IDEAS home Printed from https://ideas.repec.org/p/zbw/glodps/534.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/216773/1/GLO-DP-0534.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Yun Qiu & Xi Chen & Wei Shi, 2020. "Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1127-1172, October.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
    2. Zhixiang Xie & Rongqin Zhao & Minglei Ding & Zhiqiang Zhang, 2021. "A Review of Influencing Factors on Spatial Spread of COVID-19 Based on Geographical Perspective," IJERPH, MDPI, vol. 18(22), pages 1, November.
    3. Chen, Simiao & Jin, Zhangfeng & Bloom, David E., 2020. "Act Early to Prevent Infections and Save Lives: Causal Impact of Diagnostic Efficiency on the COVID-19 Pandemic," IZA Discussion Papers 13749, Institute of Labor Economics (IZA).
    4. Catalina Amuedo-Dorantes & Neeraj Kaushal & Ashley N. Muchow, 2021. "Timing of social distancing policies and COVID-19 mortality: county-level evidence from the U.S," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1445-1472, October.
    5. Singhal, Amit & Singh, Pushpendra & Lall, Brejesh & Joshi, Shiv Dutt, 2020. "Modeling and prediction of COVID-19 pandemic using Gaussian mixture model," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    6. Maxim Ananyev & Michael Poyker & Yuan Tian, 2021. "The safest time to fly: pandemic response in the era of Fox News," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(3), pages 775-802, July.
    7. Badi H. Baltagi & Ying Deng & Jing Li & Zhenlin Yang, 2022. "Cities in a Pandemic: Evidence from China," CESifo Working Paper Series 10037, CESifo.
    8. Indrikis A. Krams & Priit Jõers & Severi Luoto & Giedrius Trakimas & Vilnis Lietuvietis & Ronalds Krams & Irena Kaminska & Markus J. Rantala & Tatjana Krama, 2021. "The Obesity Paradox Predicts the Second Wave of COVID-19 to Be Severe in Western Countries," IJERPH, MDPI, vol. 18(3), pages 1-10, January.
    9. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    10. Nicholas W. Papageorge & Matthew V. Zahn & Michèle Belot & Eline Broek-Altenburg & Syngjoo Choi & Julian C. Jamison & Egon Tripodi, 2021. "Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(2), pages 691-738, April.
    11. Lewandowski, Piotr, 2020. "Occupational Exposure to Contagion and the Spread of COVID-19 in Europe," IZA Discussion Papers 13227, Institute of Labor Economics (IZA).
    12. Huang, Yubo & Wu, Yan & Zhang, Weidong, 2020. "Comprehensive identification and isolation policies have effectively suppressed the spread of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    13. Chen, Xi & Qiu, Yun & Shi, Wei & Yu, Pei, 2022. "Key links in network interactions: Assessing route-specific travel restrictions in China during the Covid-19 pandemic," China Economic Review, Elsevier, vol. 73(C).
    14. Borja Gambau & Juan C. Palomino & Juan G. Rodríguez & Raquel Sebastian, 2022. "COVID-19 restrictions in the US: wage vulnerability by education, race and gender," Applied Economics, Taylor & Francis Journals, vol. 54(25), pages 2900-2915, May.
    15. Pawel Dlotko & Simon Rudkin, 2020. "Visualising the Evolution of English Covid-19 Cases with Topological Data Analysis Ball Mapper," Papers 2004.03282, arXiv.org, revised Apr 2020.
    16. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    17. Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
    18. Li, Xun & Lai, Weizheng & Wan, Qianqian & Chen, Xi, 2022. "Role of professionalism in response to the COVID-19 pandemic: Does a public health or medical background help?," China Economic Review, Elsevier, vol. 71(C).
    19. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2022. "From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1517-1550, October.
    20. Syed Abul Basher & A. K. Enamul Haque, 2021. "Public policy lessons from the Covid-19 outbreak: How to deal with it in the post-pandemic world?," Journal of Social and Economic Development, Springer;Institute for Social and Economic Change, vol. 23(2), pages 234-247, September.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:glodps:534. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/glabode.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/glabode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.