IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i3p451-d1329974.html
   My bibliography  Save this article

Sensitivity Analysis on Hyperprior Distribution of the Variance Components of Hierarchical Bayesian Spatiotemporal Disease Mapping

Author

Listed:
  • I Gede Nyoman Mindra Jaya

    (Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Farah Kristiani

    (Department of Mathematics, Parahyangan University, Kota Bandung 40141, Indonesia)

  • Yudhie Andriyana

    (Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia)

  • Anna Chadidjah

    (Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia)

Abstract

Spatiotemporal disease mapping modeling with count data is gaining increasing prominence. This approach serves as a benchmark in developing early warning systems for diverse disease types. Spatiotemporal modeling, characterized by its inherent complexity, integrates spatial and temporal dependency structures, as well as interactions between space and time. A Bayesian approach employing a hierarchical structure serves as a solution for spatial model inference, addressing the identifiability problem often encountered when utilizing classical approaches like the maximum likelihood method. However, the hierarchical Bayesian approach faces a significant challenge in determining the hyperprior distribution for the variance components of hierarchical Bayesian spatiotemporal models. Commonly used distributions include logGamma for log inverse variance, Half-Cauchy, Penalized Complexity, and Uniform distribution for hyperparameter standard deviation. While the logGamma approach is relatively straightforward with faster computing times, it is highly sensitive to changes in hyperparameter values, specifically scale and shape. This research aims to identify the most optimal hyperprior distribution and its parameters under various conditions of spatial and temporal autocorrelation, as well as observation units, through a Monte Carlo study. Real data on dengue cases in West Java are utilized alongside simulation results. The findings indicate that, across different conditions, the Uniform hyperprior distribution proves to be the optimal choice.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:451-:d:1329974
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/3/451/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/3/451/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Getayeneh Antehunegn Tesema & Zemenu Tadesse Tessema & Stephane Heritier & Rob G. Stirling & Arul Earnest, 2023. "A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research," IJERPH, MDPI, vol. 20(7), pages 1-24, March.
    2. Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.
    3. 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.
    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. 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.
    6. 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).
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    3. Daniela Castro-Camilo & Raphaël Huser & Håvard Rue, 2019. "A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(3), pages 517-534, September.
    4. 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.
    5. Clinton Woods & Han Yu & Hong Huang, 2020. "Predicting the success of entrepreneurial campaigns in crowdfunding: a spatio-temporal approach," Journal of Innovation and Entrepreneurship, Springer, vol. 9(1), pages 1-23, December.
    6. Silius M. Vandeskog & Sara Martino & Daniela Castro-Camilo & Håvard Rue, 2022. "Modelling Sub-daily Precipitation Extremes with the Blended Generalised Extreme Value Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 598-621, December.
    7. 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.
    8. Na Zhao & Mingxing Chen, 2021. "A Comprehensive Study of Spatiotemporal Variations in Temperature Extremes across China during 1960–2018," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    9. 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.
    10. 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.
    11. Fernando Santa & Roberto Henriques & Joaquín Torres-Sospedra & Edzer Pebesma, 2019. "A Statistical Approach for Studying the Spatio-Temporal Distribution of Geolocated Tweets in Urban Environments," Sustainability, MDPI, vol. 11(3), pages 1-29, January.
    12. 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.
    13. Chen, Yewen & Chang, Xiaohui & Luo, Fangzhi & Huang, Hui, 2023. "Additive dynamic models for correcting numerical model outputs," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    14. 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.
    15. Paula Simões & Sérgio Gomes & Isabel Natário, 2021. "Hospital Emergency Room Savings via Health Line S24 in Portugal," Econometrics, MDPI, vol. 9(1), pages 1-10, February.
    16. 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.
    17. 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.
    18. 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.
    19. Hubin, Aliaksandr & Storvik, Geir, 2018. "Mode jumping MCMC for Bayesian variable selection in GLMM," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 281-297.
    20. Brian Witrick & Corey A. Kalbaugh & Lu Shi & Rachel Mayo & Brian Hendricks, 2021. "Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina," IJERPH, MDPI, vol. 19(1), pages 1-11, December.

    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:gam:jmathe:v:12:y:2024:i:3:p:451-:d:1329974. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.