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哥伦比亚境内寨卡和登革热感染的时空建模。

Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia.

机构信息

Department of Statistics and Operations Research, Faculty of Mathematics, University of Valencia, 46100 Valencia, Spain.

Epidemiologic Monitoring Office, Secretary of Health of the Department of Santander, Cl. 45 11-52 Bucaramanga, Colombia.

出版信息

Int J Environ Res Public Health. 2018 Jun 30;15(7):1376. doi: 10.3390/ijerph15071376.

Abstract

The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015⁻2016 ZVD outbreak. We apply the integrated nested Laplace approximation (INLA) for parameter estimation, using the epidemiological week (EW) as a time measure. At the departmental level, the best model showed that the dengue or ZVD risk in one municipality was highly associated with risk in the same municipality during the preceding EWs, while at the city level, the final model selected established that the high risk of dengue or ZVD in one census sector was highly associated not only with its neighboring census sectors in the same EW, but also with its neighboring sectors in the preceding EW. The spatio-temporal models provided smoothed risk estimates, credible risk intervals, and estimation of the probability of high risk of dengue and ZVD by area and time period. We explore the intricacies of the modeling process and interpretation of the results, advocating for the use of spatio-temporal models of the relative risk of dengue and ZVD in order to generate highly valuable epidemiological information for public health decision making.

摘要

本研究旨在利用时空交互效应模型,估算哥伦比亚一个省和一个城市在 2015-2016 年寨卡病毒病(ZVD)暴发期间的寨卡病毒病(ZVD)和登革热的平行相对风险。我们采用整合嵌套拉普拉斯近似(INLA)进行参数估计,以流行病学周(EW)作为时间度量。在省级水平上,最佳模型表明,一个市的登革热或 ZVD 风险与其前几个 EW 中同一市的风险高度相关,而在市级水平上,最终选择的模型表明,一个普查区的高登革热或 ZVD 风险不仅与其同一 EW 中的邻近普查区高度相关,而且与其前一个 EW 中的邻近区也高度相关。时空模型提供了平滑的风险估计、可信风险区间,并按地区和时间段估计了登革热和 ZVD 高风险的概率。我们探讨了建模过程和结果解释的复杂性,主张使用登革热和 ZVD 相对风险的时空模型,以便为公共卫生决策生成非常有价值的流行病学信息。

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