Estimasi time reproduction number penyebaran awal COVID-19 di Nusa Tenggara Timur, Indonesia

Astri Atti

doi:10.34312/jjbm.v3i2.17652

Estimasi time reproduction number penyebaran awal COVID-19 di Nusa Tenggara Timur, Indonesia

Astri Atti

Abstract

COVID-19 has become a threat to human life. Since the beginning of its spread, the number of deaths due to COVID-19 has reached millions of people and has spread throughout the world including East Nusa Tenggara. To understand the dynamics of the spread of COVID-19 is to use a mathematical model and the indicator used to measure the level of spread is the reproduction number. In this paper, the estimation of the reproduction number (time reproduction number) is carried out using the Extended Kalman Filter (EkF) technique. The data used is daily data for COVID-19 patients from August to October 2021. The estimation results show that the reproduction number of COVID-19 at the beginning of its spread is above one. This means that COVID-19 is spreading in the population.

Keywords

Covid-19; Time Reproduction Number; Extended Kalman Filter

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References

World Health Organization, Coronavirus disease (COVID-19) pandemic, 2021.

M. A. Billah, M. M. Miah, and M. N. Khan, “Reproductive number of coronavirus: A systematic review and meta-analysis based on global level evidence,” PLos One, vol. 15, no. 11, p. e0242128, 2020. DOI: 10.1371/journal.pone.0242128

N. Anggriani, M. Z. Ndii, R. Amelia, W. Suryaningrat, and M. A. A. Pratama, “A mathematical COVID-19 model considering asymptomatic and symptomatic classes with waning immunity,” Alexandria Engineering Journal, vol. 61, no. 1, pp. 113–124, 2022. DOI: 10.1016/j.aej.2021.04.104

D. Aldila, M. Z. Ndii, and B. M. Samiadji, “Optimal control on COVID-19 eradication program in Indonesia under the effect of community awareness,” Mathematical Biosciences and Engineering, vol. 17, no. 6, pp. 6355–6389, 2020. DOI: 10.3934/mbe.2020335

M. Z. Ndii, P. Hadisoemarto, D. Agustian, and A. K. Supriatna, “An analysis of Covid-19 transmission in Indonesia and Saudi Arabia,” Communication in Biomathematical Sciences, vol. 3, no. 1, pp. 19-27, 2020. DOI: 10.5614/cbms.2020.3.1.3

K. Sarkar, S. Khajanchi, and J. J. Nieto, “Modeling and forecasting the COVID-19 pandemic in India,” Chaos, Solitons & Fractals, vol. 139, p. 110049, 2020. DOI: 10.1016/j.chaos.2020.110049

W. Zhou, A. Wang, F. Xia, Y. Xiao, and S. Tang, “Effects of media reporting on mitigating spread of COVID-19 in the early phase of the outbreak,” Mathematical Biosciences and Engineering, vol. 17, no. 3, pp. 2693–2707, 2020. DOI: 10.3934/mbe.2020147

M. Z. Ndii, Pemodelan matematika dinamika populasi dan penyebaran penyakit teori, aplikasi, dan numerik. Deepublish, 2018. ISBN 9786232090354.

A. Hasan and Y. Nasution, “A compartmental epidemic model incorporating probable cases to model covid-19 outbreak in regions with limited testing capacity,” ISA Transactions, vol. 124, pp. 157–163, 2022. DOI: 10.1016/j.isatra.2021.01.029

M. Z. Ndii, L. K. Beay, N. Anggriani, K. N. Nukul, and B. S. Djahi, “Estimating the Time Reproduction Number in Kupang City Indonesia, 2016–2020, and Assessing the Effects of Vaccination and Different Wolbachia Strains on Dengue Transmission Dynamics,” Mathematics, vol. 10, no. 12, p. 2075, 2022. DOI: 10.3390/math10122075

S. M. Isman, “Penerapan metode extended kalman filter untuk estimasi transmisi filariasis.” Skripsi, Jurusan Matematika Fakultas Matematika dan Ilmu Pengetahuan Alam Institut Teknologi Sepuluh Nopember Surabaya, 2016.

DOI: https://doi.org/10.34312/jjbm.v3i2.17652

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