IDEAS home Printed from https://ideas.repec.org/a/kap/jgeosy/v22y2020i1d10.1007_s10109-019-00311-4.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10109-019-00311-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10109-019-00311-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yan Liu & Stella C Watson & Jenna R Gettings & Robert B Lund & Shila K Nordone & Michael J Yabsley & Christopher S McMahan, 2017. "A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-18, July.
    2. Randall Jackson & Peter Schaeffer (ed.), 2017. "Regional Research Frontiers - Vol. 2," Advances in Spatial Science, Springer, number 978-3-319-50590-9, February.
    3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    4. Dian Handayani & Henk Folmer & Anang Kurnia & Khairil Anwar Notodiputro, 2018. "The spatial empirical Bayes predictor of the small area mean for a lognormal variable of interest and spatially correlated random effects," Empirical Economics, Springer, vol. 55(1), pages 147-167, August.
    5. Ping Yin & Lan Mu & Marguerite Madden & John Vena, 2014. "Hierarchical Bayesian modeling of spatio-temporal patterns of lung cancer incidence risk in Georgia, USA: 2000–2007," Journal of Geographical Systems, Springer, vol. 16(4), pages 387-407, October.
    6. 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.
    7. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    8. Bivand, Roger & Gómez-Rubio, Virgilio & Rue, Håvard, 2015. "Spatial Data Analysis with R-INLA with Some Extensions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i20).
    9. Rodrigues, E.C. & Assunção, R., 2012. "Bayesian spatial models with a mixture neighborhood structure," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 88-102.
    10. Stella C Watson & Yan Liu & Robert B Lund & Jenna R Gettings & Shila K Nordone & Christopher S McMahan & Michael J Yabsley, 2017. "A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-22, May.
    11. I Gede Nyoman Mindra Jaya & Henk Folmer & Budi Nurani Ruchjana & Farah Kristiani & Yudhie Andriyana, 2017. "Modeling of Infectious Diseases: A Core Research Topic for the Next Hundred Years," Advances in Spatial Science, in: Randall Jackson & Peter Schaeffer (ed.), Regional Research Frontiers - Vol. 2, chapter 0, pages 239-255, Springer.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Zhaoyang Liu & Heqing Huang & Juha Siikamäki & Jintao Xu, 2024. "Area-Based Hedonic Pricing of Urban Green Amenities in Beijing: A Spatial Piecewise Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(5), pages 1223-1248, May.
    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. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    2. Darren J. Mayne & Geoffrey G. Morgan & Bin B. Jalaludin & Adrian E. Bauman, 2018. "Does Walkability Contribute to Geographic Variation in Psychosocial Distress? A Spatial Analysis of 91,142 Members of the 45 and Up Study in Sydney, Australia," IJERPH, MDPI, vol. 15(2), pages 1-24, February.
    3. Chien-Chou Chen & Guo-Jun Lo & Ta-Chien Chan, 2022. "Spatial Analysis on Supply and Demand of Adult Surgical Masks in Taipei Metropolitan Areas in the Early Phase of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(11), pages 1-12, May.
    4. 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.
    5. Sheyla Rodrigues Cassy & Samuel Manda & Filipe Marques & Maria do Rosário Oliveira Martins, 2022. "Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique," IJERPH, MDPI, vol. 19(10), pages 1-15, May.
    6. 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.
    7. Virgilio Gómez-Rubio & Roger S. Bivand & Håvard Rue, 2021. "Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation," Mathematics, MDPI, vol. 9(17), pages 1-23, August.
    8. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    9. Massimo Bilancia & Giacomo Demarinis, 2014. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 71-94, March.
    10. Isabel Martínez-Pérez & Verónica González-Iglesias & Valentín Rodríguez Suárez & Ana Fernández-Somoano, 2021. "Spatial Distribution of Hospitalizations for Ischemic Heart Diseases in the Central Region of Asturias, Spain," IJERPH, MDPI, vol. 18(23), pages 1-10, November.
    11. Maike Tahden & Juliane Manitz & Klaus Baumgardt & Gerhard Fell & Thomas Kneib & Guido Hegasy, 2016. "Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-19, October.
    12. Luca Grassetti & Laura Rizzi, 2019. "The determinants of individual health care expenditures in the Italian region of Friuli Venezia Giulia: evidence from a hierarchical spatial model estimation," Empirical Economics, Springer, vol. 56(3), pages 987-1009, March.
    13. Ferreira, Marco A.R. & Porter, Erica M. & Franck, Christopher T., 2021. "Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    14. Faustin Habyarimana & Temesgen Zewotir & Shaun Ramroop, 2017. "Structured Additive Quantile Regression for Assessing the Determinants of Childhood Anemia in Rwanda," IJERPH, MDPI, vol. 14(6), pages 1-15, June.
    15. Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng, 2022. "Spatial dependence in microfinance credit default," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1071-1085.
    16. Yang, Anni & Liu, Chenhui & Yang, Di & Lu, Chaoru, 2023. "Electric vehicle adoption in a mature market: A case study of Norway," Journal of Transport Geography, Elsevier, vol. 106(C).
    17. Thomas Suesse, 2018. "Estimation of spatial autoregressive models with measurement error for large data sets," Computational Statistics, Springer, vol. 33(4), pages 1627-1648, December.
    18. Ropo E. Ogunsakin & Themba G. Ginindza, 2022. "Bayesian Spatial Modeling of Diabetes and Hypertension: Results from the South Africa General Household Survey," IJERPH, MDPI, vol. 19(15), pages 1-17, July.
    19. Marc Francke & Alex Van de Minne, 2021. "Modeling unobserved heterogeneity in hedonic price models," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(4), pages 1315-1339, December.
    20. Panczak, Radoslaw & Moser, André & Held, Leonhard & Jones, Philip A. & Rühli, Frank J. & Staub, Kaspar, 2017. "A tall order: Small area mapping and modelling of adult height among Swiss male conscripts," Economics & Human Biology, Elsevier, vol. 26(C), pages 61-69.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:jgeosy:v:22:y:2020:i:1:d:10.1007_s10109-019-00311-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.