×

Inverse optimal control for positive impulsive systems toward personalized therapeutic regimens. (English) Zbl 1531.93187

Summary: Infectious diseases are latent threats to humankind. Control theoretical approaches can help practitioners to advance the scheduling of drugs. For the case of infectious diseases, it is not possible to keep continuous flow of drug administration over all time-steps, thus the action of the control input has to be restricted at some of the \(k\)th instants. This paper presents the adaptation of inverse optimal control to positive impulsive systems in discrete-time to schedule therapies. The properties of positive systems are used to simplify the control design. Thus, the problem of scheduling therapies in infectious diseases is illustrated with influenza and COVID-19. Numerical results show the applicability of the control algorithms.
{© 2022 John Wiley & Sons Ltd.}

MSC:

93C28 Positive control/observation systems
93C27 Impulsive control/observation systems
49N45 Inverse problems in optimal control
93C55 Discrete-time control/observation systems
92C50 Medical applications (general)
Full Text: DOI

References:

[1] BenedictowO. The black death: the greatest catastrophe ever. History Today. 2005;55(3):1346‐1353.
[2] WHO. History of 1918 flu pandemic; 2018. https://www.cdc.gov/flu/pandemic‐resources/1918‐commemoration/1918‐pandemic‐history.htm
[3] CDC. Coronavirus diseases (COVID‐2019) situation reports; 2020. https://www.who.int/emergencies/diseases/novel‐coronavirus‐2019/situation‐reports/
[4] LopezL, RodoX. The end of the social confinement in Spain and the COVID‐19 re‐emergence risk; 2020. http://www.nature.com/articles/s41562‐020‐0908‐8
[5] Ricardo‐AzanzaCL, Vargas‐HernandezEA. Epidemiological characteristics of COVID‐19 in Mexico and the potential impact of lifting confinement across regions. Front Phys. 2020;8:573322. doi:10.3389/fphy.2020.573322
[6] PratherKA, WangCC, SchooleyRT. Reducing transmission of SARS‐CoV‐2. Science (New York, NY). 2020;368(6498):1424‐1425. doi:10.1126/science.abc6197
[7] AndersonRM, HeesterbeekH, KlinkenbergD, HollingsworthTD. How will country‐based mitigation measures influence the course of the COVID‐19 epidemic?Lancet. 2020;395:931‐934. doi:10.1016/S0140-6736(20)30567-5
[8] Hernandez‐MejiaG, Hernandez‐VargasEA. When is SARS‐CoV‐2 in your shopping list?Math Biosci. 2020;328:108434. doi:10.1016/j.mbs.2020.108434
[9] Hernandez‐VargasEA. Modeling and Control of Infectious Diseases: With MATLAB and R. 1st ed.ELSEVIER Academic Press; 2019.
[10] RongL, ASPÃ. Modeling HIV persistence, the latent reservoir, and viral blips. J Theor Biol. 2009;260(2):308‐331. doi:10.1016/j.jtbi.2009.06.011 · Zbl 1402.92409
[11] PerelsonAS, RibeiroRM. Modeling the within‐host dynamics of HIV infection. BMC Biol. 2013;11(1):96. doi:10.1186/1741-7007-11-96
[12] RelugaTC, DahariH, PerelsonAS. Analysis if hepatitis C virus infection models with hepatocyte homeostasis. SIAM J Appl Math. 2009;69(4):999‐1023. · Zbl 1167.92014
[13] GrawF, PerelsonAS. Modeling viral spread. Annu Rev Virol. 2015;July:1‐18. doi:10.1146/annurev-virology-110615-042249
[14] NguyenVK, BinderSC, BoianelliA, Meyer‐HermannM, Hernandez‐VargasEA. Ebola virus infection modeling and identifiability problems. Front Microbiol. 2015;6:1‐11.
[15] NguyenVK, Hernandez‐VargasEA. Windows of opportunity for Ebola virus infection treatment and vaccination. Sci Rep. 2017;7(1):8975.
[16] BaccamP, BeaucheminC, CaM, HaydenFG, PerelsonAS. Kinetics of influenza a virus infection in humans. J Virol. 2006;80(15):7590‐7599.
[17] HandelA, LonginiIM, AntiaR. Neuraminidase inhibitor resistance in influenza: assessing the danger of its generation and spread. PLoS Comput Biol. 2007;3(12):2456‐2464.
[18] PawelekKA, DorD, SalmeronC, HandelA. Within‐host models of high and low pathogenic influenza virus infections: the role of macrophages. PLoS One. 2016;11(2):1‐16. doi:10.1371/journal.pone.0150568
[19] GonçalvesA, BertrandJ, KeR, et al. Timing of antiviral treatment initiation is critical to reduce SARS‐Cov‐2 viral load. medRxiv. 2020. doi:10.1101/2020.04.04.20047886
[20] GoyalA, Cardozo‐OjedaE, SchifferJT. Potency and timing of antiviral therapy as determinants of duration of SARS CoV‐2 shedding and intensity of inflammatory response. medRxiv. 2020;20061325. doi:10.1101/2020.04.10.20061325
[21] PinkyL, DobrovolnyHM. SARS‐CoV‐2 coinfections: could influenza and the common cold be beneficial?J Med Virol. 2020;May:1‐8. doi:10.1002/jmv.26098
[22] Hernandez VargasEA, Velasco‐HernandezJX. In‐host modelling of COVID‐19 kinetics in humans. Annu Rev Control. 2020;50:448‐456.
[23] ChangH, AstolfiA. Control of HIV infection dynamics. IEEE Control Syst Mag. 2008;28(2):28‐39. · Zbl 1395.93413
[24] RivadeneiraPS, MoogCH, StanG‐B, et al. Mathematical modeling of HIV dynamics after antiretroviral therapy initiation: a review. BioRes Open Access. 2014;3(5):233‐241.
[25] Seok‐KyoonKim, DonghuKim, and Tae‐WoongYoon. Adaptive observer for estimating the parameters of an HIV model with mutants. Int J Control Automat Syst, 13(1):126-137, feb 2015.
[26] PaoloDi Giamberardino and DanielaIacoviello. HIV nfection control: a constructive algorithm for a state‐based switching control. Int J Control Automat Syst, 16(3):1469-1473, jun 2018.
[27] FeiSun and KamranTurkoglu. Estimation of CD4+ T cell count parameters in HIV/AIDS patients based on real‐time nonlinear receding horizon control. Int J Control Automat Syst, 16(4):1805-1813, aug 2018.
[28] Hernandez‐MejiaG, AlanisAY, Hernandez‐GonzalezM, FindeisenR, Hernandez‐vargasEA. Passivity‐based inverse optimal impulsive control for influenza treatment in the host. IEEE Trans Control Syst Technol. 2019;28:1‐12.
[29] RivadeneiraPS, CaicedoM, FerramoscaA, GonzalezAH. Impulsive Zone Model Predictive Control ( iZMPC ) for therapeutic treatments: application to HIV dynamics. Proceedings of the 56th IEEE Conference on Decision and Control; 2017:1‐6; Melbourne.
[30] LocatelliA. Optimal Control: An Introduction. 1st ed.Birkhauser Verlag; 2001. · Zbl 1096.49500
[31] FreemanRA, KokotovicPV. Optimal nonlinear controllers for feedback linearizable systems. Proceedings of the American Control Conference; 1995:2722‐2726; Seattle, American Autom Control Council.
[32] SanchezEN, Ornelas‐TellezF. Discrete‐time inverse optimal control for nonlinear systems; 2016.
[33] RivadeneiraP, FerramoscaA, GonzálezAH. Control strategies for nonzero set‐point regulation of linear impulsive systems. IEEE Trans Automat Control. 2018;63(9):2994‐3001. · Zbl 1423.93151
[34] BolzernP, ColaneriP, De NicolaoG. Markov jump linear systems with switching transition rates: mean square stability with dwell‐time. Automatica. 2010;46(6):1081‐1088. · Zbl 1192.93119
[35] HaddadM, WassimCVS, SergeyGN. Impulsive and Hybrid Dynamical Systems: Stability, Dissipativity, and Control. 1st ed.Princeton University Press; 2006. · Zbl 1114.34001
[36] Ornelas‐TellezF, SanchezEN, LoukianovA. Inverse optimal control for discrete‐time nonlinear systems via passivation. Opt Control Appl Methods. 2014;35:110‐126. · Zbl 1285.49026
[37] Elvira‐CejaS, SanchezEN. Inverse optimal control for asymptotic trajectory tracking of discrete‐time stochastic nonlinear systems in block controllable form. Optim Control Appl Methods. 2018;39(5):1702‐1715. · Zbl 1402.93264
[38] RicaldeLJ, SanchezEN. Inverse optimal neural control of a class of nonlinear systems with constrained inputs for trajectory tracking. Optim Control Appl Methods. 2012;33(2):176‐198. · Zbl 1258.93069
[39] ChangH, AstolfiA. Enhancement of the immune system in HIV dynamics by output feedback. Automatica. 2009;45(7):1765‐1770. · Zbl 1184.93059
[40] FerreiraJ, Hernandez‐VargasEA, MiddletonRH. Computer simulation of structured treatment interruption for HIV infection. Comput Methods Programs Biomed. 2011;104(2):50‐61.
[41] ZurakowskiR. Nonlinear observer output‐feedback MPC treatment scheduling for HIV. Biomed Eng Online. 2011;10(40):1‐16.
[42] Vega‐MagdalenoGD, AlanisAY, Hernandez‐MejiaG, Hernandez‐VargasEA. Impulsive MPC for influenza infection treatment at variable time. IFAC‐PapersOnLine. 2018;51:79‐84.
[43] GonzalezAH, RivadeneiraPS, FerramoscaA, MagdelaineN, MoogCH. Impulsive zone MPC for type I diabetic patients based on a long‐term model. Proceedings of the IFAC World Congress; 2017:15294‐15299.
[44] FarinaL, RinaldiS. Positive Linear Systems: Theory and Applications. Vol 50. Wiley; 2011.
[45] KhongSZ, RantzerA. Diagonal Lyapunov functions for positive linear time‐varying systems. Proceedings of the 2016 IEEE 55th Conference on Decision and Control, CDC; 2016:5269‐5274; Las Vegas.
[46] KhalilHK. Nonlinear Systems. 3rd ed.Prentice Hall; 2002. · Zbl 1003.34002
[47] BellmanR. Dynamic Programming. Dover Publications; 2010.
[48] CaniniL, ConwayJ, PerelsonA, CarratF. Impact of different oseltamivir regimens on treating influenza A virus infection and resistance emergence: insights from a modelling study. PLoS Comput Biol. 2014;10(4):1‐12.
[49] WattanagoonY, StepniewskaK, LindegårdhN, et al. Pharmacokinetics of high‐dose oseltamivir in healthy volunteers. Antimicrob Agents Chemother. 2009;53(3):945‐952.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.