Abstract
This paper studies a two-scales Markov compartmental model (SIRS) with demography, vaccination, reinfections and waning immunity, and with external control inputs of pharmaceutical (vaccination) and non-pharmaceutical (social distancing) type. The demography events are considered to be rare and contribute to a jump mechanism with trajectory depending jump intensity ultimately providing a piecewise deterministic Markov model. We study well-posedness of the system with time-varying total population and the herd immunity zone when intensive care units constraints are enforced. The third contribution describes, via Zubov’s method, the initial configurations that can be driven to the immunity zone. Numeric illustrations are provided using data from INSEE (France) on COVID-19.
Similar content being viewed by others
Data Availability
The data that support the findings of this study are openly available from INSEE (Institut national de la statistique et des études économiques) at https://www.insee.fr/fr/statistiques/. under the references: 2381380, 2383440.
Notes
This value is obtained by averaging over 2020–2021 period.
This value is obtained by averaging over 2017–2019 period prior to the epidemics.
This value is obtained by averaging over 2020–2021 period from which the previous \(\mu _-\) is substracted.
The continuity of \(\hat{Q}\) is also true, but this condition on \(\hat{\lambda } \hat{Q}\) suffices for the constructions, see also [21, Remark 1].
References
Alvarez, F.E., Argente, D., Lippi, F.: A simple planning problem for covid-19 lockdown. Technical report, National Bureau of Economic Research (2020)
Ames, A.D., Molnár, T.G., Singletary, A.W., Orosz, G.: Safety-critical control of active interventions for COVID-19 mitigation. IEEE Access 8, 188454–188474 (2020)
Anderson, R.M., Anderson, B., May, R.M.: Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford (1992)
Avram, F., Freddi, L., Goreac, D.: Optimal control of a SIR epidemic with ICU constraints and target objectives. Appl. Math. Comput. 418, 126816 (2022)
Behncke, H.: Optimal control of deterministic epidemics. Opt. Control Appl. Methods 21(6), 269–285 (2000)
Bolzoni, L., Bonacini, E., Della Marca, R., Groppi, M.: Optimal control of epidemic size and duration with limited resources. Math. Biosci. 315, 108232 (2019)
Britton, T., Leskelä, L.: Optimal intervention strategies for minimizing total incidence during an epidemic. SIAM J. Appl. Math. 83(2), 354–373 (2022)
Camilli, F., Grüne, L.: Characterizing attraction probabilities via the stochastic Zubov equation. Discrete Contin. Dyn. Syst. B 3(3), 457–468 (2003)
Caraballo, T., Colucci, R.: A comparison between random and stochastic modeling for a SIR model. Commun. Pure Appl. Anal. 16(1), 151–162 (2017)
Cori, A., Ferguson, N.M., Fraser, C., Cauchemez, S.: A new framework and software to estimate time-varying reproduction numbers during epidemics. Am. J. Epidemiol. 178(9), 1505–1512 (2013)
Crudu, A., Debussche, A., Muller, A., Radulescu, O.: Convergence of stochastic gene networks to hybrid piecewise deterministic processes. Ann. Appl. Prob. 22(5), 1822–1859 (2012)
Davis, M.H.A.: Piecewise-deterministic Markov-processes: a general-class of non-diffusion stochastic-models. J. R. Stat. Soc. Ser. B 46(3), 353–388 (1984)
Davis, M.H.A.: Control of Piecewise-deterministic processes via discrete-time dynamic-programming. Lect. Notes Control Inf. Sci. 78, 140–150 (1986)
Davis, M.H.A.: Markov Models and Optimization. Monographs on Statistics and Applied Probability, Chapman & Hall, London (1993)
Esterhuizen, W., Lévine, J., Streif, S.: Epidemic management with admissible and robust invariant sets. PLoS ONE 16(9), 1–28 (2021)
Flannery, D.D., Gouma, S., Dhudasia, M.B., Mukhopadhyay, S., Pfeifer, M.R., Woodford, E.C., Triebwasser, J.E., Gerber, J.S., Morris, J.S., Weirick, M.E., McAllister, C.M., Bolton, M.J., Arevalo, C.P., Anderson, E.M., Goodwin, E.C., Hensley, S.E., Puopolo, K.M.: Assessment of maternal and neonatal cord blood SARS-CoV-2 antibodies and placental transfer ratios. JAMA Pediatr. 175(6), 594–600 (2021)
Freddi, L.: Optimal control of the transmission rate in compartmental epidemics. Math. Control Relat. Fields 12(1), 201–223 (2022)
Freddi, L., Goreac, D., Li, J., Boxiang, X.: SIR epidemics with state-dependent costs and ICU constraints: a Hamilton–Jacobi verification argument and dual LP algorithms. Appl. Math. Optim. 86(2), 23 (2022)
Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25), 2340–2361 (1977)
Goreac, D.: Viability, invariance and reachability for controlled piecewise deterministic markov processes associated to gene networks. ESAIM-Control Optim. Calc. Var. 18(2), 401–426 (2012)
Goreac, D., Serea, O.-S.: Linearization techniques for controlled piecewise deterministic Markov processes; application to Zubov’s method. Appl. Math. Optim. 66, 209–238 (2012). https://doi.org/10.1007/s00245-012-9169-x
Grüne, L., Picarelli, A.: Zubov’s method for controlled diffusions with state constraints. Nonlinear Differ. Equ. Appl. NoDEA 22(6), 1765–1799 (2015)
Hansen, E., Day, T.: Optimal control of epidemics with limited resources. J. Math. Biol. 62(3), 423–451 (2011)
Hernández-Vargas, E.A., González, A.H., Beck, C.L., Bi, X., Campana, F.C., Giordano, G.: Modelling and control of epidemics across scales. 2022 In: IEEE 61st Conference on Decision and Control (CDC), pp. 4963–4980 (2022)
Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. Proc. R. Soc. Lond. Ser. A 115(772), 700–721 (1927)
Ketcheson, D.I.: Optimal control of an SIR epidemic through finite-time non-pharmaceutical intervention. J. Math. Biol. 7, 83 (2020)
Kloeden, P.E., Kozyakin, V.: The dynamics of epidemiological systems with nonautonomous and random coefficients. Math. Eng. Sci. Aerosp. 2(2), 105–118 (2011)
Knight, Ronald A.: Zubov’s condition revisited. Proc. Edinb. Math. Soc. 26(2), 253–257 (1983)
Kruse, T., Strack, P.: Optimal control of an epidemic through social distancing. Cowles Foundation Discussion Papers 2229, Cowles Foundation for Research in Economics, Yale University (2020)
Li, D., Liu, S., Cui, J.: Threshold dynamics and ergodicity of an SIRS epidemic model with Markovian switching. J. Differ. Equ. 263(12), 8873–8915 (2017)
Lin, Y., Zhao, Y.: Exponential ergodicity of a regime-switching SIS epidemic model with jumps. Appl. Math. Lett. 94, 133–139 (2019)
Martcheva, M.: An Introduction to Mathematical Epidemiology, vol. 16. Springer, New York (2015)
Molina, E., Rapaport, A.: An optimal feedback control that minimizes the epidemic peak in the SIR model under a budget constraint. Automatica 146, 110596 (2022)
Riedler, M.G.: Almost sure convergence of numerical approximations for Piecewise Deterministic Markov Processes. J. Comput. Appl. Math. 239, 50–71 (2013)
Sauerteig, P., Esterhuizen, W., Wilson, M., Ritschel, T.K.S., Worthmann, K., Streif, S.: Model predictive control tailored to epidemic models. In: 2022 European Control Conference (ECC), pp. 743–748 (2022)
Zhang, X., Wang, K.: Stochastic SIR model with jumps. Appl. Math. Lett. 26(8), 867–874 (2013)
Zubov, V.I.: Methods of A. M. Lyapunov and their application. In: Boron, L.F. (ed.) Translation Prepared Under the Auspices of the United States Atomic Energy Commission. P. Noordhoff Ltd, Groningen (1964)
Funding
The authors acknowledge support from the NSF of Shandong Province (NO. ZR202306020015), the National Key R and D Program of China (NO. 2018YFA0703900), and the NSF of P.R. China (NO. 12031009).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethics Approval
The work presented here has not been published previously, it is not under consideration for publication elsewhere. The publication is approved by all authors and by the responsible authorities where the work was carried out. If accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright-holder.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Goreac, D., Li, J., Wang, Y. et al. Return-to-Normality in a Piecewise Deterministic Markov SIR+V Model with Pharmaceutical and Non-pharmaceutical Interventions. Appl Math Optim 89, 19 (2024). https://doi.org/10.1007/s00245-023-10087-1
Accepted:
Published:
DOI: https://doi.org/10.1007/s00245-023-10087-1