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Truncation data analysis for the under-reporting probability in COVID-19 pandemic

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  • Wei Liang
  • Hongsheng Dai
  • Marialuisa Restaino

Abstract

The COVID-19 pandemic has affected all countries in the world and brings a major disruption in our daily lives. Estimation of the prevalence and contagiousness of COVID-19 infections may be challenging due to under-reporting of infected cases. For a better understanding of such pandemic in its early stages, it is crucial to take into consideration unreported infections. In this study we propose a truncation model to estimate the under-reporting probabilities for infected cases. Hypothesis testing on the differences in truncation probabilities, that are related to the under-reporting rates, is implemented. Large sample results of the hypothesis test are presented theoretically and by means of simulation studies. We also apply the methodology to COVID-19 data in certain countries, where under-reporting probabilities are expected to be high.

Suggested Citation

  • Wei Liang & Hongsheng Dai & Marialuisa Restaino, 2022. "Truncation data analysis for the under-reporting probability in COVID-19 pandemic," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(3), pages 607-627, July.
    • Handle: RePEc:taf:gnstxx:v:34:y:2022:i:3:p:607-627
    DOI: 10.1080/10485252.2021.1989426
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