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Heydari Dastjerdi, Rasoul, Ahmadi, Ghasem, Yari, Ayatollah, Dadkhah, Mahmood. (1401). Application of Pontryagin's Minimum Principle in the Artificial Neural Network to Reduce the COVID-19 Pandemic Effects. سامانه مدیریت نشریات علمی, 9(4), 219-228. doi: 10.30476/jhmi.2023.96823.1159
Rasoul Heydari Dastjerdi; Ghasem Ahmadi; Ayatollah Yari; Mahmood Dadkhah. "Application of Pontryagin's Minimum Principle in the Artificial Neural Network to Reduce the COVID-19 Pandemic Effects". سامانه مدیریت نشریات علمی, 9, 4, 1401, 219-228. doi: 10.30476/jhmi.2023.96823.1159
Heydari Dastjerdi, Rasoul, Ahmadi, Ghasem, Yari, Ayatollah, Dadkhah, Mahmood. (1401). 'Application of Pontryagin's Minimum Principle in the Artificial Neural Network to Reduce the COVID-19 Pandemic Effects', سامانه مدیریت نشریات علمی, 9(4), pp. 219-228. doi: 10.30476/jhmi.2023.96823.1159
Heydari Dastjerdi, Rasoul, Ahmadi, Ghasem, Yari, Ayatollah, Dadkhah, Mahmood. Application of Pontryagin's Minimum Principle in the Artificial Neural Network to Reduce the COVID-19 Pandemic Effects. سامانه مدیریت نشریات علمی, 1401; 9(4): 219-228. doi: 10.30476/jhmi.2023.96823.1159

Application of Pontryagin's Minimum Principle in the Artificial Neural Network to Reduce the COVID-19 Pandemic Effects

Health Management & Information Science
مقاله 4، دوره 9، شماره 4، اسفند 2022، صفحه 219-228    اصل مقاله (963.4 K)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30476/jhmi.2023.96823.1159
نویسندگان
Rasoul Heydari Dastjerdi* 1؛ Ghasem Ahmadi2؛ Ayatollah Yari2؛ Mahmood Dadkhah3
1Department of Mathematics, Payame Noor University ,Tehran,Iran
2Department of Mathematics, Payame Noor University, Tehran, Iran
3Department of mathematics, PayameNoor University, Tehran, Iran
چکیده
Introduction: In this study, we analyze the optimal intervention strategies that lead to reducing the effects of the COVID-19 pandemic by artificial neural networks (ANNs). Our aim is to investigate the effects of optimal control strategies, such as the implementation of government intervention, testing, and vaccination policies during outbreaks.
Methods: We utilized a controlled SIDAREV model to study the progression of the COVID-19 pandemic. Using Pontryagin's minimum principle (PMP) for the SIDAREV model, we defined an unconstrained minimization problem. Applying the Hamiltonian conditions, we approximated the obtained ordinary differential equations (ODE) using ANNs. We utilized the multilayer perceptron (MLP) to obtain the approximate solution of the states and co-states functions.
Results: We observed the effects of optimal control strategies, and to show the efficiency of the proposed method, we compared it with the Runge-Kutta method through some examples.
Conclusion: Using a mathematical model that simulates the behavior of the Covid-19 disease, we can examine the effects of controllers such as government interventions, tests and vaccinations with the neural network method. The results show that this method is useful in solving the problem of optimal control of infectious diseases.
کلیدواژه‌ها
Optimal control, Pontryagin's minimum principle, Artificial neural network؛ SIDAREV model, COVID-19
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