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Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia

Author

Listed:
  • Daniel Adyro Martínez-Bello

    (Department of Statistics and Operations Research, Faculty of Mathematics, University of Valencia, 46100 Valencia, Spain)

  • Antonio López-Quílez

    (Department of Statistics and Operations Research, Faculty of Mathematics, University of Valencia, 46100 Valencia, Spain)

  • Alexander Torres Prieto

    (Epidemiologic Monitoring Office, Secretary of Health of the Department of Santander, Cl. 45 11-52 Bucaramanga, Colombia)

Abstract

The aim of this study is to estimate the parallel relative risk of Zika virus disease (ZVD) and dengue using spatio-temporal interaction effects models for one department and one city of Colombia during the 2015–2016 ZVD outbreak. We apply the integrated nested Laplace approximation (INLA) for parameter estimation, using the epidemiological week (EW) as a time measure. At the departmental level, the best model showed that the dengue or ZVD risk in one municipality was highly associated with risk in the same municipality during the preceding EWs, while at the city level, the final model selected established that the high risk of dengue or ZVD in one census sector was highly associated not only with its neighboring census sectors in the same EW, but also with its neighboring sectors in the preceding EW. The spatio-temporal models provided smoothed risk estimates, credible risk intervals, and estimation of the probability of high risk of dengue and ZVD by area and time period. We explore the intricacies of the modeling process and interpretation of the results, advocating for the use of spatio-temporal models of the relative risk of dengue and ZVD in order to generate highly valuable epidemiological information for public health decision making.

Suggested Citation

  • Daniel Adyro Martínez-Bello & Antonio López-Quílez & Alexander Torres Prieto, 2018. "Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia," IJERPH, MDPI, vol. 15(7), pages 1-18, June.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1376-:d:155424
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    References listed on IDEAS

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    1. Mohamed F. Sallam & Chelsea Fizer & Andrew N. Pilant & Pai-Yei Whung, 2017. "Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping," IJERPH, MDPI, vol. 14(10), pages 1-15, October.
    2. Sair Arboleda & Nicolas Jaramillo-O. & A. Townsend Peterson, 2009. "Mapping Environmental Dimensions of Dengue Fever Transmission Risk in the Aburrá Valley, Colombia," IJERPH, MDPI, vol. 6(12), pages 1-16, December.
    3. Anna M. Stewart-Ibarra & Anita Hargrave & Avriel Diaz & Aileen Kenneson & David Madden & Moory M. Romero & Juan Pablo Molina & David Macias Saltos, 2017. "Psychological Distress and Zika, Dengue and Chikungunya Symptoms Following the 2016 Earthquake in Bahía de Caráquez, Ecuador," IJERPH, MDPI, vol. 14(12), pages 1-16, December.
    4. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    5. Rachel Lowe & Christovam Barcellos & Patrícia Brasil & Oswaldo G. Cruz & Nildimar Alves Honório & Hannah Kuper & Marilia Sá Carvalho, 2018. "The Zika Virus Epidemic in Brazil: From Discovery to Future Implications," IJERPH, MDPI, vol. 15(1), pages 1-18, January.
    6. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
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    Cited by:

    1. Chao Song & Yaode Wang & Xiu Yang & Yili Yang & Zhangying Tang & Xiuli Wang & Jay Pan, 2020. "Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest Ch," IJERPH, MDPI, vol. 17(16), pages 1-23, August.
    2. Antonio López-Quílez, 2019. "Spatio-Temporal Analysis of Infectious Diseases," IJERPH, MDPI, vol. 16(4), pages 1-2, February.

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