IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i17p3766-d1231348.html
   My bibliography  Save this article

Controlling COVID-19 Spreading: A Three-Level Algorithm

Author

Listed:
  • Giovanni Dieguez

    (Department of Telecommunications and Control Engineering, São Paulo, Polytechnic School, University of São Paulo, São Paulo 05508900, Brazil
    These authors contributed equally to this work.)

  • Cristiane Batistela

    (Center for Mathematics, Computing and Cognition, Federal University of ABC, São Bernardo do Campo, São Paulo 09606070, Brazil
    These authors contributed equally to this work.)

  • José R. C. Piqueira

    (Department of Telecommunications and Control Engineering, São Paulo, Polytechnic School, University of São Paulo, São Paulo 05508900, Brazil)

Abstract

As the main methods of the coronavirus disease (COVID-19) transmission are air and physical contact, actions to mitigate and suppress its spread must be developed in order to change population dynamics and provide efficient control strategies. Here, these actions are described as a simple heuristic framework to establish public policies. Two control systems were studied: the first organized in the form of an algorithm stratified into three levels and the second as a minimization problem similar to optimal control strategies, applied to both social distancing and vaccination. The possible effects of these actions are modeled and applied to an extension of the Susceptible - Infected - Removed (SIR) compartmental model. The control system is developed, which is organized in the form of an algorithm stratified into three levels. These levels intend to represent social distancing strategies implemented by sanitary authorities around the globe, representing stronger or weaker grades of isolation intensity according to the ability of the healthcare system to cope with symptomatic individuals. The algorithm control is applied in a simulation, and the results give evidence of the effectiveness of the procedures adopted against the coronavirus. The model dynamics are analyzed and validated with simulations considering parameters obtained from epidemiological data from Brazil and Uruguay and in a more detailed way for three Brazilian states: São Paulo, Minas Gerais and Rio de Janeiro. The model was validated using cumulative data on cases and deaths. For cases of death, the results were satisfactory, while for case data, the response was reasonable, considering the possibility of adding delays or variations in parameters in the model. In addition, the effective reproduction number was proposed for the cities studied in Brazil, the result being relevant because it has a qualitative behavior similar to that published by official centers. This paper also discusses the implementation and optimization of social distancing and vaccination control strategies, considering different parameters and their effects on reducing the number of cases and deaths. Model simulations present promising results for developing strategies to attack COVID-19 dissemination.

Suggested Citation

  • Giovanni Dieguez & Cristiane Batistela & José R. C. Piqueira, 2023. "Controlling COVID-19 Spreading: A Three-Level Algorithm," Mathematics, MDPI, vol. 11(17), pages 1-39, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3766-:d:1231348
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/17/3766/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/17/3766/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Higazy, M., 2020. "Novel fractional order SIDARTHE mathematical model of COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Thomas Kruse & Philipp Strack, 2020. "Optimal Control of an Epidemic through Social Distancing," Cowles Foundation Discussion Papers 2229, Cowles Foundation for Research in Economics, Yale University.
    3. Demirguc-Kunt,Asli & Lokshin,Michael M. & Torre,Ivan, 2020. "The Sooner, the Better : The Early Economic Impact of Non-Pharmaceutical Interventions during the COVID-19 Pandemic," Policy Research Working Paper Series 9257, The World Bank.
    4. Khajanchi, Subhas & Das, Dhiraj Kumar & Kar, Tapan Kumar, 2018. "Dynamics of tuberculosis transmission with exogenous reinfections and endogenous reactivation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 52-71.
    5. Mishra, Bimal Kumar & Keshri, Ajit Kumar & Rao, Yerra Shankar & Mishra, Binay Kumar & Mahato, Buddhadeo & Ayesha, Syeda & Rukhaiyyar, Bansidhar Prasad & Saini, Dinesh Kumar & Singh, Aditya Kumar, 2020. "COVID-19 created chaos across the globe: Three novel quarantine epidemic models," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    6. Alberto Godio & Francesca Pace & Andrea Vergnano, 2020. "SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence," IJERPH, MDPI, vol. 17(10), pages 1-19, May.
    7. Batistela, Cristiane M. & Correa, Diego P.F. & Bueno, Átila M & Piqueira, José Roberto C., 2021. "SIRSi compartmental model for COVID-19 pandemic with immunity loss," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. José Roberto C. Piqueira & Cristiane M. Batistela, 2019. "Considering Quarantine in the SIRA Malware Propagation Model," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, November.
    9. Sophia Chen & Ms. Deniz O Igan & Mr. Nicola Pierri & Mr. Andrea F Presbitero, 2020. "Tracking the Economic Impact of COVID-19 and Mitigation Policies in Europe and the United States," IMF Working Papers 2020/125, International Monetary Fund.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bimal Kumar Mishra, 2022. "Stochastic models on the transmission of novel COVID-19," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 599-603, April.
    2. Batistela, Cristiane M. & Correa, Diego P.F. & Bueno, Átila M & Piqueira, José Roberto C., 2021. "SIRSi compartmental model for COVID-19 pandemic with immunity loss," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Steven Buigut & Burcu Kapar, 2021. "COVID-19 Cases, Media Attention and Social Mood," International Journal of Economics and Financial Issues, Econjournals, vol. 11(4), pages 66-72.
    4. Bole, Velimir & Prašnikar, Janez & Rop, Anton, 2023. "Support for those not affected: How macroeconomic policies have shaped COVID’s impact on sectoral activity," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 265-280.
    5. Famiglietti, Matthew & Leibovici, Fernando, 2022. "The impact of health and economic policies on the spread of COVID-19 and economic activity," European Economic Review, Elsevier, vol. 144(C).
    6. Beyer, Robert C.M. & Franco-Bedoya, Sebastian & Galdo, Virgilio, 2021. "Examining the economic impact of COVID-19 in India through daily electricity consumption and nighttime light intensity," World Development, Elsevier, vol. 140(C).
    7. Alessandro Ramponi & Maria Elisabetta Tessitore, 2024. "Optimal Social and Vaccination Control in the SVIR Epidemic Model," Mathematics, MDPI, vol. 12(7), pages 1-17, March.
    8. Menezes, Flavio & Figer, Vivian & Jardim, Fernanda & Medeiros, Pedro, 2022. "A near real-time economic activity tracker for the Brazilian economy during the COVID-19 pandemic," Economic Modelling, Elsevier, vol. 112(C).
    9. Asamoah, Joshua Kiddy K. & Okyere, Eric & Yankson, Ernest & Opoku, Alex Akwasi & Adom-Konadu, Agnes & Acheampong, Edward & Arthur, Yarhands Dissou, 2022. "Non-fractional and fractional mathematical analysis and simulations for Q fever," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    10. Anna Scherbina, 2021. "Assessing the Optimality of a COVID Lockdown in the United States," Economics of Disasters and Climate Change, Springer, vol. 5(2), pages 177-201, July.
    11. Davide Furceri & Siddharth Kothari & Longmei Zhang, 2021. "The effects of COVID‐19 containment measures on the Asia‐Pacific region," Pacific Economic Review, Wiley Blackwell, vol. 26(4), pages 469-497, October.
    12. Nicoletta Batini & Francesco Lamperti & Andrea Roventini, 2020. "Reducing risk while sharing it: a fiscal recipe for the EU at the time of COVID-19," SciencePo Working papers Main hal-04090084, HAL.
    13. Erdoğan, Güneş & Yücel, Eda & Kiavash, Parinaz & Salman, F. Sibel, 2024. "Fair and effective vaccine allocation during a pandemic," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    14. Linus Nyiwul, 2021. "Epidemic Control and Resource Allocation: Approaches and Implications for the Management of COVID-19," Studies in Microeconomics, , vol. 9(2), pages 283-305, December.
    15. Abel Brodeur & David Gray & Anik Islam & Suraiya Bhuiyan, 2021. "A literature review of the economics of COVID‐19," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1007-1044, September.
    16. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1181-1200, September.
    17. Correia, Sergio & Luck, Stephan & Verner, Emil, 2022. "Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu," The Journal of Economic History, Cambridge University Press, vol. 82(4), pages 917-957, December.
    18. Asamoah, Joshua Kiddy K. & Owusu, Mark A. & Jin, Zhen & Oduro, F. T. & Abidemi, Afeez & Gyasi, Esther Opoku, 2020. "Global stability and cost-effectiveness analysis of COVID-19 considering the impact of the environment: using data from Ghana," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    19. Carnehl, Christoph & Fukuda, Satoshi & Kos, Nenad, 2023. "Epidemics with behavior," Journal of Economic Theory, Elsevier, vol. 207(C).
    20. Naudé, Wim & Cameron, Martin, 2020. "Failing to Pull Together: South Africa's Troubled Response to COVID-19," IZA Discussion Papers 13649, Institute of Labor Economics (IZA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3766-:d:1231348. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.