Landscape Ecology and Mapping of Ehrlichial Pathogens

描述(申请人提供)：在过去的几十年里，许多壁虱物种的地理范围扩大了，并出现了几种新的壁虱传播的人畜共患病。其中两种疾病是由查菲埃利希氏菌引起的人类单核细胞增多性埃立克体病(HME)和由吞噬无浆菌引起的人类粒细胞无形体病(HGA)。这些病原体和其他新出现病原体的地图可以在评估区域公共卫生方面发挥重要作用。然而，关于病媒或病原体存在的可靠数据通常只在孤立的样本地点可用。这项拟议的研究是对之前NIH资助的一项研究的扩展，在该研究中，有关鹿的血清学、气候、土地覆盖、土壤和鹿密度的信息被整合到一个区域地理空间数据库中，并用于绘制查菲棘球绦虫和吞噬细胞性埃希氏菌的分布图。我们的主要目标是应用景观生态学的概念和方法，提高我们对病媒和病原体传播的环境约束的理解，并开发更有效的方法来绘制病原体分布和疾病风险图。为了实现这一目标，我们将(1)比较不同生态变量对病原体分布在多个空间尺度上的影响；(2)将土地覆盖异质性的空间指数作为病原体分布的预测因子；(3)将生态模型与地统计学方法相结合，以提高预测精度并表征空间不确定性。我们预计，这项工作将产生一个增强的概念模型，说明查菲埃希氏菌和吞噬细胞性埃希氏菌的范围如何受到在不同空间尺度上运行的多种生态因素的限制，以及这些病原体的地理范围的更准确的地图。这些地图最终将为评估人类感染风险提供一个框架。此外，我们开发的方法将有助于开发其他媒介、病原体和疾病的预测性地图。

DESCRIPTION (provided by applicant): The geographic ranges of many tick species have expanded over the past several decades, and several new tickborne zoonoses have emerged. Two of these diseases are human monocytotropic ehrlichiosis (HME), caused by Ehrlichia chaffeensis; and human granulocytic anaplasmosis (HGA) caused by Anaplasma phagocytophilum. Maps of these and other emerging pathogens can play an important role in the assessment of regional public health. However, reliable data on the presence of vectors or pathogens is typically only available at isolated sample locations. The proposed research expands on a previous NIH-funded study in which information on serology of deer, climate, land cover, soils, and deer density were integrated into a regional geospatial database and used to map the distributions of E. chaffeensis and A. phagocytophilum. Our main objective is to apply concepts and methods from landscape ecology to improve our understanding of the environmental constraints on vector and pathogen spread, and to develop more effective methods for mapping pathogen distributions and disease risk. To accomplish this objective, we will (1) contrast the influences of various ecological variables on pathogen distributions across multiple spatial scales; (2) incorporate spatial indices of land cover heterogeneity as predictors of pathogen distributions; and (3) integrate ecological models with geostatistical methods to improve prediction accuracy and characterize spatial uncertainty. We expect that this work will result in an enhanced conceptual model of how the ranges of E. chaffeensis and A. phagocytophilum are constrained by multiple ecological factors operating at different spatial scales, along with more accurate maps of the geographic ranges of these pathogens. These maps will ultimately provide a framework for assessing infection risks in human populations. In addition, the methodologies that we develop will be useful for developing predictive maps of other vectors, pathogens, and diseases.