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Inspection plan for COVID-19 patients for Weibull distribution using repetitive sampling under indeterminacy
G Srinivasa Rao1, Muhammad Aslam2
1Department of Statistics, University of Dodoma, PO. Box: 259, Dodoma, Tanzania.
BMC Medical Research Methodology
|October 26, 2021
Summary
This study introduces a new COVID-19 sampling plan using Weibull distribution under uncertainty. The proposed method reduces the average sample number (ASN) as uncertainty increases, requiring a smaller sample size than existing plans.
Area of Science:
- Statistics
- Epidemiology
- Quality Control
Background:
- Investigates COVID-19 data using Weibull distribution under indeterminacy.
- Focuses on a time-truncated repetitive sampling plan for uncertainty.
- Determines design parameters like sample size, acceptance, and rejection numbers.
Purpose of the Study:
- To develop and evaluate a novel repetitive sampling plan for COVID-19 data analysis under uncertainty.
- To assess the impact of indeterminacy on sampling plan efficiency.
- To compare the proposed plan with existing methodologies.
Main Methods:
- Developed plan parameters and tables for specified indeterminacy values.
- Calculated design parameters for known indeterminacy.
- Applied the methodology to COVID-19 data from Italy.
Main Results:
- The average sample number (ASN) decreases as indeterminacy values increase.
- The proposed sampling plan was applied to real-world COVID-19 data.
- Efficiency was compared against existing sampling plans.
Conclusions:
- The proposed repetitive sampling plan requires a smaller sample size compared to existing plans.
- The plan is effective for analyzing COVID-19 data under uncertainty.
- Demonstrates practical application and efficiency gains.
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