Spatio-temporal dynamics of plague vectors in Madagascar: using population genetics and landscape scale modelling to inform disease reduction strategi

许多新出现的疾病是人畜共患疾病（从动物传播给人类），在低收入国家，人类的接触率通常较高。鼠疫是最臭名昭著的人畜共患疾病之一，曾造成几次大流行。今天，马达加斯加是人间鼠疫病例的热点，约占世界年度病例的40%。人畜共患病流行病学循环涉及黑鼠、鼠和两种跳蚤。在2017年肺鼠疫大规模爆发后，人们对旨在减少鼠疫发病率的战略越来越感兴趣，其中一个选择是控制老鼠。然而，在哪里和何时控制老鼠，以及如何不同的控制方案可能会影响跳蚤的动态和疾病的transmissions.Detailed跳蚤时空动态和分散在马达加斯加的异质景观的知识仍然不清楚，但我们的鼠疫流行病学的理解和变化的疾病风险的预测至关重要。遗传和基因组数据可以提供关于人口联系的重要信息。老鼠种群表现出较弱的遗传结构，随着地形起伏而增加。然而，宿主和寄生虫的遗传结构之间的一致性并不总是被发现，并没有在马达加斯加跳蚤种群遗传学的大规模研究进行。为了帮助管理决策，需要将景观特征对鼠和蚤的种群连接性的影响的新信息与预测模型框架中的当地种群动态信息相结合。本项目将结合联合收割机先进的统计分析、种群遗传学和模型，利用现有的数据和样本（1）研究跳蚤丰度如何受到微生境和气候条件的影响，（2）对这两种跳蚤进行景观遗传学研究;（3）采用以个体为基础的建模方法，了解老鼠和跳蚤的景观动态以及不同控制制度的影响。该模型将在RangeShifter中实现，它集成了种群动力学，扩散行为和遗传学，并可用于模拟空间明确景观的场景。该项目将适合具有分子生态学或种群生态学背景的学生，对空间生态学，疾病生态学，景观遗传学感兴趣，并具有较强的数值技能。学生将与马达加斯加巴斯德研究所密切合作。学生将获得全面的多学科培训，将最先进的实验室技能和生态和遗传数据的统计分析与计算建模技能相结合。不同项目组成部分的相对重要性将取决于学生的兴趣。

Many emerging diseases are zoonotic (transmitted from animal to human), with human exposure typically higher in low income countries. Plague is one of the most infamous zoonoses, having been responsible for several pandemics. Today, Madagascar is a hot-spot for human plague cases, with approximately 40% of the world's annual cases. The zoonotic epidemiological cycle involves the black rat, Rattus rattus, and two species of flea. Following a large outbreak of pneumonic plague in 2017, there is increased interest in strategies aimed at reducing plague incidence, with rat control one option. However, where and when to control rats, and how different control options may impact on flea dynamics and disease transmission is still unclear.Detailed knowledge of flea spatio-temporal dynamics and dispersal in the heterogeneous landscapes of Madagascar is lacking, but critical for our understanding of plague epidemiology and predictions of changing disease risk. Genetic and genomic data can give important information on population connectivity. Rat populations show weak genetic structure that increases with topographic relief. However, concordance between host and parasite genetic structure is not always found and no landscape-scale study of flea population genetics in Madagascar has been conducted. To aid management decisions, new information on the impact of landscape features on population connectivity of both rats and fleas needs to be combined with information on local population dynamics in a predictive modelling framework.This project will combine advanced statistical analyses, population genetics and modelling, using existing data and samples to (1) examine how flea abundance is influenced by microhabitat and climatic conditions, (2) conduct a landscape genetic study of the two flea species and (3) use individual based modelling approaches to understand the landscape dynamics of rats and fleas and the impact of different control regimes. The modelling will be implemented in RangeShifter, which integrates population dynamics, dispersal behaviour and genetics, and can be used to simulate scenarios on spatially explicit landscapes.The project will suit a student with a background in molecular ecology or population ecology, who has interests in spatial ecology, disease ecology, landscape genetics, and who has strong numerical skills. The student will be expected to work closely with the Institut Pasteur de Madagascar. The student will be given a thorough multidisciplinary training, integrating state of the art laboratory skills and statistical analysis of ecological and genetic data with computational modelling skills. The relative importance of the different project components will depend on the interests of the student.