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2024 | OriginalPaper | Chapter

A Moving Average Genetic Algorithm (MA-GA) for Estimating the COVID-19 Dynamic Based on a Stochastic SIRD Model

Authors : Endah R. M. Putri, Aldi E. W. Widianto, Amirul Hakam, Venansius R. Tjahjono, Hadi Susanto

Published in: Applied and Computational Mathematics

Publisher: Springer Nature Singapore

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Abstract

In this study, we examine the transmission of the COVID-19 outbreak using a constructed SIRD stochastic model. To determine the most appropriate model parameters, three stochastic models are proposed, and genetic algorithms (GA) are employed. However, the standard GA has proven inadequate in obtaining suitable parameters for the model, leading to occasional discrepancies in tracking trends from actual case data. To overcome this limitation, we propose a novel modification of the genetic algorithm, termed the Moving Average Genetic Algorithm (MA-GA). Unlike the standard GA, our MA-GA continuously updates the parameters at predetermined intervals, resulting in significantly improved accuracy. By applying this method, we achieve higher precision in providing solutions for the stochastic SIRD model, thereby enhancing its ability to accurately reflect the real-world dynamics of the COVID-19 outbreak.

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Alanazi, S.A., Kamruzzaman, M., Alruwaili, M., Alshammari, N., Alqahtani, S.A., Karime, A.: Measuring and preventing COVID-19 using the SIR model and machine learning in smart health care. J. Healthc. Eng. (2020)

Calafiore, G.C., Novara, C., Possieri, C.: A time-varying SIRD model for the COVID-19 contagion in Italy. Ann. Rev. Control (2020)

Chen, H., Cox, S.H.: An option-based operational risk management model for pandemics. North Am. Actuarial J. 13(1), 54–76 (2009)MathSciNetCrossRef

Christensen, P.A., Olsen, R.J., Long, S.W., Snehal, R., Davis, J.J., Saavedra, M.O., Reppond, K., Shyer, M.N., Cambric, J., Gadd, R., et al.: Signals of significantly increased vaccine breakthrough, decreased hospitalization rates, and less severe disease in patients with COVID-19 caused by the Omicron variant of SARS-CoV-2 in Houston, Texas. Am. J. Pathol. (2022)

Cooper, I., Mondal, A., Antonopoulos, C.G.: A SIR model assumption for the spread of covid-19 in different communities. Chaos Solitons Fractals 139, 110057 (2020)MathSciNetCrossRef

Dieu, N.T., Nguyen, D.H., Du, N.H., Yin, G.: Classification of asymptotic behavior in a stochastic sir model. SIAM J. Appl. Dyn. Syst. 15(2), 1062–1084 (2016)MathSciNetCrossRef

Donthu, N., Gustafsson, A.: Effects of covid-19 on business and research. J. Bus. Res. 117, 284–289 (2020)CrossRef

El Koufi, A., Adnani, J., Bennar, A., Yousfi, N.: Analysis of a stochastic SIR model with vaccination and nonlinear incidence rate. Int. J. Differ. Equ. (2019)

Ferguson, N.M., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Bhatia, S., Boonyasiri, A., Cucunubá, Z., Cuomo-Dannenburg, G., et al.: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand (2020)

10.

Gray, A., Greenhalgh, D., Hu, L., Mao, X., Pan, J.: A stochastic differential equation SIS epidemic model. SIAM J. Appl. Math. 71(3), 876–902 (2011)MathSciNetCrossRef

11.

Hasan, A., Putri, E.R., Susanto, H., Nuraini, N.: Data-driven modeling and forecasting of COVID-19 outbreak for public policy making. ISA Trans. (2021)

12.

Hasan, A., Susanto, H., Tjahjono, V., Kusdiantara, R., Putri, E., Nuraini, N., Hadisoemarto, P.: A new estimation method for COVID-19 time-varying reproduction number using active cases. Sci. Rep. 12(1), 1–9 (2022)CrossRef

13.

Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. Proc. R. Soc. London. Ser. A Containing Pap. Math. Phys. Charact. 115(772), 700–721 (1927)

14.

McCall, J.: Genetic algorithms for modelling and optimisation. J. Comput. Appl. Math. 184(1), 205–222 (2005)MathSciNetCrossRef

15.

Miao, A., Zhang, T., Zhang, J., Wang, C.: Dynamics of a stochastic SIR model with both horizontal and vertical transmission. J. Appl. Anal. Comput 8(4), 1108–1121 (2018)MathSciNet

16.

Ndaïrou, F., Area, I., Nieto, J.J., Torres, D.F.: Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan. Chaos Solitons Fractals 135, 109846 (2020)MathSciNetCrossRef

17.

Putri, E.R., Iqbal, M., Shahab, M.L., Fadhilah, H.N., Mukhlash, I., Arif, D.K., Apriliani, E., Susanto, H.: COVID-19 outbreak prediction in Indonesia based on machine learning and SIRD-based hybrid methods. Nonlinear Dyn. Syst. Theory 21(5), 495 (2021)MathSciNet

18.

Rahimi, I., Gandomi, A.H., Asteris, P.G., Chen, F.: Analysis and prediction of COVID-19 using SIR, SEIQR and machine learning models: Australia, Italy and UK cases. Information 12(3), 109 (2021)CrossRef

19.

Rihan, F.A., Alsakaji, H.J., Rajivganthi, C.: Stochastic SIRC epidemic model with time-delay for covid-19. Adv. Differ. Equ. 2020(1), 1–20 (2020)MathSciNetCrossRef

20.

Tjahjono, V.R., Kurniawan, H., Hakam, A., Putri, E.R., Susanto, H., et al.: Epidemic modeling of COVID-19 in the ASEAN countries using a genetic partial fitting algorithm with the presence of a second wave. J. Appl. Sci. Eng. 24(6), 900–914 (2021)

21.

Tornatore, E., Buccellato, S.M., Vetro, P.: Stability of a stochastic SIR system. Phys. A 354, 111–126 (2005)CrossRef

Title: A Moving Average Genetic Algorithm (MA-GA) for Estimating the COVID-19 Dynamic Based on a Stochastic SIRD Model
Authors: Endah R. M. Putri
Aldi E. W. Widianto
Amirul Hakam
Venansius R. Tjahjono
Hadi Susanto
Publisher: Springer Nature Singapore
Book: Applied and Computational Mathematics
Print ISBN: 978-981-9721-35-1

Electronic ISBN: 978-981-9721-36-8

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-97-2136-8_13

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