Genomic Epidemiology and Transmission of Campylobacter in Africa (GETcampy-Africa)

腹泻病仍然是中低收入国家儿童发病、生长迟缓和死亡的一个主要原因，其中弯曲杆菌是最常见的原因之一。英国的主要感染源包括受污染的食物，但中低收入国家的传播途径尚不清楚。这意味着对风险最高的儿童（在低收入和中等收入国家中，1岁前感染的儿童占85%）之间的传播研究最少。房屋拥挤、与动物同居以及卫生条件差/食品安全都是潜在的风险因素，但有效的干预措施取决于对感染源的定量估计。那么，为什么弯曲杆菌在中低收入国家大多被忽视了呢？虽然这个问题的答案部分与感染的亚临床散发性质和培养嗜微气细菌的困难有关，但一个更令人不安的原因是，人们面临最大风险的国家经济和发展状况较低。这种认识使我决定将未来的研究，以及我所开发的知识和资源，用于在中低收入国家最需要我的技能的地方与弯曲杆菌作斗争。弯曲杆菌病的流行病学在中低收入国家了解甚少。在初步研究中，我们已经确定了菌株的基因组变异，这可能表明在来源、存活、传播和毒性方面的差异（与英国相比）。特别是，我们已经确定了全球和当地分布的菌株，家庭内部传播的证据以及与无症状感染和其他肠道病原体感染相关的菌株。基因组测序技术和生物信息学分析通过识别来自多个来源的毒株之间的差异并跟踪其传播，为解释这些隐性疾病网络提供了一种手段。然而，基因组来源归属的有效实施不仅依赖于先进的比较基因组学技术，还依赖于适当的样本框架的部署和微生物实验室的准入。为了实现这一目标，我通过对非洲的几次访问，建立了一个合作者网络，可以进行所需的多中心抽样（与以前的国家规模研究一致），包括广泛的横断面抽样和哨点监测，从而计算弯曲杆菌的总体负担，并进行病例对照比较，以量化无症状感染。信息将输入来源归因和货币化决策支持模型，以便通过公共卫生接触对风险进行知情评估和有针对性的干预。如果资金到位，我将重新定位到冈比亚每年3-6个月，在我自己的费用。在英国和非洲（冈比亚、加纳、布基纳法索）已建立的合作网络的基础上，我们将制定一个全球化弯曲杆菌NGS监测项目。具体而言，我们将：(i)对来自动物、食物、环境来源和人（有症状的、无症状的以及匹配的病例和对照）的数千个分离株进行取样和基因组测序；（ii）开发开放获取数据库和新的分析管道（关联研究和机器学习），以表征弯曲杆菌种群结构并确定来源归属标记；量化不同人类感染源的相对贡献；（iv）使用成本效益风险模型确定输电网络中最有效的干预措施。这种以证据为基础的方法将使当地的公共卫生和政策干预措施有效，并将努力重点放在减轻非洲腹泻病的负担上。

Diarrhoeal disease remains a major cause of child morbidity, growth faltering and mortality in low and middle income countries (LMICs), with Campylobacter among the most common causes. The major infection sources in the UK include contaminated food, but transmission routes in LMICs are unknown. This means that transmission among the children at highest risk (85% infected before 1yr in LMICs) is the least studied. House crowding, cohabitation with animals and poor sanitation/food safety are all potential risk factors, but effective interventions depend upon quantitative estimates of infection sources. So why is Campylobacter largely overlooked in LMICs? While the answer to this question, in part, relates to the perceived sub-clinical sporadic nature of infection and difficulties in culturing microaerophilic bacteria, a more unsettling reason is that the countries where people are at the greatest risk have low economic and development status. This realisation led to my decision to devote future research, and the knowledge and resources I have developed, to combat Campylobacter where my skills are most needed, in LMICs.The epidemiology of campylobacteriosis is poorly understood in LMICs. In pilot studies, we have identified genomic variation in strains that may indicate differences in source, survival, transmission and virulence (compared to the UK). In particular, we have identified globally and locally distributed strains, evidence of within household spread and strains associated with asymptomatic infection and infection with other enteropathogens. Genome sequencing technologies and bioinformatics analysis provide a means for explaining these cryptic disease networks by identifying differences between strains from multiple sources, and tracking their transmission. However, the effective implementation of genomic source attribution relies not only on advanced comparative genomics techniques, but also the deployment of an appropriate sample frame and access to microbiology laboratories. To achieve this I have developed a network of collaborators, through several visits to Africa, that can undertake the required multicentre sampling (consistent with previous national-scale studies) including both broad cross-sectional sampling and sentinel site surveillance allowing calculation of the overall Campylobacter burden as well as case-control comparison to quantify asymptomatic infection. Information will feed into source attribution and monetised decision support models, to allow informed assessment of risk and targeted intervention through public health contacts. If funded, I will re-locate to The Gambia for 3-6 months of each year, at my own expense.Building on an established collaborative network in the UK and Africa (The Gambia, Ghana, Burkina Faso), we will develop a program of globalized Campylobacter NGS surveillance. Specifically, we will: (i) sample and genome sequence thousands of isolates from animals, food, environmental sources and people (symptomatic, asymptomatic, and matched cases and controls); (ii) develop open-access databases and novel analysis pipelines (association study and machine learning) to characterize Campylobacter population structure and identify source attribution markers; (iii) quantify the relative contribution of different human infection sources; (iv) use a cost-benefit risk models to identify the most effective interventions in the transmission network. This evidence-based approach will enable effective local public health and policy interventions and focus efforts to reducing the burden of diarrhoeal disease in Africa.