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SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence

Author

Listed:
  • Alberto Godio

    (Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Francesca Pace

    (Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

  • Andrea Vergnano

    (Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy)

Abstract

We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardy, Piedmont, and Veneto regions. We focused on the application of a stochastic approach in fitting the model parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyzed the official data and the predicted evolution of the epidemic in the Italian regions, and we compared the results with the data and predictions of Spain and South Korea. We linked the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discussed the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:10:p:3535-:d:359842
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    Citations

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    Cited by:

    1. Amin Eshkiti & Fatemeh Sabouhi & Ali Bozorgi-Amiri, 2023. "A data-driven optimization model to response to COVID-19 pandemic: a case study," Annals of Operations Research, Springer, vol. 328(1), pages 337-386, September.
    2. Zhou, Xin & Liao, Wenzhu, 2023. "Research on demand forecasting and distribution of emergency medical supplies using an agent-based model," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    3. 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.
    4. Yas Al-Hadeethi & Intesar F. El Ramley & Hiba Mohammed & Abeer Z. Barasheed, 2023. "A New Polymorphic Comprehensive Model for COVID-19 Transition Cycle Dynamics with Extended Feed Streams to Symptomatic and Asymptomatic Infections," Mathematics, MDPI, vol. 11(5), pages 1-27, February.
    5. Anil Babu Payedimarri & Diego Concina & Luigi Portinale & Massimo Canonico & Deborah Seys & Kris Vanhaecht & Massimiliano Panella, 2021. "Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review," IJERPH, MDPI, vol. 18(9), pages 1-11, April.
    6. Lei Zhang & Guang-Hui She & Yu-Rong She & Rong Li & Zhen-Su She, 2022. "Quantifying Social Interventions for Combating COVID-19 via a Symmetry-Based Model," IJERPH, MDPI, vol. 20(1), pages 1-15, December.
    7. Kai Yin & Anirban Mondal & Martial Ndeffo-Mbah & Paromita Banerjee & Qimin Huang & David Gurarie, 2022. "Bayesian Inference for COVID-19 Transmission Dynamics in India Using a Modified SEIR Model," Mathematics, MDPI, vol. 10(21), pages 1-18, October.

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