APLIKASI PERSAMAAN DIFERENSIAL STOKASTIK PADA MASALAH KONTROL OPTIMUM BERKENDALA
Abstract
Penyebaran virus Covid-19 adalah salah satu fenomena yang dapat dimodelkan. Pada penelitian ini dibahas tentang kontrol optimal pada model stokastik dari penyebaran virus Covid 19 yang terdiri atas tiga populasi yaitu rentan (Susceptible), Terinfeksi (Infected) dan pulih (Recovered). Adanya kondisi fisik yang berbeda tiap individu dan perubahan perilaku dari individu menjadi alasan penyusunan model stokastik ini. Pada penelitian ini model penyebaran virus Covid-19 diasumsikan dapat dikendalikan oleh pemerintah dengan pemberian kontrol vaksinasi dan isolasi dengan tujuan meminimalkan jumlah individu terinfeksi dan biaya vaksinasi. Model dikonstruksikan berdasarkan 2 kasus yaitu Model I adalah model dengan vaksinasi dan model II adalah model dengan isolasi. Kedua model tersebut akan dianalisis dengan menerapkan Prinsip Stokastik Maksimum. Prinsip ini dilakukan terhadap Hamiltonian dari persamaan optimal yang terbentuk untuk menentukan kontrol optimalnya.
Kata Kunci : Covid-19, Vaksinasi, Isolasi, Hamiltonian, Prinsip Stokastik Maksimum.
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References
World Health Organization. WHO Characterizes COVID-19 as a pandemic, 2020. https://www.who.int/dg/speeches/detail/who-director-general-opening-remarks-at-the-media-briefing-on-covid-19-11 march 2020
Keputusan Presiden RI No. 12 Tahun 2020 tentang penetapan Bencana Non Alam Penyebaran Virus Corona sebagai Bencana Nasional. 13 April 2020
C. I. Siertos and L. Russo. Mathematical modelling of Infectious disease dynamics. Virulence. 4(4): 295-306, 2013
F. Brauer. Mathematical epidemiology: Past, present and future. Infectious Disease Modelling, 2(2):113-127:2017
H. W. Hethcote. The mathematics of Infectious Disease. SIAM review, 42(4): 599-653, 2000.
E. V. Grigorieva, E. N. Khailov and A. Korobeinikov. Optimal quarantine strategies for COVID-19 Control Models. arXiv preprint arXiv:2004.10614v2, 2020.
E. Okyere, J. D. Ankamah, A. K. Hunkpe, and D. Mensah. Deterministic epidemic models for ebola infection with time-dependent controls. arXiv preprint arXiv:1908.07974, 2019.
S. Olaniyi, K. Okosuh, S. O. Adesanya, and E. A. Areo. Global Stability and optimal control analysis of malaria dynamics in the presence of human travels. The Open Infectious Disease Journal, 10(10), 2018.
E. Syahril, 1991, A Maximum Principle in Stochastic Optimal Control, Graduate Diploma Project, Department of Applied Mathematics, The University of Adelaide, Adelaide
Buldaev A.S, Burlakov, I. D, and Anakhin V.D. Pertubation Methods for Maximum Principle in Optimal Control Problem. Advances in Intelligent Systems Research, 164.
A. Lesniewski. Epidemic Control via Stochastic Optimal Control. arXiv preprint arXiv:2004.06680v3, 2020
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