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

Wei Liang, Hongsheng Dai and Marialuisa Restaino ()

Journal of Nonparametric Statistics, 2022, vol. 34, issue 3, 607-627

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.

Date: 2022
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DOI: 10.1080/10485252.2021.1989426

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