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Estimating COVID-19 under-reporting through stochastic frontier analysis and official statistics: A case study of São Paulo State, Brazil

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  • Danelon, André F.
  • Kumbhakar, Subal C.

Abstract

During outbreaks, natural disasters, and any unexpected event, it is usual that public authorities face issues tracking the outcomes, and the available reports of casualties tend to be lower than actual numbers. Using weekly data at the municipality level in São Paulo State (the most densely populated region in Brazil), we employ stochastic frontier methods to fit the dynamics of the COVID-19 outbreak spanning from March 2020 to December 2021. The empirical model incorporates the inverse hyperbolic sine transformation to address the issue of zero reporting of COVID-19 deaths. Furthermore, we utilize a flexible frontier method to capture the S-shaped epidemic curve in two distinct waves of infections/deaths. Our results reveal that the actual death toll resulting from COVID-19 is, on average, 1.24 times higher than the officially reported figures. Consequently, these findings hold significant implications for public authorities in identifying regions characterized by substantial under-reporting of COVID-19 fatalities. Moreover, this study presents an empirical framework that can be used for other municipalities, states, or countries confronting outbreak scenarios.

Suggested Citation

  • Danelon, André F. & Kumbhakar, Subal C., 2023. "Estimating COVID-19 under-reporting through stochastic frontier analysis and official statistics: A case study of São Paulo State, Brazil," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:soceps:v:90:y:2023:i:c:s0038012123002653
    DOI: 10.1016/j.seps.2023.101753
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    References listed on IDEAS

    as
    1. Roberto Colombi & Subal Kumbhakar & Gianmaria Martini & Giorgio Vittadini, 2014. "Closed-skew normality in stochastic frontiers with individual effects and long/short-run efficiency," Journal of Productivity Analysis, Springer, vol. 42(2), pages 123-136, October.
    2. Thomas P. Triebs & David S. Saal & Pablo Arocena & Subal C. Kumbhakar, 2016. "Estimating economies of scale and scope with flexible technology," Journal of Productivity Analysis, Springer, vol. 45(2), pages 173-186, April.
    3. Ghislain B D Aihounton & Arne Henningsen, 2021. "Units of measurement and the inverse hyperbolic sine transformation," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 334-351.
    4. Daniel L. Millimet & Christopher F. Parmeter, 2022. "COVID‐19 severity: A new approach to quantifying global cases and deaths," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1178-1215, July.
    5. Inmaculada C. Álvarez & Luis Orea & Alan Wall, 2023. "Estimating the propagation of both reported and undocumented COVID-19 cases in Spain: a panel data frontier approximation of epidemiological models," Journal of Productivity Analysis, Springer, vol. 59(3), pages 259-279, June.
    6. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    7. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107029514, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    COVID-19; Stochastic frontier; Under-reporting;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • H12 - Public Economics - - Structure and Scope of Government - - - Crisis Management
    • I10 - Health, Education, and Welfare - - Health - - - General

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