Models for improving surveillance of environmentally-mediated infectious diseases

改善环境介导的传染病监测的模型

• 批准号: 8604361
• 负责人: Justin V Remais
• 金额: $ 12.83万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-02-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8604361
• 关键词: Accounting; China; Communicable Diseases; Coupled; Coupling; Data; Data Set; Decision Making; Dimensions; Disease; Disease Outbreaks; Disease Outcome; Distant; Environment; Environmental Risk Factor; Epidemiology; Foundations; Graph; Heterogeneity; Human; Infection; Knowledge; Lead; Life; Measures; Mediating; Methods; Metric; Modeling; Organism; Outcome; Parasites; Process; Province; Public Health; Research; Research Activity; Research Personnel; Risk; Schistosoma; Schistosoma japonicum; Schistosomiasis; Sentinel; Series; Services; Site; Staging; Statistical Methods; Surveys; System; Techniques; Temperature; Testing; Theoretical model; Time; Training; Tropical Disease; Work; base; career development; disease transmission; environmental change; epidemiological model; improved; innovation; longitudinal database; mathematical methods; mathematical model; neglect; network models; novel; prevent; research and development; skills; theories; transmission process; vector

DESCRIPTION (provided by applicant): Schistosomiasis, like many other neglected tropical diseases, has strong associations with dynamic climactic, ecological, hydrological and other environmental phenomena, raising an important opportunity for public health decision-making. Because disease persistence and establishment are highly dependent on environmental phenomena, spatial and temporal environmental datasets have the potential to inform public health actions, such as where and when to focus surveillance efforts. This application provides multiple modes of training to the applicant in advanced numerical and statistical methods, applied to the optimization of schistosomiasis surveillance in the presence of environmental heterogeneity in Sichuan Province, China. Surveillance for Schistosoma parasites in China is currently guided by analytical models with key limitations, including simplistic, isotropic spatial functions that perform poorly for environmentally-mediated organisms, and crude phenomenological representations of environmental processes. The specific aims of this proposal are: 1) to assemble a world-class Schistosoma japonicum epidemiological dataset, combining surveillance and research data into a cohesive, longitudinal database; 2) to quantitatively attribute the effects of multiple environmental drivers at varying scales on dynamic Schistosoma outcomes using novel statistical and mathematical approaches, including spatially explicit, graph-theoretic models and time-series approaches allowing for transient coupling; and 3) to optimize Schistosoma surveillance campaigns in Sichuan using models developed in Aim 2, evaluating predictions using historical and contemporary data. The career development and research activities proposed in this application will lead to a more rigorous quantification of environmental drivers of schistosomiasis, more accurate modeling of the spatial and temporal dimensions of risk, and improved selection of surveillance sites and survey timing. The resulting techniques will be generalized for use in other systems where they can be applied to decision-making support in the face of environmental change. The research builds on the candidate's foundation of skills, leveraging existing data and knowledge to support his transition to a productive independent investigator. PUBLIC HEALTH RELEVENCE: Human parasites like schistosomes are known to be highly sensitive to environmental factors. Understanding how these parasites respond to changes in temperature, rainfall and vegetation can be used to inform public health decision-making, such as where and when to focus surveillance for disease outbreaks. The proposed study will be the first to investigate how environmental information can be used to improve public health activities to prevent new parasite infections.

描述（由申请人提供）：血吸虫病与许多其他被忽视的热带疾病一样，与动态气候、生态、水文和其他环境现象密切相关，为公共卫生决策提供了重要机会。由于疾病的持续和建立高度依赖于环境现象，空间和时间环境数据集有可能为公共卫生行动提供信息，例如在何处和何时集中监测工作。该应用程序为申请人提供了先进的数值和统计方法的多种培训模式，适用于在中国四川省存在环境异质性的情况下优化血吸虫病监测。中国血吸虫寄生虫的监测目前由具有关键局限性的分析模型指导，包括对环境介导的生物体表现不佳的简单化、各向同性的空间函数，以及环境过程的粗糙现象学表示。该建议的具体目标是：1）汇集世界级的日本血吸虫流行病学数据集，将监测和研究数据结合成一个连贯的纵向数据库; 2）使用新的统计和数学方法，包括空间显式方法，在不同尺度上对多种环境驱动因素对血吸虫动态结果的影响进行定量归因，图理论模型和时间序列方法，允许瞬态耦合;和3）使用目标2中开发的模型优化四川血吸虫病监测活动，使用历史和当代数据评估预测。本申请中提出的职业发展和研究活动将导致血吸虫病的环境驱动因素的更严格的量化，风险的空间和时间维度的更准确的建模，并改善监测点和调查时间的选择。由此产生的技术将被推广用于其他系统，在那里它们可以被应用到面对环境变化的决策支持。该研究建立在候选人的技能基础之上，利用现有的数据和知识来支持他向富有成效的独立调查员的过渡。 公共卫生救济：人类寄生虫，如寄生虫，对环境因素高度敏感。了解这些寄生虫如何对温度，降雨和植被的变化做出反应，可以用于为公共卫生决策提供信息，例如在何处以及何时集中监测疾病爆发。这项拟议的研究将是第一个调查如何利用环境信息来改善公共卫生活动，以防止新的寄生虫感染的研究。