The Environmental and Human Factors that Determine Ixodes scapularis-borne Diseases Incidence

决定肩胛硬蜱传播疾病发病率的环境和人为因素

• 批准号: 9758579
• 负责人: Tam Minh Tran
• 金额: $ 4.5万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9758579
• 关键词: Accounting; Address; Adopted; Affect; Anaplasma phagocytophilum; Anaplasmosis; Area; Award; Babesia microti; Babesiosis; Bayesian Analysis; Bayesian learning; Behavior; Big Data; Big Data Methods; Bite; Black-legged Tick; Borrelia burgdorferi; Characteristics; Climate; Communicable Diseases; Communities; Contracts; Dangerousness; Data; Data Analyses; Decision Trees; Disease; Disease Outbreaks; Ecological Change; Ecology; Emerging Communicable Diseases; Entomology; Environment; Environmental Risk Factor; Epidemic; Epidemiological trend; Future; Geography; Goals; Health; Human; Human Characteristics; Incidence; Individual; Infection; Linear Regressions; Link; Lyme Disease; Machine Learning; Maps; Mentorship; Methods; Modeling; Monitor; New York; Pattern; Population; Population Dynamics; Predisposition; Preparation; Prevalence; Process; Public Health; Research Personnel; Research Project Grants; Risk; Risk Assessment; Risk Factors; Role; Sampling; Statistical Methods; System; Techniques; Temperature; Tick-Borne Diseases; Tick-Borne Infections; Ticks; Time; Training; Uncertainty; United States; Variant; Vector-transmitted infectious disease; Vulnerable Populations; anthropogenesis; base; burden of illness; cost effective; decision tree learning; demographics; disorder risk; environmental change; geographic population; granulocyte; human disease; human pathogen; improved; infection risk; learning strategy; model design; novel strategies; pathogen; pathogen exposure; predictive modeling; regression trees; spatial temporal variation; statistics; tick-borne pathogen; trend; vector; vector control; vector transmission

Project Summary Vector-borne diseases (VBDs) are the most common types of emerging and re-emerging infectious diseases in the world. VBD epidemics have been increasing over recent decades, with tickborne diseases having doubled in the last decade in the United States. Despite the increase in public health burden, over 80% of vector-control organizations lack preventative capabilities. Understanding the interplay between the environment, vectors, pathogens, and humans that expedite disease spread remains a challenge. The overarching goal of this project is to identify the key environmental and human drivers that have led to the emergence of VBDs. Current models that predict tickborne disease risk have oversimplified the process by focusing only on the vector, i.e. risk of tick exposure. A human’s risk of infection is not only a function of entomological risk but also of factors inherent to the individual including behavior or characteristics that increase susceptibility to disease. This project proposes a novel approach to tickborne disease prediction by developing a comprehensive model that incorporate pathogen population dynamics and human factors to predict disease risk. This study will investigate several pathogens vectored by the black-legged tick (Ixodes scapularis): Borrelia burgdorferi (Lyme disease), Anaplasma phagocytophilum (human granulocytic anaplasmosis), and Babesia microti (babesiosis). The central hypothesis is that the prediction of tickborne disease risk can be improved by using sophisticated statistical methods to identify environmental drivers that impact pathogen population dynamics while incorporating human demographic characteristics. The hypothesis will be addressed in the following aims: (1) Determine the current and historical population dynamic patterns of pathogens vectored by I. scapularis to predict pathogen distribution; (2) Determine the association between human characteristics and tick-borne disease risk in order to develop an improved spatial disease risk model. This model will allow the identification and quantification of factors that are associated with the emergence of tickborne diseases in New York State, which is geographically advantageous because it is representative of much of the natural environment that ticks encounter in the northeastern US including rapid and recent changes in climate and landscapes. The results of this project will be used to develop a public disease warning system that will use contemporary and future climate forecasts to monitor tick populations and predict potential disease outbreaks for areas with vulnerable populations. With climate forecasts predicting an increase in 2-3°C in temperature by 2100, there is uncertainty in how diseases will shift and a warning system will allow preparation accordingly. At the completion of the proposed research project, the applicant will have acquired the following skillsets through intensive, interdisciplinary mentorship: big data analysis, advanced statistics including Bayesian and machine learning methods, spatial analyses, and risk analysis. This will enable the applicant to succeed as an independent investigator to address the challenges posed by emerging infectious diseases.

项目摘要 病媒传播疾病(VBD)是中国最常见的新发和再发传染病 整个世界。近几十年来，VBD的流行一直在增加，壁虱传播的疾病增加了一倍 在过去的十年里，在美国。尽管公共卫生负担增加，但超过80%的媒介控制 组织缺乏预防能力。了解环境、病媒、 病原体和加速疾病传播的人类仍然是一个挑战。这件事的首要目标是 该项目旨在确定导致VBD出现的关键环境和人类驱动因素。 目前预测扁虱传播疾病风险的模型过于简化了这一过程，只关注 媒介，即扁虱暴露的风险。人类感染的风险不仅是昆虫学风险的函数，而且 指个体固有的因素，包括增加疾病易感性的行为或特征。 该项目提出了一种通过开发一个综合模型来预测壁虱传播疾病的新方法。 它结合了病原体种群动态和人为因素来预测疾病风险。 这项研究将调查几种由黑腿硬蜱传播的病原体：疏螺旋体 伯格多费氏病(莱姆病)、吞噬无浆菌(人粒细胞无浆体病)和巴贝斯虫病 微小巴贝斯虫病。中心假设是壁虱传播疾病风险的预测可以通过以下方式提高 使用复杂的统计方法确定影响病原体种群的环境驱动因素 动态，同时纳入人类人口统计特征。这一假设将在 以下目标：(1)确定病原体的当前和历史种群动态模式 用来预测病原体分布；(2)确定人类特征和 为了开发一种改进的空间疾病风险模型。此型号将允许 识别和量化与新出现的壁虱传播疾病有关的因素 纽约州，这是地理上的优势，因为它代表了许多自然 扁虱在美国东北部遇到的环境，包括气候和最近的快速变化和 风景画。该项目的成果将用于开发一个公共疾病预警系统，该系统将使用 当前和未来的气候预报，以监测扁虱种群并预测潜在的疾病暴发 针对易受伤害人口的地区。气候预报预测，到2019年气温将上升2-3摄氏度 2100年，疾病将如何转移存在不确定性，预警系统将允许进行相应的准备。 在完成拟议的研究项目时，申请者将获得以下技能 通过密集的跨学科指导：大数据分析、包括贝叶斯和 机器学习方法、空间分析和风险分析。这将使申请者能够成功地成为 作为一名独立调查员，应对新出现的传染病带来的挑战。