How to cite this paper
Jaya, I., Andriyana, Y., Tantular, B & Kristiani, F. (2025). A Bayesian latent gaussian model with time-varying spatial weight matrices: Application to mod-eling the impact of multi-pollutant exposure on tuberculosis.International Journal of Data and Network Science, 9(3), 419-436.
Refrences
Blangiardo, M., & Cameletti, M. (2015). Spatial and Spatio-temporal Bayesian Models with R-INLA. Chennai: John Wiley & Sons.
Carrasco-Escobar, G., Schwalb, A., Tello-Lizarraga, K., Vega-Guerovich, P., & Ugarte-Gil, C. (2020). Spatio-temporal co-occurrence of hotspots of tuberculosis, poverty and air pollution in Lima, Peru. Infectious Diseases of Poverty, 9(02), 84-89.
Dimala, C., & Kadia, B. (2022). A systematic review and meta‐analysis on the association between ambient air pollution and pulmonary tuberculosis. Scientific Reports, 12(11282), 1-13.
Dubé, J., & Legros, D. (2013). A spatio-temporal measure of spatial dependence: An example using real estate data. Pa-pers in Regional Science, 92(1), 19-30.
Feng, Y., Wei, J., Hu, M., Xu, C., Li, T., Wang, J., & Chen, W. (2022). Lagged effects of exposure to air pollutants on the risk of pulmonary tuberculosis in a highly polluted region. International Journal of Environmental Research and Pub-lic Health, 19(9), 5752.
Gelman, A. (2006). Prior Distributions for Variance Parameters in Hierarchical Models. Bayesian Analysis, 1(3), 515–534.
Hazra, A., Huser, R., & Jóhannesson, Á. (2023). Bayesian Latent Gaussian Models for High-Dimensional Spatial Ex-tremes. In B. Hrafnkelsson, Statistical Modeling Using Bayesian Latent Gaussian Models (pp. 219-251). Switzerland: Springer.
Herrera, M., Guzmán-Beltrán, S., Bobadilla, K., Santos-Mendoza, T., Flores-Valdez, M., Gutiérrez-González, L., & Gon-zález, Y. (2022). Human Pulmonary Tuberculosis: Understanding the Immune Response in the Bronchoalveolar Sys-tem. Biomolecules, 12(1148), 1-20.
Hrafnkelsson, B., & Bakka, H. (2023). Bayesian Latent Gaussian Models. In B. Hrafnkelsson, Statistical Modeling Using Bayesian Latent Gaussian Models With Applications in Geophysics and Environmental Sciences (pp. 1-80). Switzer-land: Springer.
Jaya, I. G. N. M., & Folmer, H. (2021). Bayesian spatiotemporal forecasting and mapping of COVID‐19 risk with applica-tion to West Java Province, Indonesia. Journal of Regional Science, 61(4), 849-881.
Leroux, B., Lei, X., & Breslow, N. (1999). Estimation of disease rates in small areas: a new mixed model for spatial de-pendence. In M. Halloran, & D. Berry, Statistical Models in Epidemiology, the Environment and Clinical Trials (pp. 135–178). New York: Springer-Verlag.
Li, H., Ge, M., & Zhang, M. (2022). Spatio-temporal distribution of tuberculosis and the effects of environmental factors in China. BMC Infectious Diseases, 22(565), 1-13.
Lin, R., Shi, H., Yin, G., Thall, P. F., Yuan, Y., & Flowers, C. R. (2022). Bayesian hierarchical random-effects meta-analysis and design of phase I clinical trials. The annals of applied statistics, 16(4), 2481.
Lin, Y. J., Lin, H. C., Yang, Y. F., Chen, C. Y., Ling, M. P., Chen, S. C., ... & Liao, C. M. (2019). Association between am-bient air pollution and elevated risk of tuberculosis development. Infection and drug resistance, 12, 3835-3847.
Mingione, M., Loro, P., Farcomeni, A., Divino, F., Lovison, G., Maruotti , A., & Lasinio, G. (2022). Spatio-temporal mod-elling of COVID-19 incident cases using Richards’ curve: An application to the Italian regions. Spatial Statistics, 49(100544), 1-23.
Moran, P. (1950). A Test for the Serial Independence of Residuals. Biometrika, 37(1/2), 178-181.
Nurhaliza, S. (2024, March 30). Kualitas udara Jakarta urutan lima besar terburuk di dunia. Retrieved May 27, 2024, from ANTARA: https://www.antaranews.com/berita/4035441/kualitas-udara-jakarta-urutan-lima-besar-terburuk-di-dunia
Ou, B., Zhao, X., & Wang, M. (2015). Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices. Journal of Systems Science and Information, 3(5), 463–471.
Peng, Z., Liu, C., Xu, B., Kan, H., & Wang, W. (2017). Long-term exposure to ambient air pollution and mortality in a Chinese tuberculosis cohort. Science of The Total Environment, 580, 1483-1488.
RI, M. o. (2023). Laporan Program Penanggulangan Tuberkulosis Tahun 2022. Jakarta: Ministry of Health RI.
Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrat-ed nested Laplace approximations. Journal of the Royal Statistical Society Series B, 71(2), 319–392.
Rustand, D., Niekerk, J., Krainski, E., Rue, H., & Proust-Lima, C. (2024). Fast and flexible inference for joint models of multivariate longitudinal and survival data using integrated nested Laplace approximations. Biostatistics, 25(2), 429–448.
WHO. (2024, April 20). WHO. Retrieved from Tuberculosis: https://www.who.int/health-topics/tuberculosis#tab=tab_1
Yang, J., Zhang, M., Chen, Y., Ma, L., Yadikaer, R., Lud, Y., . . . Rui, B. (2020). A study on the relationship between air pollution and pulmonary tuberculosis based on the general additive model in Wulumuqi, China. International Journal of Infectious Diseases, 96, 43-47.