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RESEARCH PRODUCT
Bayesian dynamic modeling of time series of dengue disease case counts
Daniel Adyro Martínez-belloAntonio López-quílezAlexander Torres-prietosubject
Atmospheric ScienceMeteorological ConceptsUrban PopulationEpidemiologyRainPoisson distributionGeographical locationsDengueMathematical and Statistical Techniques0302 clinical medicineStatisticsMedicine and Health Sciences030212 general & internal medicineAtmospheric DynamicsMathematicsMathematical Modelslcsh:Public aspects of medicinePhysicsElectromagnetic RadiationRandom walkDeviance information criterionGeophysicsInfectious DiseasesMean absolute percentage errorPhysical SciencessymbolsSolar RadiationStatistics (Mathematics)Research ArticleGeneralized linear modelConstant coefficientslcsh:Arctic medicine. Tropical medicinelcsh:RC955-962030231 tropical medicineColombiaDisease SurveillanceResearch and Analysis Methods03 medical and health sciencessymbols.namesakeMeteorologyHumansStatistical MethodsCitiesModel selectionPublic Health Environmental and Occupational Healthlcsh:RA1-1270HumidityBayes TheoremMarkov chain Monte CarloSouth AmericaAtmospheric PhysicsRandom WalkEarth SciencesPeople and placesMathematicsForecastingdescription
The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order random walk time-varying coefficients. We applied Markov Chain Monte Carlo simulations for parameter estimation, and deviance information criterion statistic (DIC) for model selection. We assessed the short-term predictive performance of the selected final model, at several time points within the study period using the mean absolute percentage error. The results showed the best model including first-order random walk time-varying coefficients for calendar trend and first-order random walk time-varying coefficients for the meteorological variables. Besides the computational challenges, interpreting the results implies a complete analysis of the time series of dengue with respect to the parameter estimates of the meteorological effects. We found small values of the mean absolute percentage errors at one or two weeks out-of-sample predictions for most prediction points, associated with low volatility periods in the dengue counts. We discuss the advantages and limitations of the dynamic Poisson models for studying the association between time series of dengue disease and meteorological variables. The key conclusion of the study is that dynamic Poisson models account for the dynamic nature of the variables involved in the modeling of time series of dengue disease, producing useful models for decision-making in public health.
year | journal | country | edition | language |
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2017-07-01 | PLOS Neglected Tropical Diseases |