A sampling scheme for estimating the prevalence of a pandemic
Communications in Statistics: Simulation & Computation
; : 1-17, 2023.
Article
in English
| Academic Search Complete | ID: covidwho-2324534
ABSTRACT
The spread of COVID-19 makes it essential to investigate its prevalence. In such investigation research, as far as we know, the widely-used sampling methods didn't use the information sufficiently about the numbers of the previously diagnosed cases, which provides a priori information about the true numbers of infections. This motivates us to develop a new, two-stage sampling method in this paper, which utilizes the information about the distributions of both population and diagnosed cases, to investigate the prevalence more efficiently. The global likelihood sampling, a robust and efficient sampler to draw samples from any probability density function, is used in our sampling strategy, and thus, our new method can automatically adapt to the complicated distributions of population and diagnosed cases. Moreover, the corresponding estimating method is simple, which facilitates the practical implementation. Some recommendations for practical implementation are given. Finally, several simulations and a practical example verify its efficiency. [ FROM AUTHOR] Copyright of Communications in Statistics Simulation & Computation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
Full text:
Available
Collection:
Databases of international organizations
Database:
Academic Search Complete
Type of study:
Observational study
Language:
English
Journal:
Communications in Statistics: Simulation & Computation
Year:
2023
Document Type:
Article
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