Abstract
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.
Highlights
Dengue fever (DF) affects more than 100 million people every year and is one of the most important mosquito-borne diseases in the world [1]
These serotypes can induce a range of symptoms, including the most dangerous stage: dengue haemorrhagic fever (DHF) which is characterised by circulatory collapse and death [4, 7]
The titles and abstracts of articles found through keyword searches were screened first by one author and the papers identified were evaluated through reading the full text and selected according to the inclusion criteria
Summary
Dengue fever (DF) affects more than 100 million people every year and is one of the most important mosquito-borne diseases in the world [1]. The dengue virus is transmitted by a female mosquito of the Aedes aegypti species [3, 4] and consists of four serotypes: DENV-1, DENV-2, DENV-3 and DENV-4 [2, 5, 6] These serotypes can induce a range of symptoms, including the most dangerous stage: dengue haemorrhagic fever (DHF) which is characterised by circulatory collapse and death [4, 7]. The major appeal of these approaches is in considering uncertainty in the predictions or estimates and the straightforward incorporation of spatial and temporal structure as prior distributions [11]. This approach allows one to take into account a much wider class of conceptual models than non-Bayesian approaches [12]. The priors can be used to incorporate information from preceding studies [9]
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