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: 8415962
• 负责人: Justin V Remais
• 金额: $ 12.85万
• 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/8415962
• 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 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(申请人提供)：血吸虫病与许多其他被忽视的热带疾病一样，与动态的气候、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。由于疾病的持久性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，例如在哪里和何时集中监测工作。中国，这一应用程序为申请者提供了先进的数值和统计方法的多种模式的培训，应用于四川省存在环境异质性的血吸虫病监测的优化。目前，中国对血吸虫寄生虫的监测是由具有关键局限性的分析模型指导的，包括对环境中介生物表现不佳的简单、各向同性的空间功能，以及对环境过程的粗略现象学表示。该建议的具体目标是：1)汇集世界级的日本血吸虫病流行病学数据集，将监测和研究数据结合到一个连贯的纵向数据库中；2)利用新颖的统计和数学方法，包括空间显式、图论模型和允许瞬时耦合的时间序列方法，对不同规模的多种环境驱动因素对血吸虫病动态结果的影响进行定量归类；以及3)利用目标2开发的模型，利用历史和当代数据评估预测，优化四川的血吸虫病监测活动。本申请中提出的职业发展和研究活动将导致对血吸虫病的环境驱动因素进行更严格的量化，对风险的空间和时间维度进行更准确的建模，并改进监测地点的选择和调查时机。由此产生的技术将推广到其他系统中使用，在这些系统中，它们可以应用于面对环境变化的决策支持。这项研究建立在候选人的技能基础上，利用现有的数据和知识来支持他过渡到一名多产的独立调查员。 公共卫生研究：众所周知，像血吸虫这样的人类寄生虫对环境因素高度敏感。了解这些寄生虫对温度、降雨量和植被变化的反应可以用来为公共卫生决策提供信息，例如在哪里以及何时集中监测疾病暴发。这项拟议的研究将首次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。