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

Endah R.M. Putri; Aldi E.W. Widianto; Amirul Hakam; Venansius R. Tjahjono; Hadi Susanto

doi:10.1007/978-981-97-2136-8_13

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

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

Mathematics

Institut Teknologi Sepuluh Nopember
Institut Teknologi Bandung

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	British English
Title of host publication	Applied and Computational Mathematics - ICoMPAC 2023
Editors	Dieky Adzkiya, Kistosil Fahim
Publisher	Springer
Pages	159-176
Number of pages	18
ISBN (Print)	9789819721351
DOIs	https://doi.org/10.1007/978-981-97-2136-8_13
State	Published - 2024
Event	8th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2023 - Lombok, Indonesia Duration: 30 Sep 2023 → 30 Sep 2023

Publication series

Name	Springer Proceedings in Mathematics and Statistics
Volume	455
ISSN (Print)	2194-1009
ISSN (Electronic)	2194-1017

Conference

Conference	8th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2023
Country/Territory	Indonesia
City	Lombok
Period	30/09/23 → 30/09/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Epidemic
Genetic algorithm
Geometric Brownian motion
Moving average

Access to Document

10.1007/978-981-97-2136-8_13

OpenUrl availability

Full text

Cite this

Putri, E. R. M., Widianto, A. E. W., Hakam, A., Tjahjono, V. R., & Susanto, H. (2024). A Moving Average Genetic Algorithm (MA-GA) for Estimating the COVID-19 Dynamic Based on a Stochastic SIRD Model. In D. Adzkiya, & K. Fahim (Eds.), Applied and Computational Mathematics - ICoMPAC 2023 (pp. 159-176). (Springer Proceedings in Mathematics and Statistics; Vol. 455). Springer. https://doi.org/10.1007/978-981-97-2136-8_13

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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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Putri, ERM, Widianto, AEW, Hakam, A, Tjahjono, VR & Susanto, H 2024, A Moving Average Genetic Algorithm (MA-GA) for Estimating the COVID-19 Dynamic Based on a Stochastic SIRD Model. in D Adzkiya & K Fahim (eds), Applied and Computational Mathematics - ICoMPAC 2023. Springer Proceedings in Mathematics and Statistics, vol. 455, Springer, pp. 159-176, 8th International Conference on Mathematics: Pure, Applied and Computation, ICoMPAC 2023, Lombok, Indonesia, 30/09/23. https://doi.org/10.1007/978-981-97-2136-8_13

A Moving Average Genetic Algorithm (MA-GA) for Estimating the COVID-19 Dynamic Based on a Stochastic SIRD Model. / Putri, Endah R.M.; Widianto, Aldi E.W.; Hakam, Amirul et al.
Applied and Computational Mathematics - ICoMPAC 2023. ed. / Dieky Adzkiya; Kistosil Fahim. Springer, 2024. p. 159-176 (Springer Proceedings in Mathematics and Statistics; Vol. 455).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Putri ERM, Widianto AEW, Hakam A, Tjahjono VR, Susanto H. A Moving Average Genetic Algorithm (MA-GA) for Estimating the COVID-19 Dynamic Based on a Stochastic SIRD Model. In Adzkiya D, Fahim K, editors, Applied and Computational Mathematics - ICoMPAC 2023. Springer. 2024. p. 159-176. (Springer Proceedings in Mathematics and Statistics). doi: 10.1007/978-981-97-2136-8_13