West Nile Virus transmission risk from environmental, animal and human surveillance data

西尼罗河病毒(West Nile Virus，WNV)是一种新出现的蚊媒人畜共患病，在气候变化的背景下日益成为人们关注的公共卫生问题。西尼罗河病毒维持在禽类-蚊子传播周期中，偶尔会蔓延到作为死胡同宿主的人类身上。在人类中，大约80%的西尼罗河病毒感染是无症状的，但大约1%的人会患上致命的神经侵袭性疾病。由于人与人之间的传播可以通过器官或血液移植发生，因此欧洲在每个季节都会密切监测西尼罗河病毒的传播，以确保血液安全。在人类缺乏针对西尼罗河病毒的疫苗和抗病毒治疗的情况下，监测是疾病控制的主要形式。随着气候的变化，我们迫切需要更好地了解西尼罗河病毒宿主对环境的反应，以预测和控制这种疾病。这个跨学科的项目结合了计算和实验室工作，旨在：(I)根据在意大利收集的高分辨率环境、昆虫学、鸟类学和人类监测数据预测西尼罗河病毒的传播；(Ii)表征欧洲库蚊动态中的温度依赖性；(Iii)更新欧洲西尼罗河病毒人类传播风险地图。在(I)我们将根据诱捕器数据估计西尼罗河病毒在库蚊中的流行情况，并调查其与2013-2021年在意大利埃米利亚-罗马尼亚每周收集的鸟类学和环境数据的相关性。我们将使用一套统计方法，包括时空贝叶斯混合效应模型、应用于观测气候数据的机器和深度学习，以及在另一项单独分析中应用于过去天气预报数据的方法。这些数据是通过伦敦帝国理工学院(https://www.izsler.it/)，ARPAE(https://www.arpae.it/en))和有限元(https://www.fmach.it/eng).In(II))之间的现有合作提供的。我们将通过进行实验室实验来表征在受控温度条件下的取血行为(例如，性腺营养周期的持续时间、血食和产卵之间的时间以及咬合率)，以填补目前关于温度对欧洲蚊子特征的影响的知识空白。我们将使用从(Ii)获得的关系和从文献综述中获得的其他感兴趣的协变量来开发数学和统计模型，将欧洲疾病预防控制中心(https://www.ecdc.europa.eu/en))报告的欧洲人类感染西尼罗河病毒病例的历史时间序列与空间和时间匹配的气候和环境数据(https://pearselab.github.io/areadata/)，)联系起来，以生成欧洲西尼罗河病毒人类传播风险的更新地图。

West Nile virus (WNV) is an emerging mosquito-borne zoonosis and a growing public health concern in the changing climate. WNV is maintained in an avian-mosquito transmission cycle and occasionally spills over into humans who are dead-end hosts. About 80% of WNV infections in humans are asymptomatic but around 1% develop fatal neuro-invasive disease. Because human-to-human transmission can occur via organ or blood transplant, WNV circulation is closely monitored in Europe during each season for blood safety. In the absence of a vaccine and antiviral treatment against WNV in humans, surveillance is the main form of disease control. As climate changes, we urgently need to better understand WNV hosts' responses to the environment to forecast, and so control, this disease.This interdisciplinary project combines computational and lab-work and aims at (i) forecasting WNV transmission from high-resolution environmental, entomological, ornithological, and human surveillance data collected in Italy; (ii) characterizing temperature dependencies in European Culex mosquito dynamics and (iii) updating a WNV human transmission risk map for Europe.In (i) we will estimate WNV prevalence in Culex mosquitoes from trap data and investigate its association with ornithological and environmental data collected weekly over 2013 - 2021 in Emilia-Romagna (Italy). We will use a suite of statistical approaches including spatiotemporal Bayesian mixed-effects models, machine and deep learning applied to observed climate data and, in a separate analysis, past weather forecast data. These data are provided through an existing collaboration between Imperial College London, IZSLER (https://www.izsler.it/), ARPAE (https://www.arpae.it/en) and FEM (https://www.fmach.it/eng).In (ii) we will fill current knowledge gaps on the effect of temperature on European Culex mosquito traits by conducting lab experiments to characterise blood-feeding behaviour (e.g., the duration of the gonotrophic cycle, the period between a blood meal and oviposition and the biting rate) from controlled temperature conditions.In (iii), we will use the relationships obtained from (ii) and other covariates of interest from a literature review to develop mathematical and statistical models linking observed historical time series of human WNV cases reported in Europe by ECDC (https://www.ecdc.europa.eu/en) with spatially and temporally matched climate and environmental data (https://pearselab.github.io/areadata/), to generate an updated map of WNV human transmission risk across Europe.