Surveillance and Modeling of West Nile Virus Risk in California

描述（由申请人提供）：西尼罗河病毒自1999年引入美国以来一直是公共卫生工作的焦点。感染西尼罗河病毒的人可能会出现一系列症状，从流感样发热性疾病到神经侵入性疾病（通常称为西尼罗河神经侵入性疾病）。在接触西尼罗河病毒的人群中，西尼罗河病毒是一种严重的，有时甚至是致命的疾病。2012年，美国经历了一个流行年，报告了5245例西尼罗河病毒病例，其中51%为西尼罗河病毒病例，估计发病率为每10万人1.67例。WNND病例的估计死亡率为3-15%，突出了对公共卫生的影响。由于目前没有针对人类的西尼罗河病毒疫苗，因此可能的最佳预防措施是在监测病毒活动的指导下控制蚊虫媒介种群。在加州哨点鸡群中，蚊虫感染流行度和丰度以及死鸟感染流行度是蚊虫控制机构为降低人类传播风险而采取的监测方法。在本项目中，将对这些方法进行分析，以解决使用时空建模可以在不同生态环境中准确预测人类感染西尼罗河病毒风险的总体假设。从拟议项目中获得的信息旨在供公共卫生机构用于影响监测战略，以便更有效地发现病毒活动并调整控制工作，以防止在风险最大的地点和时间向人群传播。2004-2011年间，来自加州四个县的监测方法数据将与气候和人口数据一起用于检验整个假设。将使用混合效应泊松回归模型来估计每种监测方法最能预测人类WNND病例的提前时间和距离。利用监测措施、人口密度、气候和蚊虫叮咬偏好等多种生态和种群预测变量，构建预测泊松回归模型，以确定人类种群风险最高的时间和地点。建立这个模型的目的是让全国各地的蚊子控制机构使用它来指导控制工作。此外，将建立一个概率统计模型，以表征最有利于西尼罗河病毒向人类传播的气候和人口因素的不同组合。

DESCRIPTION (provided by applicant): West Nile virus has been a focus of public health efforts in the United States since its introduction in 1999. Humans infected with WNV may experience a range of symptoms, from flu-like febrile illness to neuroinvasive disease, often termed West Nile Neuroinvasive disease (WNND). WNND is a serious, and at times fatal, condition in populations exposed to WNV. In 2012, the US has experienced an epidemic year with 5,245 reported cases of WNV, 51% of which were WNND cases, and an estimated incidence of 1.67 per 100,000 people. WNND cases have an estimated fatality rate of 3-15% highlighting the impact on public health. Because there is currently no vaccine against WNV for humans, the best preventative measure possible is control of the mosquito vector population guided by the surveillance of virus activity. In California sentinel chicken flocks, mosquito infection prevalence and abundance, and the infection prevalence in dead birds are the surveillance methods used by mosquito control agencies to target control efforts to reduce the risk of transmission to humans. For the proposed project, these methods will be analyzed to address the overall hypothesis that the risk of WNV infection for humans can be accurately predicted over diverse ecological contexts using spatio-temporal modeling. The information gained from the proposed project is intended to be used by public health agencies in influencing surveillance strategies to more effectively detect virus activity and tailor control efforts to preent transmission to the human population where and when the risk is greatest. Data on the surveillance methods from four California counties for the years 2004-2011 will be used along with climatic and population data to test the overall hypothesis. A mixed-effects Poisson regression model will be used to estimate the lead-time and distance at which each of the surveillance methods is most predictive of human WNND cases. In order to determine the time and place that the human population is at highest risk, a predictive Poisson regression model will be constructed using various ecological and population-based predictor variables, such as surveillance measures, human population density, climate, and mosquito-host biting preference. This model will be constructed with the intent that it be used by mosquito control agencies across the nation to guide control efforts. In addition, a probabilistic statistical model will be uilt in order to characterize the different combinations of climatic and population factors which are most conducive to transmission of WNV to humans.