Predicting seasonal dynamics of UK mosquito vectors across urban and rural gradients

了解城市和农村的梯度如何以可能导致蚊媒疾病(例如西尼罗河病毒)暴发的方式改变蚊子的时间丰度，对于预测疾病的影响和管理至关重要。环境驱动因素(如降雨量、温度、蒸发、竞争和捕食)在城市到农村的梯度上有所不同，影响着蚊子的发育、繁殖力、生存，从而影响蚊子的数量。环境驱动因素的空间变异性也会影响蚊子赖以生存的野生动物和人类宿主的丰度和物候。虽然空间和土地利用梯度对病原体传播率有直接影响，但最微妙和最深刻的影响可能是对媒介物种物候和与敏感宿主的相互作用。雌性吸血蚊子数量的时间模式与敏感宿主的可获得性有关，这是蚊媒疾病能否在野生动物、人类和家畜中建立和持续存在的关键决定因素。该项目的目的是调查城市和农村梯度在推动蚊子种群季节和空间变异性以及与宿主重叠方面的作用。学生将开发和分析一个依赖于状态的延迟微分方程组，其中环境驱动因素影响生活史参数，并将重点放在英国蚊子物种，如库蚊和环库蚊，这些蚊子是潜在的疾病媒介。该模型将通过广泛的数学和数值模拟技术在高性能计算集群上使用合适的编程语言进行分析。此外，学生将使用广泛的统计技术针对新的和现有的时间数据集验证模型。最后，学生将推导和分析一个蚊子群落模型，以调查宿主的可获得性如何影响城市和农村的疾病可能性，控制蚊子物种、捕食者和非生物司机之间的竞争。总体而言，这项研究将证明预测模型的价值，这些模型有助于了解蚊子和蚊媒疾病的动态。

Understanding how urban and rural gradients alter mosquito temporal abundance in a way that is likely to cause outbreaks of mosquito-borne disease (e.g. West Nile virus) is central to predicting the impact and management of diseases. Environmental drivers (such as rainfall, temperature, evaporation, competition and predation) that vary along urban to rural gradients, affect development, fecundity, survival and therefore abundance of mosquitoes. Spatial variability in environmental drivers also affects abundance and phenology of wildlife and human hosts upon which the mosquitoes feed. Whilst spatial and land use gradients have direct effects on pathogen transmission rates, perhaps the most subtle and profound impacts are on vector species phenology and interactions with susceptible hosts. The temporal pattern of female-blood feeding mosquito abundance in relation to the availability of susceptible hosts is a key determinant of whether a mosquito-borne disease can establish and persist in wildlife, humans and domestic animals. The aim of this project is to investigate the role of urban and rural gradients in driving seasonal and spatial variability in mosquito populations and overlap with hosts. The student will develop and analyse a system of state-dependent delayed differential equations in which environmental drivers affect life-history parameters and development lags focusing on UK mosquito species such as Culex pipiens and Culiseta annulata, which are potential vectors of disease. The model will be analysed by extensive mathematical and numerical simulation techniques using a suitable programming language on high performance computing clusters. In addition, the student will validate the models against new and existing temporal datasets using a broad range of statistical techniques. Finally, the student will derive and analyse a mosquito community model to investigate how host availability shapes the likelihood of disease across urban and rural gradients controlling for competition between mosquito species, predation and abiotic drivers.Overall, the research will demonstrate the value of predictive models that help to understand the dynamics of mosquitoes and mosquito-borne diseases.