Review Articles

A novel nonparametric mixture model for the detection pattern of COVID-19 on Diamond Princess cruise

Huijuan Ma ,

KLATASDS-MOE, School of Statistics and Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai, People's Republic of China

hjma@fem.ecnu.edu.cn

Jing Qin ,

National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

Fang Chen ,

School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, People's Republic of China

Yong Zhou

KLATASDS-MOE, School of Statistics and Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai, People's Republic of China

Pages | Received 15 Aug. 2021, Accepted 05 Dec. 2022, Published online: 20 Dec. 2022,
  • Abstract
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The outbreak of COVID-19 on the Diamond Princess cruise ship has attracted much attention. Motivated by the PCR testing data on the Diamond Princess, we propose a novel cure mixture nonparametric model to investigate the detection pattern. It combines a logistic regression for the probability of susceptible subjects with a nonparametric distribution for the detection of infected individuals. Maximum likelihood estimators are proposed. The resulting estimators are shown to be consistent and asymptotically normal. Simulation studies demonstrate that the proposed approach is appropriate for practical use. Finally, we apply the proposed method to PCR testing data on the Diamond Princess to show its practical utility.

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To cite this article: Huijuan Ma, Jing Qin, Fang Chen & Yong Zhou (2023) A novel nonparametric mixture model for the detection pattern of COVID-19 on Diamond Princess cruise, Statistical Theory and Related Fields, 7:1, 85-96, DOI: 10.1080/24754269.2022.2156743 To link to this article: https://doi.org/10.1080/24754269.2022.2156743