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.

项目摘要 媒介传播疾病（VBD）是最常见的新兴和重新出现感染性疾病的类型 世界。近几十年来，VBD情节一直在增加，tick疾病增加了一倍 在过去的十年中。尽管公共卫生燃烧增加，但超过80％的向量控制 组织缺乏预防能力。了解环境之间的相互作用，向量， 病原体和加快疾病的人类仍然是一个挑战。总体目标 项目是确定导致VBD出现的关键环境和人类驱动因素。 当前预测tick疾病风险的模型通过仅专注于 向量，即tick虫暴露的风险。人类感染的风险不仅是昆虫学风险的功能，而且是 继承给个人的因素，包括增加对疾病易感性的行为或特征。 该项目提出一种通过开发综合模型来提出一种新颖的方法来预测滴答疾病的预测 融合了病原体种群动态和人为因素以预测疾病风险。 这项研究将研究黑腿tick（ixodes scapularis）载体的几种病原体：Borrelia 伯氏菌（莱姆病），纳普拉氏菌吞噬（人类粒细胞无质膜）和贝贝西亚 Microti（Babesiosis）。中心假设是，可以改善tick虫疾病风险的预测 使用复杂的统计方法来识别影响病原体种群的环境驱动力 动力学同时融合了人口特征。该假设将在 以下目的：（1）确定由病原体的当前和历史种群动态模式。 I.肩cap骨预测病原体分布； （2）确定人类特征与 tick传播的疾病风险以开发改进的空间疾病风险模型。该模型将允许 识别和量化与新的滴答性疾病出现有关的因素 约克州，在地理上有利，因为它代表了许多自然 在美国东北部遇到的环境，包括气候的快速和最新变化 风景。该项目的结果将用于开发一种公共疾病警告系统，该警告系统将使用 当代和未来的气候森林，以监测壁虱种群并预测潜在的疾病暴发 对于脆弱人群的地区。随着气候预测，预测温度在2-3°C的情况下会增加 2100年，疾病将如何转移和警告系统允许相应准备。 拟议的研究项目完成时，申请人将获得以下技能 通过密集的，跨学科的心态：大数据分析，包括贝叶斯和包括的高级统计数据 机器学习方法，空间分析和风险分析。这将使应用程序能够成功 独立研究者解决了新兴的传染病所带来的挑战。