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: 10018461
• 负责人: Tam Minh Tran
• 金额: $ 3.82万
• 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 2021-05-17
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10018461
• 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; 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; machine learning method; model design; novel strategies; pathogen; pathogen exposure; predictive modeling; regression trees; spatial temporal variation; statistics; tick bite; 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.

项目概要 媒介传播疾病 (VBD) 是最常见的新发和再发传染病类型 近几十年来，VBD 流行病不断增加，蜱传疾病增加了一倍。 在过去十年中，尽管美国公共卫生负担有所增加，但超过 80% 的病媒控制仍然有效。 组织缺乏了解环境、媒介和环境之间的相互作用。 加速疾病传播的病原体和人类仍然是一个挑战。 该项目旨在确定导致 VBD 出现的关键环境和人类驱动因素。 目前预测蜱传疾病风险的模型过于简化了过程，仅关注 媒介，即人类感染蜱的风险不仅是昆虫学风险的函数，而且还与昆虫学风险有关。 个体固有的因素，包括增加疾病易感性的行为或特征。 该项目通过开发综合模型提出了一种蜱传疾病预测的新方法 结合病原体种群动态和人为因素来预测疾病风险。 本研究将调查黑腿蜱（肩突硬蜱）传播的几种病原体：疏螺旋体 伯氏疏螺旋体（莱姆病）、嗜吞噬细胞无形体（人粒细胞无形体病）和巴贝虫 核心假设是蜱传疾病风险的预测可以通过以下方式改进。 使用复杂的统计方法来识别影响病原体种群的环境驱动因素 该假设将在纳入人类人口统计特征的同时得到解决。 以下目标：（1）确定当前和历史的病原体种群动态模式 I.肩胛肌预测病原体分布；（2）确定人体特征与 蜱传疾病风险，以开发改进的空间疾病风险模型。 与新州蜱传疾病出现相关的因素的识别和量化 约克州地理位置优越，因为它代表了许多自然景观 美国东北部遭遇的环境，包括气候和近期的快速变化 该项目的结果将用于开发一个公共疾病预警系统，该系统将使用 当代和未来的气候预测，以监测蜱虫种群并预测潜在的疾病爆发 气候预测预计气温将上升 2-3°C。 2100年，疾病将如何转变存在不确定性，预警系统将允许相应的准备。 完成拟议的研究项目后，申请人将获得以下技能 通过密集的、跨学科的指导：大数据分析、高级统计，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人取得成功。 独立调查员应对新出现的传染病带来的挑战。