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COVID-19: R0 is lower where outbreak is larger

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  • Pietro Battiston
  • Simona Gamba
Abstract
We use daily data from Lombardy, the Italian region most affected by the COVID-19 outbreak, to calibrate a SIR model individually on each municipality. These are all covered by the same health system and, in the post-lockdown phase we focus on, all subject to the same social distancing regulations. We find that municipalities with a higher number of cases at the beginning of the period analyzed have a lower rate of diffusion, which cannot be imputed to herd immunity. In particular, there is a robust and strongly significant negative correlation between the estimated basic reproduction number (R0) and the initial outbreak size, in contrast with the role of R0 as a predictor of outbreak size. We explore different possible explanations for this phenomenon and conclude that a higher number of cases causes changes of behavior, such as a more strict adoption of social distancing measures among the population, that reduce the spread. This result calls for a transparent, real-time distribution of detailed epidemiological data, as such data affects the behavior of populations in areas affected by the outbreak.

Suggested Citation

  • Pietro Battiston & Simona Gamba, 2020. "COVID-19: R0 is lower where outbreak is larger," Working Papers 438, University of Milano-Bicocca, Department of Economics, revised Apr 2020.
    • Handle: RePEc:mib:wpaper:438
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    Cited by:

    1. Matthias Klumpp & Dominic Loske & Silvio Bicciato, 2022. "COVID-19 health policy evaluation: integrating health and economic perspectives with a data envelopment analysis approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(8), pages 1263-1285, November.
    2. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2023. "Social Distancing, Vaccination and Evolution of COVID-19 Transmission Rates in Europe," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(2), pages 474-508, June.
    3. Martin-Lapoirie, Dylan & McColl, Kathleen & Gallopel-Morvan, Karine & Arwidson, Pierre & Raude, Jocelyn, 2024. "Health protective behaviours during the COVID-19 pandemic: Risk adaptation or habituation?," Social Science & Medicine, Elsevier, vol. 342(C).
    4. Alexander Chudik & M. Hashem Pesaran & Alessandro Rebucci, 2021. "COVID-19 Time-Varying Reproduction Numbers Worldwide: An Empirical Analysis of Mandatory and Voluntary Social Distancing," Globalization Institute Working Papers 407, Federal Reserve Bank of Dallas.
    5. Wim Naudé & Ricardo Vinuesa, 2020. "Data, global development, and COVID-19: Lessons and consequences," WIDER Working Paper Series wp-2020-109, World Institute for Development Economic Research (UNU-WIDER).
    6. Celidoni, Martina & Costa-Font, Joan & Salmasi, Luca, 2023. "Mobility restrictions and alcohol use during lockdown: “A still and dry pandemic for the many”?," Economics & Human Biology, Elsevier, vol. 50(C).
    7. Muhammad Fayyaz Nazir & Shahzadah Fahed Qureshi, 2023. "Applying Structural Equation Modelling to Understand the Implementation of Social Distancing in the Professional Lives of Healthcare Workers," IJERPH, MDPI, vol. 20(5), pages 1-32, March.
    8. P. Battiston & M. Menegatti, 2022. "Interaction in Prevention: A General Theory and an Application to COVID-19 Pandemic," Economics Department Working Papers 2022-EP02, Department of Economics, Parma University (Italy).

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    More about this item

    Keywords

    COVID-19; tests; basic reproduction number; social distancing; containment.;
    All these keywords.

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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