Modeling Determinant of COVID-19 Mortality in Indonesia

M. Fajar¹ , S. Magdhalena² , S. Hartini³ , Z. Nurfalah⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.6 , Issue.9 , pp.17-21, Sep-2020

Online published on Sep 30, 2020

Copyright © M. Fajar, S. Magdhalena, S. Hartini, Z. Nurfalah . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This study aims to examine the determinants of mortality-related to COVID-19 in Indonesia. Generalized additive models (GAM) was used for modeling the relationship between COVID-19-related deaths and predictor variables. Information used in this study was sourced from Badan Pusat Statistik (BPS Statistics Indonesia), Ministry of Health, and the Indonesian COVID-19 Task Force. The results obtained from GAM are statistically valid. Out of the eight predicting variables used in the analysis, six were significant and two were non-significant at 95 percent confidence interval. The significant variables are GRDP per capita, the proportion of population aged 60 years and over, life expectancy at birth, number of hospitals, number of people with tuberculosis, and number of diabetics. The model can explain the variation of COVID-19-related deaths by 98.5 percent, while the remaining 1.5 percent is attributed to other factors lying outside the model. In summary, this study suggests increasing the number of health facilities, carrying out health development programs, implementing health protocols, and mobility restrictions with prioritizing populations of vulnerable age or those with comorbidities can reduce mortality-related to COVID-19.

Key-Words / Index Term :
GAM, Covid-19, Determinant, Modeling Mortality, Indonesia

References :
[1] C. de Boor, “A Practical Guide to Splines, Revised ed,” Springer, Berlin, pp: 109-128, 2001.
[2] G. Deng, M. Yin, X. Chen, and F. Zeng, “Clinical determinants for fatality of 44,672 patients with COVID-19,” Critical Care, 24(1), 1–3, 2020.
[3] G. Onder, G. Rezza and S. Bruseferro, “Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy,”,JAMA. Published online March 23, 2020.
[4] H. Sahoo, C. Mandal, S. Mishra, and S. Banerjee, “Burden of COVID-19 pandemic in India: Perspective from Health Infrastructure,” 2020.
[5] P. Eilers and B. Marx, “Flexible Smoothing with B-Splines and Penalties,”,Statistical Science, vol.11, no.2, pp. 89-102, 1996.
[6] S.N. Wood, “Generalized Additive Models: An Introduction with R 2nd edition,” Chapman & Hall/CRC, pp. 161-191, 2017
[7] S. Dan, D. Sharma, M. Mandal, D. Sharma, “Incidence of COVID-19 and Its Correlation between Temperature and Population Density,” International Journal of Scientific Research in Biological Sciences, Vol.7, Issue.2, pp.134-141, 2020.
[8] T.J. Hastie and R.J. Tibshirani, “Generalized Additive Models,” Chapman & Hall, pp: 136-166, 1990.
[9] V. Stojkoski, Z. Utkovski, P. Jolakoski, D. Tevdovski, and L. Kocarev, “The Socio-Economic Determinants of the Coronavirus Disease (COVID-19) Pandemic,” SSRN Electronic Journal, April, 1–22, 2020.
[10] W.J. Guan, W.H. Liang, Y. Zhao, H.R. Liang, Z.S. Chen, Y.M. Li, X.Q. Liu, R.C. Chen, C.L. Tang, T. Wang, C.Q. Ou, L. Li, P.Y. Chen, L. Sang, W. Wang, J.F. Li, C.C. Li, L.M. Ou, B. Cheng, S. Xiong, Z.Y. Ni, J. Xiang, Y. Hu, L. Liu, H. Shan, C.L. Lei, Y.X. Peng, L. Wei, Y. Liu, Y.H. Hu, P. Peng, J.M. Wang, J.Y. Liu, Z. Chen, G. Li, Z.J. Zheng, S.Q. Qiu, J. Luo, C.J. Ye, S.Y. Zhu, L.L. Cheng, F. Ye, S.Y. Li, J.P. Zheng, N.F. Zhang, N.S. Zhong, and J.X. He, “Comorbidity and its impact on 1,590 patients with Covid-19 in China: A nationwide analysis. European Respiratory Journal, 55(5), 2020.