# Modeling Dengue Fever by using Conditional Autoregressive Bessag-York-Mollie

## Authors

• Jajang Department of Mathematics, University of Jenderal Soedirman, Indonesia
• Budi Pratikno Department of Mathematics, University of Jenderal Soedirman, Indonesia
• Mashuri Mashuri Department of Mathematics, University of Jenderal Soedirman, Indonesia

## Keywords:

DHF, mapping, CAR-BYM, relative risk

## Abstract

Dengue fever is a tropical disease caused by the dengue virus.Â  The small proportional of this dengue fever disease can develops into a more severe dengue hemorrhagic fever (DHF). This research discussed about the model for disease mapping in Ciamis District. Â Â The purpose of this research is to characterize relative risk and factors correlated with case DHF.Â  Independent variables used in this research are population density, attitude of region, and the number of health worker. To analysis this data, we used conditional autoregressive Bessag-York-Mollie (CAR-BYM) model.Â  Based on descriptive statistic, the maximum and minimum DHF cases are Ciamis and Sukamantri, respectively.Â  Furthermore, basedon model results, we found that the maximum and minimum relative risk are Cijeungjing and Sukamantri, respectively. Furthermore, there were 7 sub districts which relative risk are greater than one and 20 sub districts which relative risk are less than one.Â  The sub districts which relative risk are greater than one show that DHF cases in these sub districts are greater than expectation.Â  Based on the CAR-BYM model result showed that Each increase the population density by one unit contributes to the addition of DHF cases by 0.0012 units. Each additional health worker one unit, it will reduce the number of DHF cases by 0.0675 units. Each additional altitude of one unit will reduce the number of dengue cases by 0.0011 units.Â  Based on relative risk (RR) value of the CAR-BYM model we found that the Cijengjing and Ciamis Districts have darkest color.Â  The RR values in the two sub-districts are 3,449 and 3,240, respectively. The RR values of the two sub-districts are more than expected values.

## References

Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014). Hierarchical modeling and analysis for spatial data. CRC press.

Besag, J., & Kooperberg, C. (1995). On conditional and intrinsic autoregressions. Biometrika, 82(4), 733â€“746.

Cheng, Y.-J., Norris, J., Bao, C.-J., Liang, Q., Hu, J.-L., Wu, Y., Tang, F.-Y., Liu, W.-D., Ding, K.-Q., & Zhao, Y. (2012). Geographical information systems-based spatial analysis and implications for syphilis interventions in Jiangsu province, Peopleâ€™s Republic of China. Geospatial Health, 7(1), 63â€“72.

Cressie, N. A. (1993). Statistics for spatial data. John Willy and Sons. Inc., New York.

Feng, W., Lim, C. Y., Maiti, T., & Zhang, Z. (2016). Spatial regression and estimation of disease risks: A clusteringâ€based approach. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(6), 417â€“434.

Gaetan, C., & Guyon, X. (2010). Spatial statistics and modeling (Vol. 90). Springer.

Han, X., & Lee, L. (2013). Bayesian estimation and model selection for spatial Durbin error model with finite distributed lags. Regional Science and Urban Economics, 43(5), 816â€“837.

Hendri, J., Santya, R. N. R. E., & Prasetyowati, H. (2015). Distribusi dan kepadatan vektor demam berdarah dengue (DBD) berdasarkan ketinggian tempat di Kabupaten Ciamis Jawa Barat. Indonesian Journal of Health Ecology, 14(1), 17â€“28.

Hendricks, J., & Neumann, C. (2020). A Bayesian approach for the analysis of error rate studies in forensic science. Forensic Science International, 306, 110047.

Ibrahim, J., Chen, M., & Sinha, D. (2001). Bayesian Survival Analysis Springer Series in Statistics. New York, NY: Springer. Doi, 10, 978â€“1.

Lawson, A. B. (2018). Bayesian disease mapping: Hierarchical modeling in spatial epidemiology. Chapman and Hall/CRC.

MacNab, Y. C., Farrell, P. J., Gustafson, P., & Wen, S. (2004). Estimation in Bayesian disease mapping. Biometrics, 60(4), 865â€“873.

McCullagh, P., & Nelder, J. A. (1989). Generalized Linear Models (Vol. 37). CRC Press.

Nelson, T. A., & Boots, B. (2008). Detecting spatial hot spots in landscape ecology. Ecography, 31(5), 556â€“566.

Obaromi, D. (2019). Spatial Modelling of Some Conditional Autoregressive Priors in A Disease Mapping Model: The Bayesian Approach. Biomedical Journal of Scientific & Technical Research, 14(3).

Srinivasan, R., & Venkatesan, P. (2014). Bayesian random effects model for disease mapping of relative risks. Ann Biol Res, 5(1), 23â€“31.

Stern, H. S., & Cressie, N. (2000). Posterior predictive model checks for disease mapping models. Statistics in Medicine, 19(17â€18), 2377â€“2397.

Zhi-Hang, P., Yue-Jia, C., Reilly, K. H., Lu, W., Qian-Qian, Q., Zheng-Wei, D., Guo-Wei, D., Ke-Qin, D., Rong-Bin, Y., & Feng, C. (2011). Spatial distribution of HIV/AIDS in Yunnan province, Peopleâ€™s Republic of China. Geospatial Health, 5(2), 177â€“182.

2022-05-31

## How to Cite

Jajang, J., Pratikno, B., & Mashuri, M. (2022). Modeling Dengue Fever by using Conditional Autoregressive Bessag-York-Mollie: Pemodelan Demam Berdarah dengan Menggunakan Conditional Autoregressive Bessag-York-Mollie. Indonesian Journal of Statistics and Its Applications, 6(1), 101–113. https://doi.org/10.29244/ijsa.v6i1p101-113

Articles