Analysis The Influence of Climate Factors With COVID-19 Recovery Rates in DKI Jakarta

Dimas Teguh Ramadhan, Shofiyah Calista Ricadonna, Syalwa Rastia Kaldi

Abstract


COVID-19 is a disease caused by the SARS-CoV-2 virus.  The ideal air for the growth of this virus is at a temperature of around 8-10°C with a relative humidity of 60-90%.  From March 2020 to December 2021, there were 7,709,036 cases of recovering positive COVID-19 patients. One of the factors thought to affect the rate of recovery from COVID-19 is climate factors, including the average temperature, average relative humidity of the air, rainfall, length of time.  solar radiation, and average wind speed.  The cure rate for COVID-19 in DKI Jakarta tends to increase from May 2020 to July 2020 and decrease from July 2020 to December 2020. This research was carried out with a spatial analysis using the Poisson regression, negative binomial and generalized Poisson regression methods.  Based on the AIC value model, the most suitable model for this model is the Generalized Poisson Regression.  From this study, it was concluded that the climatic factors that had a significant effect on the recovery rate of COVID-19 in DKI Jakarta were the average temperature, the average relative humidity of the air, the duration of sunlight, and the average wind speed.

Keywords


Poisson regression method; DKI Jakarta; COVID-19; Climate Factor

Full Text:

PDF

References


N. Zhu, D. Zhang, W. Wang, X. Li, B. Yang, J. Song, X. Zhao, B. Huang, W. Shi, R. Lu, P. Niu, F. Zhan, X. Ma, D. Wang, W. Xu, G. Wu, G.F. Gao, and W. Tan, “A Novel Coronavirus from Patients with Pneumonia In China,” New England Journal of Medicine, vol. 382, no. 8, pp. 727–733, 2020, https://doi.org/10.1056/nejmoa2001017

Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, Z. Feng, et al, “Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia”, New England Journal of Medicine, vol. 382, no. 13, pp. 1199-1207, 2020, doi:10.1056/nejmoa2001316

Y. Wang, Y. Chen, and Q. Qin, “Unique Epidemiological and Clinical Features of The Emerging 2019 Novel Coronavirus Pneumonia (COVID‐19) Implicate Special Control Measures,” Journal of Medical Virology, vol. 92, no. 6, pp. 568-576, 2020, doi:10.1002/jmv.25748

Ministry of Health Republic of Indonesia, 2020b, “Guidelines for the Prevention and Control of Corona Virus Disease (COVID-19) July 2020,” 13 July 20, Jakarta: Ministry of Health Republic of Indonesia.

World Health Organization, 2020, “Timeline of WHO’s response to COVID-19”, Newsroom. Available: https://www.who.int/news-room/detail/29- 06-2020-covidtimeline [Accessed at 8 April 2022].

World Health Organization, “Climate change and human health: risks and responses: summary,” 2003, World Health Organization. Available: https://apps.who.int/iris/handle/10665/42749

P. Shi, Y. Dong, H. Yan, C. Zhao, X. Li, et al, “Impact Of Temperature On The Dynamics Of The Covid-19 Outbreak In China,” Science Of The Total Environment, vol. 728, no. 138890, 2020, doi: 10.1016/j.scitotenv.2020.138890

R. Tosepu, J. Gunawan, D.S. Effendy, L.O Ahmad, H. Lestari, H. Bahar, and P. Asfian, “Correlation between weather and COVID-19 pandemic in Jakarta, Indonesia,” Science of The Total Environment, vol. 725, no. 138436, 2020, doi: 10.1016/j.scitotenv. 2020.138436

R.H. Myers, Classical and Modern Regression with Application, PWS-KENT Publishing Company, 1990.

W. W. Hines, “Probability And Statistics In Engineering and Management Science,” Hoboken, New Jersey: John Wiley and Sons, 2003.

L. Amaliana, “Geographically Weighted Zero Inflated Poisson Regression Model, Case Study: The Number of Filariasis Cases in East Java 2012,” S,Si Thesis, Institut Teknologi Sepuluh Nopember, Surabaya, 2014.

J, M. Hilbe, Negatif Binomial Regression, 2nd ed. Cambridge: Cambridge University Press, 2011, doi: 10.1017/cbo9780511973420

Mc P. Cullagh and J.A. Nelder, Generalized Linier Models, 2nd ed. Chapman and Hall, London, 1989, doi: 10.1007/978-1-4899-3242-6

H.A. Park, “An Introduction To Logistic Regression: From Basic Concepts To Interpretation With Particular Attention To Nursing Domain,” Journal of Korean Academy of Nursing, vol. 43, no. 2, pp. 154, 2013, https://doi.org/10.4040/jkan.2013.43.2.154

Marlene Müller, Generalized Linear Model: Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserlauten, 2014.

F. Fadhillah, “Aplikasi Regresi Binomial Negatif Dan Generalized Poisson Dalam Mengatasi Overdispersion Pada Regresi Poisson (Studi Kasus Data Kemiskinan Provinsi di Indonesia Tahun 2009),” Institutional repository Uin Syarif Hidayatullah Jakarta, 2019, Available: https://repository.uinjkt.ac.id/dspace/

S. Abdullah, S. Susilo, S. Ula, A. Aswata, N. Valentika, and S. I. Chasanah, “Algoritma Membangkitkan Proses Poisson Majemuk Dengan Komponen Proses Poisson Nonhomogen Fungsi Linear dan Komponen Berdistribusi Eksponensial,” Statmat: Jurnal Statistika Dan Matematika, vol. 2, no. 1, pp. 81, https://doi.org/10.32493/sm.v2i1.4224

H. Ihsan. W. Sanusi, and R. Ulfadwiyanti, “Model Generalized Poisson Regression (GPR) dan Penerapannya pada Angka Pengangguran bagi Penduduk Usia Kerja di Provinsi Sulawesi Selatan,” Journal of Mathematics, Computations, and Statistics, 2020, pp. 109 – 117.

Jakarta Tanggap COVID-19, (n.d), Retrieved April 8, 2022, Available: https://corona.jakarta.go.id/id

BPS provinsi DKI Jakarta, (n.d.), Retrieved April 8, 2022, Available: https://jakarta.bps.go.id/publication/2021/02/26/bb7fa6dd5e90b534e3fa6984/provinsi-dki-jakarta-dalam-angka-2021.html

BPS provinsi DKI Jakarta, (n.d.), Retrieved April 8, 2022, Available: https://jakarta.bps.go.id/publication/2022/02/25/5979600247867d861a1f334c/provinsi-dki-jakarta-dalam-angka-2022.html


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.


Editorial Office:

President University Actuarial Science: Jalan Ki Hajar Dewantara, Mekarmukti, Cikarang Utara, Bekasi 17530