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Spatial Estimation of Relative Risk for Dengue Fever in Aceh Province using Conditional Autoregressive Method
Abstract
Dengue Fever (DHF) is a dangerous infectious disease that can cause death in an infected person. DHF is a disease transmitted by the Aedes Aegypti mosquito. Dengue cases have been reported in 449 districts/cities spread across 34 provinces with deaths spread across 162 districts/cities in 31 provinces, one of which is in Aceh Province. However, there are districts and cities in Aceh Province with a large number of cases and population at risk, and there are also districts and cities with fewer cases and population at risk. As a result, the number of cases and population at risk of DHF varies. Therefore, it is important to do planning to see which districts and cities have a high chance of DHF. In this study, the type of data used is secondary data sourced from the Aceh Provincial Health Profile from 2016 to 2022. The approach used is the Bayesian Conditional Autoregressive (CAR) prior model Besag-York-Mollie (BYM). The results of this study showed that mortality in dengue cases in Aceh Province from 2016 to 2022 had the highest mortality values in 2016 and 2022. The results of estimating the relative risk of DHF cases using the Bayesian Conditional Autoregressive (CAR) approach of the Besag-York-Mollie (BYM) Model in Aceh Province fulfill all categories with their relative risk values. Some districts/cities have relative risk values. Some districts/cities have high relative risk values of DHF cases and low relative risk values of DHF cases. Sabang city had the highest relative risk value of 3.54 and Bener Meriah district had the lowest relative risk of 0.2.
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L. D. Frenkel, “Infectious diseases as a cause of global childhood mortality and morbidity: Progress in recognition, prevention, and treatment,” Adv. Pediatr. Res., pp. 1–11, 2018, doi: 10.24105/apr.2018.5.14.
N. R. Sasmita, M. Ikhwan, S. Suyanto, and V. Chongsuvivatwong, “Optimal control on a mathematical model to pattern the progression of coronavirus disease 2019 (COVID-19) in Indonesia,” Glob. Heal. Res. Policy, vol. 5, no. 1, p. 38, Dec. 2020, doi: 10.1186/s41256-020-00163-2.
A. Sanyaolu, “Global Epidemiology of Dengue Hemorrhagic Fever: An Update,” J. Hum. Virol. Retrovirology, vol. 5, no. 6, Oct. 2017, doi: 10.15406/jhvrv.2017.05.00179.
World Health Organization (WHO)., Dengue: Guidelines for Diagnosis, Treatment, Prevention and Control. Genewa: WHO, 2009. [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK143157/
S. Bhatt et al., “The global distribution and burden of dengue,” Nature, vol. 496, no. 7446, pp. 504–507, Apr. 2013, doi: 10.1038/nature12060.
O. J. Brady et al., “Refining the Global Spatial Limits of Dengue Virus Transmission by Evidence-Based Consensus,” PLoS Negl. Trop. Dis., vol. 6, no. 8, p. e1760, Aug. 2012, doi: 10.1371/journal.pntd.0001760.
L. Liu et al., “Epidemiology for public health practice: The application of spatial epidemiology,” World J. Diabetes, vol. 13, no. 7, pp. 584–586, Jul. 2022, doi: 10.4239/wjd.v13.i7.584.
H. Sofyan et al., “The state of diabetes care and obstacles to better care in Aceh, Indonesia: a mixed-methods study,” BMC Health Serv. Res., vol. 23, no. 1, p. 271, Mar. 2023, doi: 10.1186/s12913-023-09288-9.
Z. Kesuma, L. Siregar, E. Fradinata, and A. Fathinah, “Factors Affecting Stunting in Children under 5 Years of Age in Indonesia using Spatial Model,” Indian J. Public Health, vol. 66, no. 3, p. 264, 2022, doi: 10.4103/ijph.ijph_1989_21.
G. Konstantinoudis, D. Schuhmacher, H. Rue, and B. D. Spycher, “Discrete versus continuous domain models for disease mapping,” Spat. Spatiotemporal. Epidemiol., vol. 32, p. 100319, Feb. 2020, doi: 10.1016/j.sste.2019.100319.
K. M. Zurnila, Hizir, R. Latifah, and U. Farazilla, “Spatial Distribution of Anemia Among Adolescents In Banda Aceh, Indonesia,” IOP Conf. Ser. Mater. Sci. Eng., vol. 506, no. 1, p. 012007, Apr. 2019, doi: 10.1088/1757-899X/506/1/012007.
N. A. Samat and L. W. Mey, “Malaria Disease Mapping in Malaysia based on Besag-York-Mollie (BYM) Model,” J. Phys. Conf. Ser., vol. 890, p. 012167, Sep. 2017, doi: 10.1088/1742-6596/890/1/012167.
D. Lee, A. Rushworth, and S. K. Sahu, “A Bayesian localized conditional autoregressive model for estimating the health effects of air pollution,” Biometrics, vol. 70, no. 2, pp. 419–429, Jun. 2014, doi: 10.1111/biom.12156.
L. Raubenheimer, R. Southey, and Sarah E. Radlof, “Bayesian Disease Mapping: A Literature Review With an Application, Using WinBugs Software,” Stat. Optim. Inf. Comput., vol. 10, no. 3, pp. 829–857, Apr. 2022, doi: 10.19139/soic-2310-5070-1395.
J. Besag, J. York, and A. Molli, “Bayesian image restoration, with two applications in spatial statistics,” Ann. Inst. Stat. Math., vol. 43, no. 1, pp. 1–20, Mar. 1991, doi: 10.1007/BF00116466.
F. Yanuar and C. Saputri, “Bayesian inference for Pareto distribution with prior conjugate and prior non conjugate,” J. Mat. Stat. dan Komputasi, vol. 16, no. 3, p. 382, Apr. 2020, doi: 10.20956/jmsk.v16i3.8019.
R. Idroes et al., A Genetic Algorithm to Determine Research Consultation Schedules in Campus Environment, vol. 796. 2020, pp. 1–8. doi: 10.1088/1757-899X/796/1/012033.
L. Bernardinelli, D. Clayton, C. Pascutto, C. Montomoli, M. Ghislandi, and M. Songini, “Bayesian analysis of space—time variation in disease risk,” Stat. Med., vol. 14, no. 21–22, pp. 2433–2443, Nov. 1995, doi: 10.1002/sim.4780142112.
D. Vats, N. Robertson, J. M. Flegal, and G. L. Jones, “Analyzing Markov chain Monte Carlo output,” WIREs Comput. Stat., vol. 12, no. 4, Jul. 2020, doi: 10.1002/wics.1501.
R. Idroes et al., “Retention Index Prediction of Flavor and Fragrance by Multiple Linear Regression and the Genetic Algorithm,” Int. Rev. Model. Simulations, vol. 12, no. 6, p. 373, Dec. 2019, doi: 10.15866/iremos.v12i6.18353.
M. van Oijen, “Deriving the Posterior Distribution,” in Bayesian Compendium, Cham: Springer International Publishing, 2020, pp. 29–32. doi: 10.1007/978-3-030-55897-0_5.
R. Idroes et al., “Analysis of Temperature and Column Variation in Gas Chromatography to Dead Time of Inert Gas and N-Alkane Homologous Series Using Randomized Block Design,” IOP Conf. Ser. Earth Environ. Sci., vol. 364, no. 012020, pp. 1–7, 2019, doi: 10.1088/1755-1315/364/1/012020.
R. Idroes et al., “Application of Genetic Algorithm-Multiple Linear Regression and Artificial Neural Network Determinations for Prediction of Kovats Retention Index,” Int. Rev. Model. Simulations, vol. 14, no. 2, p. 137, Apr. 2021, doi: 10.15866/iremos.v14i2.20460.
A. Terenin, S. Dong, and D. Draper, “GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model,” Stat. Comput., vol. 29, no. 2, pp. 301–310, Mar. 2019, doi: 10.1007/s11222-018-9809-3.
M. Agustia et al., “Application of Fuzzy Support Vector Regression to Predict the Kovats Retention Indices of Flavors and Fragrances,” in 2022 International Conference on Electrical Engineering and Informatics (ICELTICs), IEEE, Sep. 2022, pp. 13–18. doi: 10.1109/ICELTICs56128.2022.9932124.
T. Park and S. Lee, “Improving the Gibbs sampler,” WIREs Comput. Stat., vol. 14, no. 2, Mar. 2022, doi: 10.1002/wics.1546.
Y. Nadia et al., “Simple Combination Method of FTIR Spectroscopy and Chemometrics for Qualitative Identification of Cattle Bones,” IOP Conf. Ser. Earth Environ. Sci., vol. 364, no. 1, p. 012040, Nov. 2019, doi: 10.1088/1755-1315/364/1/012040.
G. Zanella and G. Roberts, “Multilevel Linear Models, Gibbs Samplers and Multigrid Decompositions (with Discussion),” Bayesian Anal., vol. 16, no. 4, Dec. 2021, doi: 10.1214/20-BA1242.
A. Maulana, T. R. Noviandy, R. Idroes, N. R. Sasmita, R. Suhendra, and I. Irvanizam, “Prediction of Kovats Retention Indices for Fragrance and Flavor using Artificial Neural Network,” Proc. Int. Conf. Electr. Eng. Informatics, vol. 2020-Octob, pp. 1–5, Oct. 2020, doi: 10.1109/ICELTICs50595.2020.9315391.
M. N. Mittinty and J. Lynch, “Reflection on modern methods: risk ratio regression—simple concept yet complex computation,” Int. J. Epidemiol., vol. 52, no. 1, pp. 309–314, Feb. 2023, doi: 10.1093/ije/dyac220.
Azharuddin et al., “Patient Satisfaction And Its Socio-Demographic Correlates In Zainoel Abidin Hospital, Indonesia: A Cross-Sectional Study,” Unnes J. Public Heal., vol. 12, no. 2, pp. 57–67, 2023, doi: 10.15294/ujph.v12i2.69233.
D. Sasaki, H. Sofyan, N. R. Sasmita, M. Affan, and N. Nizamuddin, “Assessing the Intermediate Function of Local Academic Institutions During the Rehabilitation and Reconstruction of Aceh, Indonesia,” J. Disaster Res., vol. 16, no. 8, pp. 1265–1273, Dec. 2021, doi: 10.20965/jdr.2021.p1265.
N. Earlia et al., “Protective Effects of Acehnese Traditionally Fermented Coconut Oil (Pliek U Oil) and its Residue (Pliek U) in Ointment against UV Light Exposure: Studies on Male Wistar Rat Skin (Rattus novergicus),” Sains Malaysiana, vol. 50, no. 5, pp. 1285–1295, May 2021, doi: 10.17576/jsm-2021-5005-09.
T. R. Noviandy et al., “The Prediction of Kovats Retention Indices of Essential Oils At Gas Chromatography Using Genetic Algorithm-Multiple Linear Regression and Support Vector Regression,” J. Eng. Sci. Technol., vol. 17, no. 1, pp. 306–326, 2022.
S. P. Brooks, “Markov Chain Monte Carlo Method and Its Application,” J. R. Stat. Soc. Ser. D (The Stat., vol. 1, no. 47, pp. 69–100, 1998, [Online]. Available: http://www.jstor.org/stable/2988428
S. D. Hill and J. C. Spall, “Stationarity and Convergence of the Metropolis-Hastings Algorithm: Insights into Theoretical Aspects,” IEEE Control Syst., vol. 39, no. 1, pp. 56–67, Feb. 2019, doi: 10.1109/MCS.2018.2876959.
G. Fitzmaurice, Handbook of Markov Chain Monte Carlo. Boston, MA, U.S.A2/; Chapman and Hall/CRC, 2011. doi: 10.1201/b10905.
N. A. Samat and M. I. M. S.H, “Dengue Disease Mapping with Standardized Morbidity Ratio and Poisson-gamma Model: An Analysis of Dengue Disease in Perak, Malaysia,” Int. J. Math. Comput. Sci., vol. 7, no. 8, pp. 1299–1303, 2013.
DOI: https://doi.org/10.47738/jads.v4i4.141
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