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Modeling and Optimal Control for Resource Allocation in the Epidemic Monitoring of a Multi-group Population

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Abstract

In the last 3 years, the entire world has been facing the sanitary emergency due to the SARS-CoV2; it has been stressed the mutual interdependence of the human populations, as well as the strong impact of specific conditions, such as age, work, habits, on the disease spread among subgroups of a given population. Moreover, the high percentage of asymptomatic individuals, especially among young subjects, improves the infection spread among groups with higher probability of fatal consequences. The vaccination campaign has strongly contributed to reduce mortality, so bringing the epidemic scenario probably closer to the endemic state. Nevertheless, the loss of the vaccination protection, the appearance of new variants and the mixing of moving people require a surveillance action to reduce new virus waives. By suitable modeling and optimal control design, this paper addresses the planning of a swab test campaign for the surveillance of the disease spread within a given multi-group population, when resource limitation and distinctive epidemiological characteristics of the subgroups are present. The proposed approach, applied on a case study in which the population is split into four groups, stresses the importance of a suitable tuned swab test campaign, saving a significant number of infections (and therefore of human lives), by scheduling the surveillance effort on the different populations. The determination of this kind of control is useful not only in the endemic condition, but also as general surveillance strategy at the beginning of an epidemic spread, with limitations in material and logistic resources.

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References

  1. Assefa Y, Gilks CF, Reid S, et al. Analysis of the COVID-19 pandemic: lessons towards a more efective response to public health emergencies. Globalization and Health. 2022;18.https://doi.org/10.1186/s12992-022-00805-9.

  2. Borri A, Palumbo P, Papa F, et al. Optimal design of lock-down and reopening policies for early-stage epidemics through SIR-D models. Ann Rev control. 2021;51:511–24.https://doi.org/10.1016/j.arcontrol.2020.12.002.

    Article MathSciNet  Google Scholar 

  3. Casares M, Khan H. The timing and intensity of social distancing to flatten the COVID-19 curve: The case of Spain. Int J Env Res Pub He. 2020;1(7283):1–14.https://doi.org/10.3390/ijerph17197283.

    Article  Google Scholar 

  4. Contreras S, Villavicencio HA, Medina-Ortiz D, et al. A multi-group SEIRA model for the spread of COVID-19 among heterogeneous populations. Chaos Solitons Fractals. 2020;136(109):925.https://doi.org/10.1016/j.chaos.2020.109925.

    Article  Google Scholar 

  5. Di Giamberardino P, Iacoviello D, Papa F, et al. A data-driven model of the covid-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in italy. Nonlinear Dyn. 2021;106(2):1239–66.https://doi.org/10.1007/s11071-021-06840-2.

    Article  Google Scholar 

  6. Di Giamberardino P, Iacoviello D, Papa F (2022) Optimal resource allocation for fast epidemic monitoring in networked populations. Proceedings of ICINCO 2022

  7. Di Giamberardino P, Iacoviello D. Evaluation of the effect of different policies in the containment of epidemic spreads for the COVID-19 case. Biomed Signal Process Control. 2021;65(102325):1–15.https://doi.org/10.1016/j.bspc.2020.102325.

    Article  Google Scholar 

  8. Di Giamberardino P, Caldarella R, Iacoviello D. A control based mathematical model for the evaluation of intervention lines in COVID-19 epidemic spread: The italian case study. Symmetry. 2021;13(5):1–21.

    Article  Google Scholar 

  9. Di Giamberardino P, Iacoviello D, Papa F, et al. Dynamical evolution of COVID-19 in Italy with an evaluation of the size of the asymptomatic infective population. IEEE J Biomed Health Inform. 2021;25(4):1326–32.

    Article  Google Scholar 

  10. Espinoza B, Castillo-Chavez C, Perrings C (2020) Mobility restrictions for the control of epidemics: When do they work? PLoS One 15(7):e0235,731. doi:10.1371/journal.pone.0235731

  11. Gatto M, Bertuzzo E, Mari L, et al. Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. PNAS. 2020;117(10):484–91.https://doi.org/10.1073/pnas.2004978117.

    Article  Google Scholar 

  12. Liu M, Thomadsen R, Yao S (2020) Forecasting the spread of COVID-19 under different reopening strategies. Sci Rep 10(1):20,367. doi:10.1038/s41598-020-77292-8

  13. Marziano V, Guzzetta G, Rondinone B, et al. Retrospective analysis of the italian exit strategy from COVID-19 lockdown. PNAS. 2021;118(e2019617):118.https://doi.org/10.1073/pnas.2019617118.

    Article  Google Scholar 

  14. Ndairou F, Area I, Nieto J, et al. Mathematical modeling of covid-19 transmission dynamics with a case study of wuhan. Chaos, Solitons and Fractals. 2020;135:1–6.

    Article MathSciNet  Google Scholar 

  15. Pung R, Cook A, Chiew C, et al. Effectiveness of containment measures against COVID-19 in Singapore: implications for other national containment efforts. Epidemiology. 2021;32(1):79–86.https://doi.org/10.1097/EDE.0000000000001257.

    Article  Google Scholar 

  16. Radulescu A, Williams C, Cavanagh K. Management strategies in a SEIR-type model of COVID-19 community spread. Nat Sci Rep. 2020;21256(3):1–16.

    Google Scholar 

  17. Roosa K, Lee Y, Luo R, et al. (2020) Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th. Infect Dis Modell. 2020;5(3):256–63.

    Google Scholar 

  18. Tang B, Wang X, Li Q, et al. Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. J Clin Med. 2020;9:462.https://doi.org/10.3390/jcm9020462.

    Article  Google Scholar 

  19. Van Den Driessche P, Watmough J. Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math Biosciences. 2002;180:29–48.

    Article MathSciNet  Google Scholar 

  20. Wu J, K.Leung, G.M.Leung (2020) Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet pp 1–9

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Author notes

  1. P. Di Giamberardino, D. Iacoviello and F. Papa: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Computer, Control and Management Engineering “A. Ruberti”, Sapienza University of Rome, Via Ariosto 25, Rome, 00185, Italy

    Paolo Di Giamberardino & Daniela Iacoviello

  2. Institute for System Analysis and Computer Science “A. Ruberti”, CNR, Via dei Taurini 19, Rome, 00185, Italy

    Federico Papa

Authors
  1. Paolo Di Giamberardino

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  2. Daniela Iacoviello

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  3. Federico Papa

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Correspondence toDaniela Iacoviello.

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This article is part of the topical collection “Advances on Informatics in Control, Automation and Robotics” guest edited by Dimitar Filev, Giuseppina Gini and Henk Nijmeijer.

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Di Giamberardino, P., Iacoviello, D. & Papa, F. Modeling and Optimal Control for Resource Allocation in the Epidemic Monitoring of a Multi-group Population.SN COMPUT. SCI.5, 121 (2024). https://doi.org/10.1007/s42979-023-02377-w

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