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Inspection plan for COVID-19 patients for Weibull distribution using repetitive sampling under indeterminacy.
Rao, G Srinivasa; Aslam, Muhammad.
  • Rao GS; Department of Statistics, University of Dodoma, PO. Box: 259, Dodoma, Tanzania.
  • Aslam M; Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, 21551, Saudi Arabia. aslam_ravian@hotmail.com.
BMC Med Res Methodol ; 21(1): 229, 2021 10 25.
Article in English | MEDLINE | ID: covidwho-1484301
ABSTRACT

BACKGROUND:

This research work is elaborated investigation of COVID-19 data for Weibull distribution under indeterminacy using time truncated repetitive sampling plan. The proposed design parameters like sample size, acceptance sample number and rejection sample number are obtained for known indeterminacy parameter.

METHODS:

The plan parameters and corresponding tables are developed for specified indeterminacy parametric values. The conclusion from the outcome of the proposed design is that when indeterminacy values increase the average sample number (ASN) reduces.

RESULTS:

The proposed repetitive sampling plan methodology application is given using COVID-19 data belong to Italy. The efficiency of the proposed sampling plan is compared with the existing sampling plans.

CONCLUSIONS:

Using the tables and COVID-19 data illustration, it is concluded that the proposed plan required a smaller sample size as examined with the available sampling plans in the literature.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: BMC Med Res Methodol Journal subject: Medicine Year: 2021 Document Type: Article Affiliation country: S12874-021-01387-7

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Prognostic study Limits: Humans Country/Region as subject: Europa Language: English Journal: BMC Med Res Methodol Journal subject: Medicine Year: 2021 Document Type: Article Affiliation country: S12874-021-01387-7