PDF(Pubmed)
Abstract：
Air pollution has become a significant concern for human health, and its impact on influenza, has been increasingly recognized. This study aims to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza and to confirm a better method for infectious disease surveillance. Spearman correlation coefficient was used to evaluate the correlation between air pollution and the influenza case counts. VIF was used to test for collinearity among selected air pollutants. OLS regression, GWR, and STWR models were fitted to explore the potential spatiotemporal relationship between air pollution and influenza. The R2, the RSS and the AICc were used to evaluate and compare the models. In addition, the DTW and K-medoids algorithms were applied to cluster the county-level time-series coefficients. Compared with the OLS regression and GWR models, STWR model exhibits superior fit especially when the influenza outbreak changes rapidly and is able to more accurately capture the changes in different regions and time periods. We discovered that identical air pollutant factors may yield contrasting impacts on influenza within the same period in different areas of Fuzhou. NO2 and PM10 showed opposite impacts on influenza in the eastern and western areas of Fuzhou during all periods. Additionally, our investigation revealed that the relationship between air pollutant factors and influenza may exhibit temporal variations in certain regions. From 2013 to 2019, the influence coefficient of O3 on influenza epidemic intensity changed from negative to positive in the western region and from positive to negative in the eastern region. STWR model could be a useful method to explore the spatiotemporal heterogeneity of the impacts of air pollution on influenza in geospatial processes. The research findings emphasize the importance of considering spatiotemporal heterogeneity when studying the relationship between air pollution and influenza.
摘要：
空气污染已成为人类健康的重要问题，以及它对流感的影响，越来越得到认可。本研究旨在探讨空气污染对流感影响的时空异质性，并确定更好的传染病监测方法。Spearman相关系数用于评估空气污染与流感病例数之间的相关性。VIF用于测试选定空气污染物之间的共线性。OLS回归，GWR,和STWR模型被拟合以探索空气污染与流感之间潜在的时空关系。R2、RSS和AICc用于评估和比较模型。此外,采用DTW和K-medoids算法对县级时间序列系数进行聚类。与OLS回归和GWR模型相比，特别是当流感爆发迅速变化时，STWR模型表现出优异的拟合性，并且能够更准确地捕获不同地区和时间段的变化。我们发现，在福州不同地区，相同的空气污染物因素可能对同一时期的流感产生不同的影响。NO2和PM10在福州东部和西部各时期对流感的影响相反。此外,我们的调查显示，空气污染物因素与流感之间的关系可能在某些地区表现出时间变化。2013-2019年,O3对流感流行强度的影响系数在西部地区由负向正,在东部地区由正向负。STWR模型可作为探索空气污染对流感影响的时空异质性的有效方法。研究结果强调了在研究空气污染与流感之间的关系时考虑时空异质性的重要性。
