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Growing Science » International Journal of Data and Network Science » A Bayesian latent gaussian model with time-varying spatial weight matrices: Application to mod-eling the impact of multi-pollutant exposure on tuberculosis

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International Journal of Data and Network Science

ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print)
Quarterly Publication
Volume 9 Issue 3 pp. 419-436 , 2025

A Bayesian latent gaussian model with time-varying spatial weight matrices: Application to mod-eling the impact of multi-pollutant exposure on tuberculosis Pages 419-436 Right click to download the paper Download PDF

Authors: I Gede Nyoman Mindra Jaya, Yudhie Andriyana, Bertho Tantular, Farah Kristiani

DOI: 10.5267/j.ijdns.2024.7.007

Keywords: Latent Gaussian model, Time-varying spatial weight matrices, Monte-Carlo, Air pollutants, Tuberculosis

Abstract: The main objective of spatiotemporal analysis is to offer precise predictions of outcomes. The objective of this study is to assess the accuracy of the Bayesian Latent Gaussian Model in predicting outcomes by utilizing both time-varying and fixed spatial weight matrices. The results of the Monte Carlo simulation suggest that when there is moderate spatial autocorrelation (between 0.3 and 0.7), it is strongly advised to use a time-varying spatial weight matrix. This approach yields the most precise predictions and minimizes any distortion in parameter estimates. Furthermore, we provide an illustrative case study where we simulate the effects of exposure to multiple pollutants on tuberculosis. The analysis revealed that particulate matter 10 (PM10), nitrogen oxides (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3), have a positive influence on the risk of TB, with spatial effects that change over time. The model demonstrates that a rise of 1 mg/m³ in the levels of PM10, NO2, SO2, CO, and O3 is linked to corresponding increases in TB cases by 2.1%, 21.17%, 13.20%, 6.72%, and 6.59%, respectively. NO2 and SO2 have the most significant influence on the risk of tuberculosis (TB). These findings enhance our comprehension of the spatial correlation of TB over time and promote further investigation to determine the most efficacious strategies for mitigating the dissemination of TB.

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.

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Journal: International Journal of Data and Network Science | Year: 2025 | Volume: 9 | Issue: 3 | Views: 163 | Reviews: 0

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