MAPPING DHF RISK USING BAYESIAN CONVOLUTION SIR-SI SPATIO-TEMPORAL MODEL

Published 31 May 2020 •  vol 138  • 


Authors:

 

Mukhsar, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo Kendari-Indonesia
Asrul Sani, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo Kendari-Indonesia
Edi Cahyono, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo Kendari-Indonesia
Ida Usman, Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo Kendari-Indonesia
Bahriddin Abapihi, Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Halu Oleo Kendari-Indonesia

Abstract:

 

Dengue Hemorrhagic Fever (DHF) as a tropical disease always occurs in a densely populated every year. This case always appears when the rainy season with high intensity in Indonesia, even outbreaks often happen and cause many deaths. Various efforts have been made to prevent the spread of this disease, but there are still outbreaks every year, especially in urban areas. One of the problems is the eradication process is not on target. The DHF cases occur because of the dengue virus transmitted by the Aedes aegypti mosquitoes to humans through the bite. The vaccine has been established for this disease. Therefore, one of the best ways is to prevent the breeding of Aedes aegypti mosquitoes in the epidemic locations. Several studies of statistical modeling using the Bayesian paradigm have been carried out to analyze the pattern of the DHF cases. Generally, these studies are based on the data distribution, but the modeling does not take into account the stochastic dynamics between the Aedes aegypti mosquitoes and humans. This research develops the SIR-SI stochastic model, which is a system of differential equations that describes the dynamics of infection between the Aedes aegypti mosquitoes and humans. For the numerical simulation process using the Bayesian principle, we use the Euler method for the discretization process of the model. This model is arranged spatially and temporally by taking into account local and global random heterogeneity effects that represent the mobility of people. Combining both random effects in the SIR-SI model, we called the stochastic SIR-SI convolution model. We have demonstrated the model using monthly DHF cases in 10 districts of Kendari-Indonesia. The results show that Wua-Wua and Kadia districts are consistent as DHF high risk.

Keywords:

 

Bayesian, Convolution, DHF, SIR-SI, Random effect, Stochastic

References:

 

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Citations:

 

APA:
Mukhsar, Sani, A., Cahyono, E., Usman, I., & Abapihi, B. (2020). Mapping DHF Risk Using Bayesian Convolution Sir-Si Spatio-Temporal Model. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 138, 13-22. doi: 10.33832/ijast.2020.138.02.

MLA:
Mukhsar, et al. “Mapping DHF Risk Using Bayesian Convolution Sir-Si Spatio-Temporal Model.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 138, 2020, pp. 13-22. IJAST, http://article.nadiapub.com/IJAST/Vol138/2.html.

IEEE:
[1] Mukhsar, A. Sani, E. Cahyono, I. Usman, and B. Abapihi, "Mapping DHF Risk Using Bayesian Convolution Sir-Si Spatio-Temporal Model." International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 138, pp. 13-22, May 2020.