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Drawing policy suggestions to fight Covid-19 from hardly reliable data. A machine-learning contribution on lockdowns analysis

Author

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  • Bonacini, Luca
  • Gallo, Giovanni
  • Patriarca, Fabrizio

Abstract

Feedback control-based mitigation strategies for COVID-19 are threatened by the time span occurring before an infection is detected in official data. Such a delay also depends on behavioral, technological and procedural issues other than the incubation period. We provide a machine learning procedure to identify structural breaks in detected positive cases dynamics using territorial level panel data. In our case study, Italy, three structural breaks are found and they can be related to the three different national level restrictive measures: the school closure, the main lockdown and the shutdown of non-essential economic activities. This allows assessing the detection delays and their relevant variability among the different measures adopted and the relative effectiveness of each of them. Accordingly we draw some policy suggestions to support feedback control based mitigation policies as to decrease their risk of failure, including the further role that wide swap campaigns may play in reducing the detection delay. Finally, by exploiting the huge heterogeneity among Italian provinces features, we stress some drawbacks of the restrictive measures specific features and of their sequence of adoption, among which, the side effects of the main lockdown on social and economic inequalities.

Suggested Citation

  • Bonacini, Luca & Gallo, Giovanni & Patriarca, Fabrizio, 2020. "Drawing policy suggestions to fight Covid-19 from hardly reliable data. A machine-learning contribution on lockdowns analysis," GLO Discussion Paper Series 534, Global Labor Organization (GLO).
    • Handle: RePEc:zbw:glodps:534
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    References listed on IDEAS

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    2. Massimiliano Bratti & Daniele Checchi & Antonio Filippin, 2007. "Geographical Differences in Italian Students' Mathematical Competencies: Evidence from Pisa 2003," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 66(3), pages 299-333, November.
    3. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    4. Sarti, Simone & Terraneo, Marco & Tognetti Bordogna, Mara, 2017. "Poverty and private health expenditures in Italian households during the recent crisis," Health Policy, Elsevier, vol. 121(3), pages 307-314.
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    Cited by:

    1. Domenico Depalo, 2021. "True COVID-19 mortality rates from administrative data," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 253-274, January.
    2. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. G. Dosi & L. Fanti & M. E. Virgillito, 2020. "Unequal societies in usual times, unjust societies in pandemic ones," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 47(3), pages 371-389, September.
    4. Bonacini, Luca & Gallo, Giovanni & Scicchitano, Sergio, 2020. "All that glitters is not gold. Effects of working from home on income inequality at the time of COVID-19," GLO Discussion Paper Series 541, Global Labor Organization (GLO).

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    More about this item

    Keywords

    Covid-19; coronavirus; lockdown; feedback control; mitigation strategies;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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