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 Journal of Statistics and Management Systems cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

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Open Access Research Article

Calculating sensitivity, specificity and predictive values for coronavirus tests

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pp. 959–969Vol. 28Issue 5July 2025DOI: 10.47974/JSMS-1432XML
Received:
06 Aug 2024
Published Online:
08 Jul 2025
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1432
Pages:
959–969

Abstract

The accurate diagnosis of COVID-19 is essential for effective disease management and control. This study evaluates the performance of diagnostic tests using sensitivity, specificity, and predictive values along with their 95% confidence intervals (CI), applying Bayes’ Rule to estimate the probability of an individual being COVID-positive based on specific symptoms. Data were collected during the second wave of COVID-19 in India, focusing on the National Capital Region (NCR), India, through a survey conducted via Google Forms. The study identifies common symptoms to predict COVID-19 and calculates the probability of a person being COVID-positive if they exhibit these symptoms. Furthermore, it demonstrates the utility of Bayesian methods [1][2] in enhancing diagnostic accuracy by synthesizing results from the tests. These findings underscore the importance of accurate diagnostic tools and symptom recognition in managing COVID-19 and provide valuable insights into the progression of the pandemic during its second wave in India.

Keywords

Subject Classifications

62P10

References

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