Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review.
Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review.
复制标题
DOI：
10.1017/s0950268818002807
复制
发表时间：
2018-10-29
影响因子：
4.2
通讯作者：
Mengersen K
中科院分区：
文献类型：
--
作者：
Aswi A;Cramb SM;Moraga P;Mengersen K
登革热 (DF) 是世界上最致残的蚊媒疾病之一，有多种方法可以对其空间和时间动态进行建模。本文旨在识别和比较应用于 DF 的不同空间和时空贝叶斯建模方法，并检查据报道与 DF 风险相关的有影响力的协变量。 2017 年 12 月，使用 Web of Science、Scopus、ScienceDirect、PubMed、ProQuest 和 Medline（通过 Ebscohost）电子数据库进行了系统检索。检索仅限于 2000 年 1 月至 2017 年 11 月以英文发表的参考期刊文章。31 篇文章符合纳入标准。使用修改后的质量评估工具，各项研究的中位质量得分为 14/16。登革热建模最流行的贝叶斯统计方法是广义线性混合模型，其空间随机效应由条件自回归先验描述。有限数量的研究包括时空随机效应。研究表明，温度和降水通常会影响登革热的风险。开发时空随机效应模型、考虑其他先验、使用涵盖较长时间段的数据集并研究其他协变量将有助于更好地理解和控制 DF 传输。
Dengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.