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Multivariate Bayesian Semiparametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia

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  • I. Gede Nyoman Mindra Jaya

    (Center of Epidemiology, Department of Statistics, Universitas Padjadjaran, Jl. Raya Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia)

  • Budhi Handoko

    (Center of Flexible Modeling, Department of Statistics, Universitas Padjadjaran, Jl. Raya Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia)

  • Yudhie Andriyana

    (Center of Flexible Modeling, Department of Statistics, Universitas Padjadjaran, Jl. Raya Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia)

  • Anna Chadidjah

    (Center of Epidemiology, Department of Statistics, Universitas Padjadjaran, Jl. Raya Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia)

  • Farah Kristiani

    (Department of Mathematics, Parahyangan University, Jl. Ciumbuleuit No. 94, Hegarmanah, Kec. Cidadap, Kota Bandung 40141, Indonesia)

  • Mila Antikasari

    (Center of Epidemiology, Department of Statistics, Universitas Padjadjaran, Jl. Raya Bandung Sumedang km 21 Jatinangor, Sumedang 45363, Indonesia)

Abstract

Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects of covariates, and random effects involving spatial and temporal components and their interactions. A shared spatial–temporal component considers correlations between the joint outcomes. Notably, due to spatial–temporal variations, certain covariates may exhibit nonlinear effects, necessitating the use of semiparametric regression models. Sometimes, choropleth maps based on regional data that is aggregated by administrative regions do not adequately depict infectious disease transmission. To counteract this, we combine the area-to-point geostatistical model with inverse distance weighted (IDW) interpolation for high-resolution mapping based on areal data. Additionally, to develop an effective and efficient early warning system for controlling disease transmission, it is crucial to forecast disease risk for a future time. Our study focuses on developing a novel multivariate Bayesian semiparametric regression model for forecasting and mapping HIV and TB risk in West Java, Indonesia, at fine-scale resolution. This novel approach combines multivariate Bayesian semiparametric regression with geostatistical interpolation, utilizing population density and the Human Development Index (HDI) as risk factors. According to an examination of annual data from 2017 to 2021, HIV and TB consistently exhibit recognizable spatial patterns, validating the suitability of multivariate modeling. The multivariate Bayesian semiparametric model indicates significant linear effects of higher population density on elevating HIV and TB risks, whereas the impact of the HDI varies over time and space. Mapping of HIV and TB risks in 2022 using isopleth maps shows a clear HIV and TB transmission pattern in West Java, Indonesia.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3641-:d:1223432
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    References listed on IDEAS

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    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. Mohammadreza Mohebbi & Rory Wolfe & Andrew Forbes, 2014. "Disease Mapping and Regression with Count Data in the Presence of Overdispersion and Spatial Autocorrelation: A Bayesian Model Averaging Approach," IJERPH, MDPI, vol. 11(1), pages 1-20, January.
    3. 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.
    4. 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.
    5. 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).
    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.
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