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COVID-19 under-reporting: spillovers and stringent containment strategies of global cases

Author

Listed:
  • Yulu Wang

    (State University of New York)

  • Subal C. Kumbhakar

    (State University of New York
    Czech University of Life Sciences Prague)

Abstract

Due to the rapid spread of the COVID-19 pandemic, accurately determining the true global infection count has become an extremely challenging task. In this context, our study explores the spatial spillover analysis of COVID-19 cases and assesses the impact of containment policy stringency on these spillovers. Furthermore, we examine the extent of under-reporting of COVID-19 cases at the country level. To account for diverse spatial dependencies, we employ a semiparametric spatial autoregressive model, in which the coefficients are smooth, unknown functions of countries’ stringency indices. Country-specific under-reporting, modeled as a one-sided deterministic function of exogenous variables, is estimated using the sieves method. Our analysis relies on COVID-19 infection data from 57 countries, which span from 2020 to 2021. We find that spillovers vary significantly across different levels of containment stringency. In addition, the true number of infections is estimated to be 1.72 to 5.73 times higher than the reported cases. These results align with previous research and have important policy implications for improving the precision of COVID-19 reporting and managing spillover effects more effectively.

Suggested Citation

  • Yulu Wang & Subal C. Kumbhakar, 2025. "COVID-19 under-reporting: spillovers and stringent containment strategies of global cases," Journal of Productivity Analysis, Springer, vol. 63(1), pages 87-106, February.
    • Handle: RePEc:kap:jproda:v:63:y:2025:i:1:d:10.1007_s11123-024-00741-3
    DOI: 10.1007/s11123-024-00741-3
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    References listed on IDEAS

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    More about this item

    Keywords

    COVID-19; Stochastic frontier analysis; Spillover; Spatial economies; Under-reporting;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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