整合时间 disaggregation（时间分解）与分布滞后非线性模型，用于结合高分辨率环境暴露的贝叶斯时空疾病制图
Integrating Temporal Disaggregation and Distributed Lag Nonlinear Models for Bayesian Spatio-Temporal Disease Mapping with High-Resolution Environmental Exposures
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中文总结 AI 辅助
该研究提出整合时间分解与分布滞后非线性模型的贝叶斯时空框架，结合高分辨率环境暴露开展疟疾制图，在莫桑比克数据上验证其提升了预测精度，可用于研究环境延迟效应并扩展至多类疾病。
中文摘要 AI 辅助
环境条件是疟疾传播的主要驱动因素，但流行病学分析常受限于：以粗时间尺度报告的健康结局与更精细分辨率的环境暴露之间存在时间错位。传统方法会聚合环境数据以匹配健康结局，可能掩盖延迟和非线性关系。我们提出一种贝叶斯时空框架，通过时间分解将潜在的每日疾病过程与观测到的月度疟疾计数关联，以此解决该局限。该框架在统一层次模型中整合了用于气候效应的分布滞后非线性模型、时空随机效应及干预协变量。此方法应用于2017-2024年莫桑比克161个地区的疟疾监测数据，整合了温度、降水量、相对湿度、植被、海拔及疟疾干预措施。与传统月度模型相比，所提框架在利用环境数据时间分辨率的同时，提升了预测精度与不确定性量化能力。估计的关系显示气候变异性与疟疾发病率之间存在非线性关联，包括最优温度范围、植被正异常时风险增加、非线性降水效应。通过避免环境暴露的时间聚合，该框架为从常规监测数据中研究延迟环境效应提供了灵活方法，且可扩展至其他具有时间分辨率不匹配问题的环境敏感性疾病。
英文摘要
Environmental conditions are major drivers of malaria transmission, but epidemiological analyses are often constrained by temporal misalignment between health outcomes reported at coarse time scales and environmental exposures available at finer resolutions. Conventional approaches aggregate environmental data to match health outcomes, potentially obscuring delayed and nonlinear relationships. We propose a Bayesian spatio-temporal framework that addresses this limitation through a latent daily disease process linked to observed monthly malaria counts by temporal disaggregation. The framework integrates distributed lag nonlinear models for climatic effects, spatio-temporal random effects, and intervention covariates within a unified hierarchical model. The methodology was applied to malaria surveillance data from 161 districts in Mozambique between 2017 and 2024, integrating temperature, precipitation, relative humidity, vegetation, elevation, and malaria interventions. Compared with a conventional monthly model, the proposed framework improved predictive accuracy and uncertainty quantification while exploiting the temporal resolution of environmental data. Estimated relationships showed nonlinear associations between climatic variability and malaria incidence, including an optimal temperature range, increasing risk with positive vegetation anomalies, and nonlinear precipitation effects. By avoiding temporal aggregation of environmental exposures, the framework provides a flexible approach for investigating delayed environmental effects from routine surveillance data and can be extended to other environmentally sensitive diseases with mismatched temporal resolutions.
