们最近确定，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。该学生将在此方法论基础上，与来自UKHSA， PHAC和CDC的公共卫生专家一起工作，合成前三种沙门氏菌的国际基因组数据集。结果将产生快速和准确的地理来源归因模型，适合立即与公共卫生机构整合，以加强现有的疾病管理反应。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。为了向流行病学家提供强大的决策支持工具，自动化预测应该既准确又能被最终用户理解。学生将定期与UKHSA， CDC和PHAC的专家利益相关者会面，并确保目标(b)和(c)中开发的预测ML框架包含与流行病学随访和最终用户解释相关的信息。b)调查沙门氏菌基因组的系统地理信号。学生将整理由UKHSA、CDC和PHAC提供给该项目的肠炎沙门氏菌、鼠伤寒沙门氏菌和新港沙门氏菌的基因组监测数据集，并将对系统地理信号进行分析（即基因组数据如何按地理来源聚类）。这将为沙门氏菌物种提供可操作的见解，允许标记高度适合ML分类的区域限制性克隆以及需要(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属模型学生将使用分层机器学习和深度学习框架建立来源归因模型，以预测爆发的地理来源。这些模型将使用最先进的可解释ML方法来促进快速、有针对性的疫情应对。”

"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. "