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

西尼罗河病毒（WNV）是一种新兴的蚊媒人畜共患病，在气候变化中日益引起公共卫生关注。西尼罗河病毒维持在禽类-蚊子传播循环中，偶尔会传播给作为终端宿主的人类。人类约80%的西尼罗河病毒感染无症状，但约1%发展为致命的神经侵袭性疾病。由于人与人之间的传播可通过器官或血液移植发生，为了血液安全，欧洲在每个季节都密切监测西尼罗河病毒的传播。在缺乏针对人类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）描述欧洲库蚊动态的温度依赖性；（iii）更新欧洲西尼罗河病毒人间传播风险图。在(i)中，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年至2021年在艾米利亚-罗马涅（意大利）每周收集的鸟类学和环境数据的关系。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，以及在单独的分析中，过去的天气预报数据。这些数据是通过伦敦帝国理工学院、IZSLER （https://www.izsler.it/）、ARPAE （https://www.arpae.it/en）和FEM (https://www.fmach.it/eng).In)之间的现有合作提供的。（ii）我们将通过进行实验室实验来描述吸血行为（例如，淋营养循环的持续时间），填补目前关于温度对欧洲库蚊特征影响的知识空白。进食血液和产卵之间的一段时间，以及在控制温度条件下的咬人率。在（iii）中，我们将使用从（ii）中获得的关系和文献综述中感兴趣的其他协变量来开发数学和统计模型，将ECDC （https://www.ecdc.europa.eu/en）报告的欧洲观察到的人类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）联系起来。. io/areadata/)，生成西尼罗河病毒在整个欧洲人间传播风险的最新地图。

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.