Bayesian dynamic modeling of time series of dengue disease case counts.
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 evalua...
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ftdoajarticles:oai:doaj.org/article:bdf84f07422e4f3d9bcc236cecbd4d3c 2023-05-15T15:14:52+02:00 Bayesian dynamic modeling of time series of dengue disease case counts. Daniel Adyro Martínez-Bello Antonio López-Quílez Alexander Torres-Prieto 2017-07-01T00:00:00Z https://doi.org/10.1371/journal.pntd.0005696 https://doaj.org/article/bdf84f07422e4f3d9bcc236cecbd4d3c EN eng Public Library of Science (PLoS) http://europepmc.org/articles/PMC5510904?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0005696 https://doaj.org/article/bdf84f07422e4f3d9bcc236cecbd4d3c PLoS Neglected Tropical Diseases, Vol 11, Iss 7, p e0005696 (2017) Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 article 2017 ftdoajarticles https://doi.org/10.1371/journal.pntd.0005696 2022-12-31T06:25:16Z 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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic PLOS Neglected Tropical Diseases 11 7 e0005696 |
institution |
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Directory of Open Access Journals: DOAJ Articles |
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language |
English |
topic |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
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Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Daniel Adyro Martínez-Bello Antonio López-Quílez Alexander Torres-Prieto Bayesian dynamic modeling of time series of dengue disease case counts. |
topic_facet |
Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Daniel Adyro Martínez-Bello Antonio López-Quílez Alexander Torres-Prieto |
author_facet |
Daniel Adyro Martínez-Bello Antonio López-Quílez Alexander Torres-Prieto |
author_sort |
Daniel Adyro Martínez-Bello |
title |
Bayesian dynamic modeling of time series of dengue disease case counts. |
title_short |
Bayesian dynamic modeling of time series of dengue disease case counts. |
title_full |
Bayesian dynamic modeling of time series of dengue disease case counts. |
title_fullStr |
Bayesian dynamic modeling of time series of dengue disease case counts. |
title_full_unstemmed |
Bayesian dynamic modeling of time series of dengue disease case counts. |
title_sort |
bayesian dynamic modeling of time series of dengue disease case counts. |
publisher |
Public Library of Science (PLoS) |
publishDate |
2017 |
url |
https://doi.org/10.1371/journal.pntd.0005696 https://doaj.org/article/bdf84f07422e4f3d9bcc236cecbd4d3c |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
PLoS Neglected Tropical Diseases, Vol 11, Iss 7, p e0005696 (2017) |
op_relation |
http://europepmc.org/articles/PMC5510904?pdf=render https://doaj.org/toc/1935-2727 https://doaj.org/toc/1935-2735 1935-2727 1935-2735 doi:10.1371/journal.pntd.0005696 https://doaj.org/article/bdf84f07422e4f3d9bcc236cecbd4d3c |
op_doi |
https://doi.org/10.1371/journal.pntd.0005696 |
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PLOS Neglected Tropical Diseases |
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11 |
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7 |
container_start_page |
e0005696 |
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