A CTMC-Based Parameter Estimation and Optimal Control Framework for Covid-19 Transmission
DOI:
https://doi.org/10.54074/jicsa.v1i02.29Keywords:
Model Covid-19 Model, Optimal Control, Parameter OptimizationAbstract
Covid-19 is a disease that infects the respiratory tract caused a corona virus. This disease has infected human populations around the world which causing a pandemic. The Covid-19 pandemic has become the focus of research on lately. This study aims to construct the latest mathematical model related to the transmission of Covid-19 in the human population. The model that is formed is constructed by considering several epidemiological parameters that are very identical to the actual conditions. In the formation of the model there are many unknown parameters. Therefore, the maximum likelihood method is used to estimate the parameters of the model that is formed. At the start the Covid-19 epidemic model was constructed without control with given assumptions and based on the facts obtained. Then from the model would formed equilibrium point, and basic reproduction number. The next discussion is to designed an optimal control using the Pontryagin Minimum Principle which is applied to reduce the number of people Covid-19 infected by using the backward-forward sweep algorithm. The results of the numerical simulation will later produce an optimal control strategy that is suitable to prevent Covid-19 to be more effective.
References
[1] P. B. Borah, D. Robidas, K. Dehingia, B. J. Nath, and H. K. Sarmah, “On the Dynamics of COVID-19 Propagation with Vaccination and Optimal Control Strategies,” Brazilian Journal of Physics 2025 55:3, vol. 55, no. 3, pp. 135-, Apr. 2025, doi: 10.1007/s13538-025-01769-y.
[2] P. J. Hotez et al., “Global public health security and justice for vaccines and therapeutics in the COVID-19 pandemic,” EClinicalMedicine, vol. 39, p. 101053, Sep. 2021, doi: 10.1016/j.eclinm.2021.101053.
[3] T. A. Prasetyo et al., “Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 34, no. 2, pp. 1353–1366, May 2024, doi: 10.11591/ijeecs.v34.i2.pp1353-1366.
[4] W. Dong, H. Yao, and W. N. Wang, “Study on the impact of COVID-19 epidemic and agent disease risk simulation model based on individual factors in Xi’an City,” Front. Cell. Infect. Microbiol., vol. 15, p. 1547601, 2025, doi: 10.3389/fcimb.2025.1547601.
[5] K. A. Kheirallah et al., “The Effect of Strict State Measures on the Epidemiologic Curve of COVID-19 Infection in the Context of a Developing Country: A Simulation from Jordan,” International Journal of Environmental Research and Public Health 2020, Vol. 17, Page 6530, vol. 17, no. 18, p. 6530, Sep. 2020, doi: 10.3390/ijerph17186530.
[6] Z. Zhang, A. Zeb, S. Hussain, and E. Alzahrani, “Dynamics of COVID-19 mathematical model with stochastic perturbation,” Advances in Difference Equations 2020 2020:1, vol. 2020, no. 1, pp. 451-, Aug. 2020, doi: 10.1186/s13662-020-02909-1.
[7] A. V. Okhuese, “Estimation of the Probability of Reinfection With COVID-19 by the Susceptible-Exposed-Infectious-Removed-Undetectable-Susceptible Model,” JMIR Public Health Surveill., vol. 6, no. 2, Jan. 2020, doi: 10.2196/19097.
[8] “Applied Artificial Intelligence An International Journal A Reinforcement Learning Based Decision Support Tool for Epidemic Control: Validation Study for COVID-19,” 2022, doi: 10.1080/08839514.2022.2031821.
[9] T. A. Prasetyo, R. Saragih, and D. Handayani, “Optimal control on the mathematical models of dengue epidemic by giving vaccination and repellent strategies,” J. Phys. Conf. Ser., vol. 1490, no. 1, p. 012034, Mar. 2020, doi: 10.1088/1742-6596/1490/1/012034.
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