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Cell-DEVS Models for the Spread of COVID-19

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Cellular Automata (ACRI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12599))

Abstract

Improved Susceptible-Infected-Recovered (SIR) models have been used to study the COVID-19 pandemic. Although they can predict epidemiology curves, spatial models cannot be easily built, and cannot model individual interactions. In this research, we show a definition of SIR-based models using the Cell-DEVS formalism (a combination of Cellular Automata and DEVS), showing how to deal with these issues. We validate the equivalence of a simple Cell-DEVS SIR model, and we present a SIIRS model, whose parameters are configured to imitate the spread of SARS-CoV-2 in South Korea. Such models may assist in the decision-making process for defining health policies, such as social distancing, to prevent an uncontrolled expansion of the virus.

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Author information

Authors and Affiliations

  1. Universidad Politécnica de Madrid, Avenida Ramiro de Maeztu 7, 28040, Madrid, Spain

    Román Cárdenas

  2. Carleton University, 1125 Colonel by Drive, Ottawa, ON, K1S 5B6, Canada

    Román Cárdenas, Cristina Ruiz-Martín & Gabriel Wainer

  3. Complutense University of Madrid, Avenida de Séneca 2, 28040, Madrid, Spain

    Kevin Henares

Authors
  1. Román Cárdenas
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  2. Kevin Henares
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  3. Cristina Ruiz-Martín
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  4. Gabriel Wainer
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Corresponding author

Correspondence to Román Cárdenas .

Editor information

Editors and Affiliations

  1. University of Łódź, Lodz, Poland

    Tomasz M. Gwizdałła

  2. Università degli Studi di Trieste, Trieste, Italy

    Luca Manzoni

  3. Democritus University of Thrace, Xanthi, Greece

    Georgios Ch. Sirakoulis

  4. University of Milano-Bicocca, Milan, Italy

    Stefania Bandini

  5. University of Łódź, Lodz, Poland

    Krzysztof Podlaski

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Cárdenas, R., Henares, K., Ruiz-Martín, C., Wainer, G. (2021). Cell-DEVS Models for the Spread of COVID-19. In: Gwizdałła, T.M., Manzoni, L., Sirakoulis, G.C., Bandini, S., Podlaski, K. (eds) Cellular Automata. ACRI 2020. Lecture Notes in Computer Science(), vol 12599. Springer, Cham. https://doi.org/10.1007/978-3-030-69480-7_24

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  • DOI: https://doi.org/10.1007/978-3-030-69480-7_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69479-1

  • Online ISBN: 978-3-030-69480-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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