Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

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.

Spatiotemporal dynamics, risk areas and social determinants of dengue in Northeastern Brazil, 2014–2017: an ecological study

“…5 ). Our results are consistent with previous findings that dengue is spatially correlated with clusters [ 36 – 39 ]. It demonstrates the wide spatial spread of dengue and the exposure of a significant portion of the population in the Northeast Brazil.…”

“…Maccormack-Gelles et al [ 18 ] reported that, in Fortaleza (Brazilian Northeast), a USD 178.58 (USD 1 = BRL 5.60) increase in average annual bairro household income was associated with reduced dengue incidence by more than 10% [ 18 ]. In Brazil, inadequate garbage disposal and income were the most significant factors related to the incidence of dengue [ 42 , 48 ], and lower socio-economic status (within a slum society) increased the risk of dengue [ 36 , 49 ].…”

Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico

“…More specifically, the data-set captured repeated measurements over the same regions, and observations were not independent because of spill over effects from neighbouring regions, therefore we needed to implement an appropriate statistical design to control for both temporal and spatial pseudo replication (lack of independence). We could deal with this in two ways, (1) either using a generalized linear mixed model (GLMM) approach, relaxing the assumption of independence and estimating the spatial/temporal correlation between residuals, or (2) model the spatial and temporal dependence in the systematic part of the model [ 54 ]. We opted to use a Generalized Additive Model (GAM) using R’s Mgcv statistical package because of its versatility and ability to fit complex models that would converge even with low numbers of observations and could capture potential complex non-linear relationships.…”

Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

“…This case study is described in detail below. Although spatial patterns in dengue fever have been the subject of considerable research effort [6], there appears to have been little research so far into modelling time-to-discharge for dengue hospitalisations. Hospitalisation of dengue fever patients is expensive [7] and understanding the geographic pattern of hospitalisation, duration of hospitalisation, influential factors affecting hospitalisation, and the influence of the severity of presentation at the hospital, is critical for hospital management.…”