Spatiotemporal modeling of relative risk of dengue disease in Colombia

Martínez-Bello, Daniel Adyro; López‐Quílez, Antonio; Prieto, Alexander Torres

doi:10.1007/s00477-017-1461-5

Cited by 25 publications

(21 citation statements)

References 58 publications

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“…Models with a single level of spatial dependence were considered in Martinez et al . [ 20 ] to analyze dengue incidence data in the city of Bucaramanga at smaller spatial aggregation units (census sections), including covariates obtained from satellite data. However, particular attention must be paid on solving identifiability issues in disease mapping models when covariates are included, because ignoring the spatial or temporal correlation between covariates and the random effects can lead to misleading results due to confounding issues (see for example, Reich et al .…”

Section: Discussionmentioning

confidence: 99%

“…Relative risk estimation of dengue disease has been developed using spatial and spatio-temporal data at several spatial resolutions. For example, spatial modeling of dengue data has been applied to data from Brazil [ 12 ] and Colombia [ 13 ], while spatio-temporal dengue data have been analyzed using relative risk models in Brazil [ 14 – 16 ], Ecuador [ 17 ], Thailand [ 18 ], Colombia [ 19 ] [ 20 ], and Indonesia [ 21 ]. However, most of these analyses did not fully explore the space-time interaction effect model framework.…”

Section: Introductionmentioning

confidence: 99%

“…[ 21 ] while Martínez-Bello et al . [ 20 ] applied models of dengue relative risks with full space-time interaction terms.…”

Section: Introductionmentioning

confidence: 99%

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Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia

et al. 2018

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Risk maps of dengue disease offer to the public health officers a tool to model disease risk in space and time. We analyzed the geographical distribution of relative incidence risk of dengue disease in a high incidence city from Colombia, and its evolution in time during the period January 2009—December 2015, identifying regional effects at different levels of spatial aggregations. Cases of dengue disease were geocoded and spatially allocated to census sectors, and temporally aggregated by epidemiological periods. The census sectors are nested in administrative divisions defined as communes, configuring two levels of spatial aggregation for the dengue cases. Spatio-temporal models including census sector and commune-level spatially structured random effects were fitted to estimate dengue incidence relative risks using the integrated nested Laplace approximation (INLA) technique. The final selected model included two-level spatial random effects, a global structured temporal random effect, and a census sector-level interaction term. Risk maps by epidemiological period and risk profiles by census sector were generated from the modeling process, showing the transmission dynamics of the disease. All the census sectors in the city displayed high risk at some epidemiological period in the outbreak periods. Relative risk estimation of dengue disease using INLA offered a quick and powerful method for parameter estimation and inference.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia

et al. 2018

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“…But it has been under the Bayesian paradigm that this kind of combination has been more prolific. The flexibility of the Bayesian hierarchical models allows for inference over complex models combining several different temporal and spatial structures with relative ease (Martínez-Bello et al 2018). In that way, several methods have been proposed, usually based on a spatiotemporal Poisson model.…”

Section: Introductionmentioning

confidence: 99%

A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks

Amorós

Conesa

López‐Quílez

et al. 2020

Stoch Environ Res Risk Assess

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Rapidly detecting the beginning of influenza outbreaks helps health authorities to reduce their impact. Accounting for the spatial distribution of the data can greatly improve the performance of an outbreak detection method by promptly detecting the first foci of infection. The use of Hidden Markov chains in temporal models has shown to be great tools for classifying the epidemic or endemic state of influenza data, though their use in spatio-temporal models for outbreak detection is scarce.In this work, we present a spatio-temporal Bayesian Markov switching model over the differentiated incidence rates for the rapid detection of influenza outbreaks. This model focuses its attention on the incidence variations to better detect the higher increases of early epidemic rates even when the rates themselves are relatively low. The differentiated rates are modelled by a Gaussian distribution with different mean and variance according to the epidemic or endemic state. A temporal autoregressive term and a spatial conditional autoregressive model are added to capture the spatio-temporal structure of the epidemic mean. The proposed model has been tested over the USA Google Flu Trends database to assess the relevance of the whole structure.

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“…Racloz et al ( 6 ) and Louis et al ( 7 ) reviewed the spatial patterns assessment of dengue risk; specifically for RR estimation of dengue, Ferreira and Schmidt ( 8 ) and Martínez-Bello et al ( 9 ) estimated RR for dengue on a local spatial scale; and Restrepo et al ( 10 ) and Martínez-Bello et al ( 11 ) applied methods for the spatiotemporal assessment of dengue risk. Examples for the spatial patterns assessment of Zika risk run from merely descriptive methods to model-based approaches.…”

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confidence: 99%

Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015–2016

Martínez-Bello¹,

López‐Quílez²,

Prieto³

2019

Emerg. Infect. Dis.

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We jointly estimated relative risk for dengue and Zika virus disease (Zika) in Colombia, establishing the spatial association between them at the department and city levels for October 2015–December 2016. Cases of dengue and Zika were allocated to the 87 municipalities of 1 department and the 293 census sections of 1 city in Colombia. We fitted 8 hierarchical Bayesian Poisson joint models of relative risk for dengue and Zika, including area- and disease-specific random effects accounting for several spatial patterns of disease risk (clustered or uncorrelated heterogeneity) within and between both diseases. Most of the dengue and Zika high-risk municipalities varied in their risk distribution; those for Zika were in the northern part of the department and dengue in the southern to northeastern parts. At city level, spatially clustered patterns of dengue high-risk census sections indicated Zika high-risk areas. This information can be used to inform public health decision making.

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Spatiotemporal modeling of relative risk of dengue disease in Colombia

Cited by 25 publications

References 58 publications

Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia

Two-level resolution of relative risk of dengue disease in a hyperendemic city of Colombia

A spatio-temporal hierarchical Markov switching model for the early detection of influenza outbreaks

Joint Estimation of Relative Risk for Dengue and Zika Infections, Colombia, 2015–2016

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