RELATIVE RISK ESTIMATION OF DENGUE DISEASE IN BANDUNG, INDONESIA, USING POISSON-GAMMA AND BYM MODELS CONSIDERING THE SEVERITY LEVEL
DOI:
https://doi.org/10.11113/.v78.7664Keywords:
Dengue, Bandung, Poisson-gamma, BYM, relative riskAbstract
Recently, dengue as one of the most dangerous diseases in the world has attracted more attention due to its soaring infection cases. One method to estimate the relative risks of dengue transmission commonly used is through the statistics approach. Dengue cases of all severity levels spread rapidly in every district in Bandung, Indonesia every month. There are two different severity levels of dengue disease: the early-stage known as Dengue Fever (DF) and the severe-stage manifested as Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome (DSS). This research investigates the early stage, the severe stage, and the combination of both stages. The non-spatial Poisson-gamma model and spatial Besag, York, and Mollie (BYM) model are applied to estimate the relative risks in each district in Bandung every month. These two models are chosen to analyze whether there is a spatial effect in dengue transmission in particular critical area. This research will use 2013’s data from St. Borromeus hospital, one of the reputable hospitals in Bandung. The results show that the implementation of non-spatial Poisson-gamma and spatial BYM models does not depict a significant difference in the result of the relative risk estimation of dengue transmission in Bandung. The Deviance Information Criterion (DIC) diagnostic indicates that non-spatial model is better than the spatial model. Therefore, it can be concluded that there is no spatial effect in dengue transmission in Bandung. It means that dengue transmission in Bandung is not affected by neighboring areas. This analysis is also applicable to every stage estimated, both for the early-stage as well as the severe-stage.Â
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References
Nathan, D., Dayal-Drager, D., Guzman, D.,et al. 2009. Epidemiology, Burden of Disease and Transmission, Dengue, Guidelines for Diagnosis, Treatment, Prevention and Control. World Health Organization.
Sungkar, S., Fadli, R., and Sukmaningsih, A. 2011. Trend of Dengue Hemorrhagic Fever in North Jakarta, Journal of the Indonesian Medical Association. 61(10): 394-399.
Munsyir, M., and Amiruddin, R. 2011. Mapping and Analysis of DHF cases in Bantaeng Residence, South Sulawesi, 2009. Journal Medika. XXXVII: 380-386.
Mulyatno, K., Yamanaka, A., Yotopranoto, S., and Konishi, E. Vertical Transmission of Dengue Virus in Aedes Aegypti Collected in Surabaya, Indonesia, during 2008-2011. Japan Journal Infectious Disease. 65(3): 274-276.
Supriatna, A. 2009. Estimating the Basic Reproduction Number of Dengue Transmission during 2002-2007 Outbreaks in Bandung, Indonesia. Dengue Bulletin. 33: 21-22.
Bartlett, M. 1964. The Relevance of Stochastic Models for Large-Scale Epidemiological Phenomena. Journal of the Royal Statistical Society. XXXIII: 2-8.
Daley, D., and Gani, J. 1999. Epidemiology Modelling : An Introduction. Cambridge University Press.
Wakefield, J. C., and Morris, S. E. 2001. The Bayesian Modeling of Disease Risk in Relation to a Point Source. Journal of the American Statistical Association. 1996(453): 77-91.
Wakefield, J. 2007. Disease Mapping and Spatial Regression with Count Data. Biostatistics. 8(2): 158-183.
Lawson, A. 2006. Statistical Methods in Spatial Epidemiology. John Wiley and Sons Ltd.
Samat, N., and Ma'arof, S. 2013. Dengue Disease Mapping with Standardized Morbidity Ratio and Poisson-Gamma Model : An Analysis of Dengue Disease in Perak, Malaysia. World Academy of Science, Engineering and Technology. International Journal of Mathematical, Computational Science and Engineering. 7(8): 640-644.
Kristiani, F., Samat, N. A., and Ghani, S. bin Ab. 2016. Dengue Disease Mapping in Bandung, Indonesia: An Analysis Based on Poisson-gamma, Log-normal, BYM and Mixture Models. Jurnal Teknologi. 78(6-5): 7-12.
Irawan, R., Yong, B., and Kristiani, F. 2015. Penentuan Risiko Relatif Untuk Penyebaran Penyakit Demam Dengue di Kota Bandung pada Tahun 2013 dengan Menggunakan Model SMR. Seminar Nasional Matematika UNPAR 2015. Bandung, Indonesia. 19 September 2015. MS 108 - MS 115
Lawson, A., Browne, W., and Rodeiro, C. 2003. Disease Mapping with WinBUGS and MLWin. John Wiley and Sons Ltd.
Lawson, A. 2013. Bayesian Disease Mapping, Hierarchical Modelling in Spatial Epidemiology. 2nd edition. CRC Press Taylor and Francis Group.
Besag, J., York, J., and Mollie, A. 1991. Bayesian Image Restoration with Two Applications in Spatial Statistics. Annals of the Institute of Statistical Mathematics. 43: 1-59.
Tango, T. 2010. Statistical Methods for Disease Clustering. London: Springer.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., and Linde, A. v. d., Bayesian Measures of Model Complexity and Fit. Royal Statistical Society. 64(4): 583-639.
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