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Journal of Statistics and Management Systems cover

Journal of Statistics and Management Systems

(Since 1998)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

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

Issues up to 2022 co-published with and available at:Taylor & Francis Online
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Journal of Statistics and Management Systems
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

SensitivitySpecificityPredictive valuesCOVID-19Diagnostic testsBayes’ theorem

Subject Classifications

62P10

References

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[2] J. A. Bielicki, X. Duval, N. Gobat, H. Goossens, M. Koopmans, E. Tacconelli, and S. van der Werf, “Monitoring approaches for health-care workers during the COVID-19 pandemic,” The Lancet Infectious Diseases, vol. 20, no. 10, pp. e261–e267 (Oct. 2020). [Online]. Available: https://doi.org/10.1016/S1473-3099(20)30458-8.
[3] N. Sharma, P. Sharma, S. Basu, S. Saxena, R. Chawla, K. Dushyant, N. Mundeja, Z. Marak, S. Singh, G. Singh, and R. Rustagi, “The seroprevalence of severe acute respiratory syndrome coronavirus 2 in Delhi, India: A repeated population-based seroepidemiological study,” Transactions of The Royal Society of Tropical Medicine and Hygiene, vol. 116, no. 3, pp. 242–251 (Mar. 2022). [Online]. Available: https://doi.org/10.1093/trstmh/trab109.
[4] R. Trevethan, ”Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice,” Frontiers in Public Health, vol. 5, p. 307 (2017). [Online]. Available: https://doi.org/10.3389/ fpubh.2017.00307.
[5] Kaggle, ”COVID-19 dataset,” [Online]. Available: https://www.kaggle. com/.
[6] covid19india.org, covid19india.org website, [Online].Available: https://www.covid19india.org/

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