|
UPSI Digital Repository (UDRep)
|
|
| ||||||||||||||||
| Abstract : |
| Dengue is one of the most dangerous vector-borne infectious diseases in the world which are fatal in many cases due to inefficient treatment. Although the relative risk estimations of dengue transmission are available for Bandung, one of the most populous cities in Indonesia, dengue cases especially among young people have increased rapidly. However, the age factor has not yet been included in these estimations. Because specific treatment and prevention depend on age, this factor must be considered in any dengue transmission model. In this article, the authors classify dengue cases in Bandung into juveniles and adults. Each group is analyzed by SIR-SI model to estimate the relative risk of dengue transmission as an indirect transmission disease which takes into account the stochastic factor. This model also considers a spatial correlation which affects dengue distribution in a specific area for a particular age group. The results of the analysis show that some areas in Bandung have medium to very high dengue risk, especially for the juvenile group. It is expected that The Health Department of Bandung will focus dengue transmission prevention programs more intensively on this group. In further research, if the data needed are available, this model can be applied to other cities in Indonesia |
| References |
1. Ahmad, R., Ismail, A., Saat, Z., & Han Lim, L. (1997). Detection of dengue virus from field aedes aegypti and aedes albopictus adults and larvae.The Southeast Asian Journal of Tropical Medicine and Public Health, 138-142. 2. Bartlett, M. (1964). The relevance of stochastic models for large-scale epidemiological phenomena. Journal of the Royal Statistical Society,XXXIII, 2-8. 3. Bandung, G. (2008). Technical Material Spatial Plan of Urban Bandung Area in the Year 2011–2031. Bandung: Bandung Government. Retrieved March 2016, from https://www.slideshare.net/joihot/dokumen-rtrw-kota-bandung-tahun-2011-2031 4. Bernardinelli, L., Clayton, D., Pascutto, C., Montomoli, C., Ghislandi, M., & Songini, M. (1995). Bayesian analysis of space-time variation in disease risk. Statistics in Medicine, 14, 2433-2443. 5. Besag, J., York, J., & Mollie, A. (1991). Bayesian image restoration with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43, 1-59. 6. Bolstad, W. (2004). Introduction to Bayesian Statistics. New Jersey: John Wiley & Sons. Bowers, N., Gerber, H., Hickman, J., Jones, D., & Nesbitt, C. (1997). Actuarial Mathematics (2nd ed.). The Society of Actuaries. 7. Daley, D., & Gani, J. (1999). Epidemiology Modelling: An Introduction. Cambridge University Press. Esteva, L., & Vargas, C. (1998). Analysis of a dengue disease transmission model. Mathematical Biosciences, 150, 131-151. 8. Fong, I. (2013). Emerging Infectious Diseases of the 21st Century, Challenges in Infectious Diseases. Toronto: Springer. 9. Kristiani, F., Samat, N. A., & Ghani, S. B. (2015). Preliminary analysis on dengue disease mapping in bandung, indonesia based on standardized morbidity ratio (SMR). International Journal of Applied Mathematics and Statistics, 53(6), 195-201. 10. Kristiani, F., Samat, N., & bin Ab Ghani, S. (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. 11. Kristiani, F., Yong, B., & Irawan, R. (2016). Relative risk estimation of dengue disease in bandung, indonesia, using poisson-gamma and BYM models considering the severity level. Jurnal Teknologi, 78(11), 57-64. 12. Lawson, A. (2006). Statistical Methods in Spatial Epidemiology. John Wiley and Sons Ltd. 13. Lawson, A. (2013). Bayesian Disease Mapping, Hierarchical Modelling in Spatial Epidemiology (2nd ed.). CRC Press Taylor and Francis Group. 14. Lawson, A., & Williams, L. (2008). Detecting commuting patterns by clustering subtrajectories. In Algorithms and Computation, 644-655. 15. Ma, Z., & Li, J. (2009). Dynamical Modeling and Analysis of Epidemics. World Scientific Publishing Co. Pte. Ltd. 16. 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. 17. Nishiura, H. (2006). Mathematical and statistical analyses of the spread of dengue. Dengue Bulletin, XXX, 51-67. 18. Pongsumpun, P. (2009). Age structured model for symptomatic and asymptomatic infections of dengue disease. International Journal of Modelling and Simulation, 2, 199-205. 19. Pongsumpun, P., & Tang, I. (2003). Transmission of dengue hemorrhagic fever in an age structured population. Mathematical and Computer Modelling, XXXVII, 949-961. 20. Pongsumpun, P., & Tiensuwan, M. (2013). Application of log-linear models to dengue virus infection patients in thailand. Model Assisted Statistics and Applications, 8, 275-287. doi: 103233/MAS-130265. 21. Pongsumpun, P., Patanarapelert, K., Sriprom, M., Varamit, S., & Tang, I. (2004). Infection risk to travelers going to dengue fever endemic regions. Southeast Asian J Trop Med Public Health, 35(1), 155-159. 22. Samat, N., & Percy, D. (2012). Vector-borne infectious disease mapping with stochastic difference equations: An analysis of dengue disease in Malaysia. Journal of Applied Statistics, 39(9), 2029-2046. 23. Spiegelhalter, D., Best, N., Carlin, B., & Linde, A. (2002). Bayesian measures of model complexity and fit. Royal Statistical Society, 64(4), 583-639. 24. Supriatna, A. (2009). Estimating the basic reproduction number of dengue transmission during 2002–2007 outbreaks in Bandung, Indonesia. Dengue Bulletin, 33, 21-22. 25. Supriatna, A., Soewono, E., & Gils, S. V. (2008). A two-age-classes dengue transmission model. Mathematical Biosciences, 216. 26. Vaughn, D., Green, S., Kalayanarooj, S., Innis, B., Nimmannitya, S., et al. (1997). Dengue in the early febrile phase: Viremia and antibody responses. The Journal of Infectious Diseases, 322-330. 27. Wakefield, J. (2007). Disease mapping and spatial regression with count data. Biostatistics, 8(2), 158-183. 28. Wakefield, J., & Morris, S. (2001). The bayesian modeling of disease risk in relation to a point source. Journal of the American Statistical Association, 1996(453), 77-91. 29. WHO. (2012). Global Strategy for Dengue Prevention and Control. Geneva: WHO Press. 30. Widiyani, R. (2013, April). Empat Sekawan Penyebab DBD. Retrieved from www.kompas.com: http://health.kompas.com/read/2013/04/03/ 18534298/Empat.Sekawan.Penyebab.DBD |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |