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Analysis of COVID-19 Data with PRISM: Parameter Estimation and SIR Modelling

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12611))

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Abstract

We propose a pipeline for the stochastic analysis of a SIR model for COVID-19 through the stochastic model checker PRISM. The pipeline consists in: (i) the definition of a modified SIR model, able to include governmental restriction and prevention measures through an additional time-dependent coefficient; (ii) parameter estimation based on real epidemic data; (iii) translation of the modified SIR model into a Continuous Time Markov Chain (CTMC) expressed using the PRISM input language; and (iv) stochastic analysis (simulation and model checking) with PRISM.

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Notes

  1. 1.

    Agenzia Regionale di Sanita (ARS), https://www.ars.toscana.it/.

  2. 2.

    Freely available at http://dati.toscana.it/dataset/open-data-covid19.

  3. 3.

    GitHub repository: https://github.com/Unipisa/SIR-covid.

References

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Acknowledgements

This work is supported by the Università di Pisa under the “PRA – Progetti di Ricerca di Ateneo” (Institutional Research Grants) - Project no. PRA_2020-2021_26 “Metodi Informatici Integrati per la Biomedica”.

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Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy

    Paolo Milazzo

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  1. Paolo Milazzo
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Correspondence to Paolo Milazzo .

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Editors and Affiliations

  1. University of St Andrews, St Andrews, UK

    Juliana Bowles

  2. ISTI-CNR, Pisa, Italy

    Giovanna Broccia

  3. ISTI-CNR, Pisa, Italy

    Mirco Nanni

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Milazzo, P. (2021). Analysis of COVID-19 Data with PRISM: Parameter Estimation and SIR Modelling. In: Bowles, J., Broccia, G., Nanni, M. (eds) From Data to Models and Back. DataMod 2020. Lecture Notes in Computer Science(), vol 12611. Springer, Cham. https://doi.org/10.1007/978-3-030-70650-0_8

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  • DOI: https://doi.org/10.1007/978-3-030-70650-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70649-4

  • Online ISBN: 978-3-030-70650-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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