Models for improving surveillance of environmentally-mediated infectious diseases

描述（由申请人提供）：血吸虫病与许多其他被忽视的热带病一样，与动态的气候、生态、水文等环境现象密切相关，为公共卫生决策提供了重要机会。由于疾病的持续和形成高度依赖于环境现象，因此空间和时间环境数据集有可能为公共卫生行动提供信息，例如在何处和何时重点开展监测工作。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，用于优化中国四川省存在环境异质性的血吸虫病监测。目前，中国血吸虫监测的分析模型存在严重的局限性，包括对环境介导的生物体表现不佳的简单化、各向同性空间函数，以及对环境过程的粗糙现象学表征。本方案的具体目标是：1)构建世界一流的日本血吸虫流行病学数据集，将监测和研究数据整合成一个有凝聚力的纵向数据库；2)利用新颖的统计和数学方法，包括空间显式、图论模型和允许瞬态耦合的时间序列方法，定量地确定不同尺度的多种环境驱动因素对血吸虫动态结果的影响；3)利用Aim 2开发的模型优化四川省血吸虫病监测活动，评估使用历史和当代数据的预测结果。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，并改进对监测点和调查时间的选择。所产生的技术将推广到其他系统，在面对环境变化时可将其应用于决策支持。该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个富有成效的独立调查员。

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.