虫媒传染病与气象因子滞后非线性关系的统计模型研究

The increasingly variable climate may change the dynamics and distribution of vector-borne diseases, which are threatening the health of human beings. Biologically speaking, climate is fundamentally associated with the vector-borne disease incidence through its effects on both the mosquito vector and the development of the pathogen inside the mosquito vector. Two aspects of the meteorological effects require special attention, the lag and non-linear characteristics. However, researchers still have a poor understanding of the mechanistic link between climate and vector-borne disease risk. Many studies were conducted to explore the link with inconsistent findings reported, and the nature and extent of the link remains highly controversial. Existing inconsistent findings may be due to the invalid statistical assumptions, which ignore the spatial-temporal variations, high-dimension interactions and multi-parameters..The aim of this study is to better understand the lagged and nonlinear epidemiological association between meteorological factors and vector-borne diseases. Specifically, this project will study the following three problems. First, a model with a new parameterization form will be proposed to directly model the interested biological parameters, and its estimation method will be developed. The parameters are highly crucial for the understanding of epidemiological mechanism. Second, the single-index-model will be applied to model the complex high-dimension interactions across “lag dimension” and “exposure dimension”, and the penalized regression spline will be used for the inference procedure. The proposed model can reveal the micro interactions between multiple exposures over the lagged time. Third, the spatial varying coefficient strategy will be adapted to distributed lag nonlinear model, leading to present the spatial variation for the lag nonlinear pattern..The proposed statistical methods can facilitate the understanding of the mechanistic link between climate and vector-borne disease, thereby helping the vector-borne disease control.

虫媒传染病是我国和全球重要公共卫生问题之一，气象变化对其影响具有滞后性与非线性两个重要特点。近年研究显示气象因子和虫媒传染病的定量关系仍不确切，各项研究间结论存在矛盾。造成该问题的重要原因之一是现有统计方法无法处理时空变异、高维交互和多维参数。.本项目根据气象因子与虫媒传染病关系的特点，发展对“暴露维”与“滞后维”同时进行统计学建模的方法。拟研究如下新模型：①针对重要的生物学参数，提出具有直接解释性的新参数化模型并给出参数估计方法；②单指标模型建模跨越“暴露维”与“滞后维”相互嵌套的复杂高维交互效应，拟采用带惩罚回归样条进行模型推断；③将分布滞后非线性模型拓展到空间变系数的情形并给出层次贝叶斯建模方法与推断技术。这些新方法可处理滞后非线性建模中的时空变异、高维交互和多维参数等问题。本研究对明确气象因子影响虫媒传染病发生和传播的流行病学机制具有重要科学意义，也对完善防控措施具有实用价值。

虫媒传染病是我国和全球重要公共卫生问题之一，气象变化对其影响具有滞后性与非线性两个重要特点。近年研究显示气象因子和虫媒传染病的定量关系仍不确切，各项研究间结论存在矛盾。造成该问题的重要原因之一是现有统计方法无法处理时空变异、高维交互和多维参数。. 本项目以我国西南地区近年来气象数据、空气污染数据、虫媒数据和死因数据为例进行分析，构建了包括多水平模型、分布滞后非线性模型和层次贝叶斯变系数模型在内的多种统计模型，通过R软件实现了算法的具体编程和相关模型的构建，对西南地区气象因子与疟疾、乙脑和登革热等虫媒传染病的作用进行了充分研究。例如，相关研究通过分布滞后非线性模型将“暴露维”与“滞后维”相互嵌套，描述了不同滞后期降雨量之间高维交互效应，在空间变系数情形下研究了降雨量对疟疾发病的影响，首次证明了降雨量在不同滞后期之间存在交互效应;发现分布滞后非线性模型能够更加精确地定量评价乙脑与气象因素的滞后关系，乙脑和温度的非线性关系十分明显，在乙脑的发病和流行过程中，最低温度可能决定着何时产生效应，而最高温度可能决定着有效滞后范围的长度。. 该项目研究得出的方法可处理滞后非线性建模中的时空变异、高维交互和多维参数等问题。同时，项目探索了气象因子与虫媒传染病之间的关系，对明确气象因子影响虫媒传染病发生和传播的流行病学机制具有重要科学意义，也对完善防控措施具有实用价值。