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Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung, Indonesia

Author

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  • I. Gede Nyoman Mindra Jaya

    (University of Groningen
    Padjadjaran University)

  • Henk Folmer

    (University of Groningen
    Northwest Agricultural and Forestry University)

Abstract

Dengue disease has serious health and socio-economic consequences. Mapping its occurrence at a fine spatiotemporal scale is a crucial element in the preparation of an early warning system for the prevention and control of dengue and other viral diseases. This paper presents a Bayesian spatiotemporal random effects (pure) model of relative dengue disease risk estimated by integrated nested Laplace approximation. Continuous isopleth mapping based on inverse distance weighting is applied to visualize the disease’s geographical evolution. The model is applied to data for 30 districts in the city of Bandung, Indonesia, for the period January 2009 to December 2016. We compared the Poisson and the negative binomial distributions for the number of dengue cases, both combined with a model which included structured and unstructured spatial and temporal random effects and their interactions. Using several Bayesian and classical model performance criteria and stepwise backward selection, we chose the negative binomial distribution and the temporal model with spatiotemporal interaction for forecasting. The estimation results show that the relative risk decreased generally from 2014. However, it consistently increased in the north-western districts because of environmental and socio-economic conditions. We also found that every district has a different temporal pattern, indicating that district characteristics influence the temporal variation across space.

Suggested Citation

  • I. Gede Nyoman Mindra Jaya & Henk Folmer, 2020. "Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung, Indonesia," Journal of Geographical Systems, Springer, vol. 22(1), pages 105-142, January.
    • Handle: RePEc:kap:jgeosy:v:22:y:2020:i:1:d:10.1007_s10109-019-00311-4
    DOI: 10.1007/s10109-019-00311-4
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    References listed on IDEAS

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    2. Laura Serra & Claudio Detotto & Pablo Juan & Marco Vannini, 2022. "Intersectoral and spatial spill-overs of firms’ bankruptcy in Spain," Letters in Spatial and Resource Sciences, Springer, vol. 15(2), pages 197-211, August.
    3. Laura Serra & Claudio Detotto & Marco Vannini, 2022. "Public lands as a mitigator of wildfire burned area using a spatio-temporal model applied in Sardinia," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 621-635, December.
    4. Ranjita Pandey & Himanshu Tolani, 2022. "Crime patterns in Delhi: a Bayesian spatio-temporal assessment," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2971-2980, December.
    5. I. Gede Nyoman Mindra Jaya & Henk Folmer, 2022. "Spatiotemporal high-resolution prediction and mapping: methodology and application to dengue disease," Journal of Geographical Systems, Springer, vol. 24(4), pages 527-581, October.
    6. Tamás Krisztin & Philipp Piribauer & Michael Wögerer, 2020. "The spatial econometrics of the coronavirus pandemic," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 209-218, December.
    7. I. Gede Nyoman M. Jaya & Henk Folmer, 2021. "Bayesian spatiotemporal forecasting and mapping of COVID‐19 risk with application to West Java Province, Indonesia," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 849-881, September.
    8. I Gede Nyoman Mindra Jaya & Farah Kristiani & Yudhie Andriyana & Anna Chadidjah, 2024. "Sensitivity Analysis on Hyperprior Distribution of the Variance Components of Hierarchical Bayesian Spatiotemporal Disease Mapping," Mathematics, MDPI, vol. 12(3), pages 1-16, January.
    9. I. Gede Nyoman Mindra Jaya & Budhi Handoko & Yudhie Andriyana & Anna Chadidjah & Farah Kristiani & Mila Antikasari, 2023. "Multivariate Bayesian Semiparametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia," Mathematics, MDPI, vol. 11(17), pages 1-23, August.
    10. Yikuan Chen & B. Wade Brorsen & Jon T. Biermacher & Mykel Taylor, 2022. "Spatially varying wheat protein premiums," Letters in Spatial and Resource Sciences, Springer, vol. 15(3), pages 587-598, December.

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    More about this item

    Keywords

    Dengue disease; Bayesian spatiotemporal random effects (pure) model; Integrated nested Laplace approximation (INLA); Isopleth mapping; Bandung—Indonesia;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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