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

Liang, Wei and Dai, Hongsheng and Restaino, Marialuisa (2021) 'Truncation data analysis for the under-reporting probability in COVID-19 pandemic.' Journal of Nonparametric Statistics. pp. 1-21. ISSN 1026-7654

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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.

Item Type: Article
Uncontrolled Keywords: COVID-19; hypothesis test; large sample theory; truncation; under-reporting
Divisions: Faculty of Science and Health
Faculty of Science and Health > Mathematical Sciences, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 03 Nov 2021 12:25
Last Modified: 06 Jan 2022 14:32

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