PENENTUAN FAKTOR-FAKTOR POTENSIAL YANG MEMPENGARUHI KEJADIAN MALARIA DI PROVINSI PAPUA DENGAN EPIDEMIOLOGI SPASIAL
DOI:
https://doi.org/10.29244/ijsa.v4i3.681Keywords:
Conditional autoregressive, epidemiology, malaria, simultaneous autoregressive, spatialAbstract
In Indonesia malaria is found to be widespread in all islands with varying degrees and severity of infection. Based on the Annual of Parasite Incidence (API) in Eastern Indonesia, Malaria is a disease that has a high incidence rate. The three provinces with the highest APIs are Papua (42.64%), West Papua (38.44%) and East Nusa Tenggara (16.37%). Spatial aspects are considered important to be studied because the spread of disease through mosquitoes is strongly influenced by fluctuating climate. The purpose of this study is to determine the potential factors that influence the incidence of Malaria disease in the province of Papua in 2013 by looking at aspects that are the focus of attention in spatial epidemiology. The methods used in analyzing the area are Simultaneous Autoregressive (SAR) and Conditional Autoregressive (CAR) models with a spatial weighting matrix up to second order. The result shows the average monthly wind velocity, average monthly rainfall, and malaria treatment with government program drugs by getting ACT drugs are substantial factors in determining the incidence number of Malaria in Papua based on the lowest AIC value for the second-order of CAR model. While the SAR model, in this case, has no spatial influence. By knowing the potential factors that influence the incidence of malaria, the Papua Province through the Health Office can take more effective preventive measures to reduce the number of malaria incidents.
Downloads
References
Arsin, A. A. (2012). Malaria di Indonesia tinjauan aspek epidemiologi. Makassar (ID): Masagena.
[BPS] Badan Pusat Statistika. (2014). Provinsi Papua dalam angka 2014. Papua (ID): BPS Papua.
Cressie, N. (1990). Statistics for Spatial Data. New York (US): John Wiley & Sons.
De Oliveira, V. (2012). Bayesian analysis of conditional autoregressive models. Annals of the Institute of Statistical Mathematics, 64(1): 107–133.
Febriyanti, R. D., & Suwadi, J. F. (2019). Aktivitas Antimalaria Senyawa Tanaman Daun Kapur (Harmsiopanax aculeatus) terhadap Plasmodium sp. Jurnal Medula, 3(9): 465–471.
[KEMENKES RI] Kementerian Kesehatan Republik Indonesia. (2014). Profil Kesehatan Indonesia Tahun 2013. Jakarta (ID): Lembaga Penerbitan Badan Litbangkes.
[KEMENKES RI] Kementerian Kesehatan RepublikIndonesia. (2013). Riset kesehatan dasar dalam angka (RISKESDAS 2013) Provinsi Papua. Jakarta (ID): Lembaga Penerbitan Badan Litbangkes.
Siswanto, Aidi, M. N., & Djuraidah, A. (2017). Conditional Autoregressive (CAR) Modeling Uses Weighted Matrix to First and Second Order (Case Study: Malaria Disease in Papua Province). International Journal of Engineering and Management Research (IJEMR), 7(4): 297–301.
Wall, M. M. (2004). A close look at the spatial structure implied by the CAR and SAR models. Journal of Statistical Planning and Inference, 2(121): 311–324.
Waller, L. A. (2005). Applied Spatial Statistics for Public Health Data. New York (US): John Wiley & Sons.
