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Assessing functional propagation patterns in COVID-19

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  • Zanin, Massimiliano
  • Papo, David

Abstract

Among the many efforts done by the scientific community to help coping with the COVID-19 pandemic, one of the most important has been the creation of models to describe its propagation, as these are expected to guide the deployment of containment and health policies. These models are commonly based on exogenous information, as e.g. mobility data, whose limitedness always compromise the reliability of obtained results. In this contribution we propose a different approach, based on extracting relationships between the evolution of the disease in different regions through information theoretical metrics. In a way similar to what is commonly done in neuroscience, propagation is understood as information transfer, and the resulting propagation patterns are represented and studied as functional networks. By applying this methodology to the dynamics of COVID-19 in several countries and regions thereof, we were able to reconstruct static and time-varying propagation graphs. We further discuss the advantages, promises and open research questions associated with this functional approach.

Suggested Citation

  • Zanin, Massimiliano & Papo, David, 2020. "Assessing functional propagation patterns in COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920303921
    DOI: 10.1016/j.chaos.2020.109993
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    References listed on IDEAS

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    3. Massimiliano Zanin & Ernestina Menasalvas & Xiaoqian Sun & Sebastian Wandelt, 2018. "From the Difference of Structures to the Structure of the Difference," Complexity, Hindawi, vol. 2018, pages 1-12, December.
    4. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    5. J. T. Lizier & M. Prokopenko, 2010. "Differentiating information transfer and causal effect," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 73(4), pages 605-615, February.
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    Cited by:

    1. Christian Acal & Manuel Escabias & Ana M. Aguilera & Mariano J. Valderrama, 2021. "COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression," Mathematics, MDPI, vol. 9(11), pages 1-23, May.
    2. Papo, David & Righetti, Marco & Fadiga, Luciano & Biscarini, Fabio & Zanin, Massimiliano, 2020. "A minimal model of hospital patients’ dynamics in COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).

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