A COMPARISON OF LOGISTIC REGRESSION AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) ON COVID-19 DATA IN WEST SUMATRA

  • Irvanal Haq Department of Statistics, FMIPA, IPB University, Indonesia
  • Muhammad Nur Aidi Department of Statistics, FMIPA, IPB University, Indonesia
  • Anang Kurnia Department of Statistics, FMIPA, IPB University, Indonesia
  • Efriwati Efriwati National Research and Innovation Agency (BRIN), Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss3pp1749-1760
Keywords: Covid-19, Logistic Regression, Sungkai, West Sumatra, GWLR

Abstract

An understanding of factors that affect the recovery time from a disease is important for the community, medical staff, and also the government. This research analyzed factors that affect the recovery time of Covid-19 sufferers in West Sumatra. In addition, the consumption of a herbal made from Sungkai leaves, which is believed by some people in West Sumatra to accelerate the healing from Covid-19, was also included in the analysis. The recovery time here was categorized into two classes (binary): 1 for within 2 weeks, and 0 for more than 2 weeks. The methods used were logistic regression and geographically weighted logistic regression (GWLR). GWLR provides estimates of parameters for each location. The data used in this study is Covid-19 data of 2021 taken from the Regional Research and Development Agency (Litbangda) of West Sumatra with a total of 764 observations collected from 19 regencies/cities in West Sumatra. The results showed that there was no difference between the logistic regression model and the GWLR models based on the values of AIC and the ratio of deviance and degrees of freedom (df). The addition of spatial factors through GWLR models did not provide additional information regarding the recovery of Covid-19 sufferers within 2 weeks or more than 2 weeks. The logistic regression model gives the result that, at significance level α = 10%, residence, vaccination status, and symptoms significantly affect the recovery time within 2 weeks or more for Covid-19 sufferers, while other variables, namely sex, age, Sungkai leaves consumption status, and ginger consumption status have no significant effects.

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References

T. Singhal, “A Review of Coronavirus Disease-2019 (COVID-19),” Indian J. Pediatr., vol. 87, no. April 2020, pp. 281–286, 2020.

T. Alam and S. Qamar, “Coronavirus Disease (COVID-19): Reviews, Applications, and Current Status,” J. Inform. Univ. Pamulang, vol. 5, no. 3, p. 213, 2020, doi: 10.32493/informatika.v5i3.6563.

N. Harlina, “Kilas Balik Satu Tahun Covid-19 di Sumbar,” Liputan6, Padang, Mar. 27, 2021. [Online]. Available: liputan6.com/regional/read/4516063/kilas-balik-satu-tahun-covid-19-di-sumbar? [Diakses: 9 Maret 2022].

N. Harlina, “Tanggapan Gubernur Sumbar Diberi ‘Rapor Merah’ dalam Penanganan Covid-19,” Liputan6, Padang, 2021. [Online]. Available: https://www.liputan6.com/regional/read/4627595/tanggapan-gubernur-sumbar-diberi-rapor-merah-dalam-penanganan-covid-19 [Diakses: 9 Maret 2022].

World Health Organization, “Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19),” 2020. [Online]. Available: https://www.who.int/docs/default-source/coronaviruse/who-china-joint-mission-on-covid-19-final-report.pdf.

A. W. Kaso, H. E. Hareru, T. Kaso, and G. Agero, “Time to recovery from Covid-19 and its associated factors among patients hospitalized to the treatment center in South Central Ethiopia,” Environ. Challenges, vol. 6, no. December 2021, p. 100428, 2022, doi: 10.1016/j.envc.2021.100428.

E. Zandkarimi, “Factors affecting the recovery of Kurdistan province COVID-19 patients: A cross-sectional study from March to June 2020,” Epidemiol. Method., vol. 10, no. s1, pp. 1–6, 2021, doi: 10.1515/em-2020-0041.

A. Rahman et al., “Pengaruh Pemberian Infusa Daun Sungkai ( Peronema canescens ) Terhadap Jumlah Leukosit Pada Mencit,” J. Healthc. Technol. Med., vol. 7, no. 2, pp. 614–620, 2021.

M. A. Santika and Y. Karyana, “Analisis Regresi Logistik Biner dengan Efek Interaksi untuk Memodelkan Angka Fertilitas Total di Jawa Barat,” Bandung Conf. Ser. Stat., vol. 2, no. 2, pp. 142–151, 2022, doi: 10.29313/bcss.v2i2.3555.

M. Rodrigues, J. de la Riva, and S. Fotheringham, “Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weightedlogistic regression,” Appl. Geogr., vol. 48, pp. 52–63, 2014, doi: 10.1016/j.apgeog.2014.01.011.

M. Fathurahman, Purhadi, Sutikno, and V. Ratnasari, “Pemodelan Geographically Weighted Logistic Regression pada Indeks Pemodelan Geographically Weighted Logistic Regression pada Indeks Pembangunan Kesehatan Masyarakat di Provinsi Papua,” in Prosiding Seminar Nasional MIPA 2016, 2016, pp. 34–42. [Online]. Available: https://www.researchgate.net/publication/324690057

N. A. Solekha and M. F. Qudratullah, “Pemodelan Geographically Weighted Logistic Regression dengan Fungsi Adaptive Gaussian Kernel Terhadap Kemiskinan di Provinsi NTT,” Jambura J. Math., vol. 4, no. 1, pp. 17–32, 2022, doi: 10.34312/jjom.v4i1.11452.

F. N. Nkeki and M. O. Asikhia, “Geographically weighted logistic regression approach to explore the spatial variability in travel behaviour and built environment interactions: Accounting simultaneously for demographic and socioeconomic characteristics,” Appl. Geogr., vol. 108, no. October 2018, pp. 47–63, 2019, doi: 10.1016/j.apgeog.2019.05.008.

D. W. Hosmer, S. Lemeshow, and R. X. Sturdivant, Applied Logistic Regression., 3rd ed. Hoboken: John Wiley & Sons, Inc., 2013.

A. Agresti, An Introduction to Categorical Data Analysis, 3rd ed. Hoboken: John Wiley & Sons, Inc., 2019.

R. Febrianti, Y. Widyaningsih, and S. Soemartojo, “The parameter estimation of logistic regression with maximum likelihood method and score function modification,” J. Phys. Conf. Ser., vol. 1725, no. 1, 2021, doi: 10.1088/1742-6596/1725/1/012014.

R. E. Caraka and H. Yasin, Geographically Weighted Regression (GWR): Sebuah Pendekatan Regresi Geografis. Yogyakarta: Mobius, 2017.

A. Djuraidah, Monograph Penerapan dan Pengembangan Regresi Spasial dengan Studi Kasus pada Kesehatan, Sosial, dan Ekonomi. Bogor: IPB Press, 2020.

Wulandari, “Geographically Weighted Logistic Regression Dengan Fungsi Kernel Fixed Gaussian Pada Kemiskinan Jawa Tengah,” Indones. J. Stat. Its Appl., vol. 2, no. 2, pp. 101–112, 2018, doi: 10.29244/ijsa.v2i2.189.

C. Madlock-Brown et al., “Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses,” BMC Public Health, vol. 22, no. 1, pp. 1–13, 2022, doi: 10.1186/s12889-022-13168-y.

H. Surkyn, D. Sandra, and R. Vandekerckhove, “Adolescents and Verb Spelling: The Impact of Gender and Educational Track on Rule Knowledge and Linguistic Attitudes,” Dutch J. Appl. Linguist., vol. 11, pp. 1–19, 2022, doi: 10.51751/dujal10932.

Published
2023-09-30
How to Cite
[1]
I. Haq, M. Aidi, A. Kurnia, and E. Efriwati, “A COMPARISON OF LOGISTIC REGRESSION AND GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION (GWLR) ON COVID-19 DATA IN WEST SUMATRA”, BAREKENG: J. Math. & App., vol. 17, no. 3, pp. 1749-1760, Sep. 2023.
Issue
Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Section
Articles

Copyright (c) 2023 Irvanal Haq, Muhammad Nur Aidi, Anang Kurnia, Efriwati Efriwati

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