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COVID-19: R0 is lower where outbreak is larger

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  • Battiston, Pietro
  • Gamba, Simona
Abstract
We use daily data from Lombardy, the Italian region most affected by the COVID-19 outbreak, to calibrate a SIR model on each municipality. Municipalities with a higher initial number of cases feature a lower rate of diffusion, not attributable to herd immunity: there is a robust and strongly significant negative correlation between the estimated basic reproduction number (R0) and the initial outbreak size. This represents novel evidence of the prevalence-response elasticity in a cross-sectional setting, characterized by a same health system and homogeneous social distancing regulations. By ruling out alternative explanations, we 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 finding calls for the distribution of detailed epidemiological data to populations affected by COVID-19 outbreaks.

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  • Battiston, Pietro & Gamba, Simona, 2021. "COVID-19: R0 is lower where outbreak is larger," Health Policy, Elsevier, vol. 125(2), pages 141-147.
    • Handle: RePEc:eee:hepoli:v:125:y:2021:i:2:p:141-147
    DOI: 10.1016/j.healthpol.2020.10.017
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. 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.
    7. 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).
    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; Prevalence-response elasticity; 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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