On the Test Accuracy and Effective Control of the COVID-19 Pandemic: A Case Study in Singapore

Guang Cheng (), Sarah Yini Gao (), Yancheng Yuan (), Chenxiao Zhang () and Zhichao Zheng ()
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Guang Cheng: Institute of Operations Research and Analytics, National University of Singapore, Singapore 117602
Sarah Yini Gao: Lee Kong Chian School of Business, Singapore Management University, Singapore 178899
Yancheng Yuan: Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong
Chenxiao Zhang: Lee Kong Chian School of Business, Singapore Management University, Singapore 178899
Zhichao Zheng: Lee Kong Chian School of Business, Singapore Management University, Singapore 178899

Interfaces, 2022, vol. 52, issue 6, 524-538

Abstract: This study examines the impact of coronavirus disease 2019 (COVID-19) test accuracy (i.e., sensitivity and specificity) on the progression of the pandemic under two scenarios of limited and unlimited test capacity. We extend the classic susceptible– exposed–infectious–recovered model to incorporate test accuracy and compare the progression of the pandemic under various sensitivities and specificities. We find that high-sensitivity tests effectively reduce the total number of infections only with sufficient testing capacity. Nevertheless, with limited test capacity and a relatively high cross-infection rate, the total number of infected cases may increase when sensitivity is above a certain threshold. Despite the potential for higher sensitivity tests to identify more infected individuals, more false positive cases occur, which wastes limited testing capacity, slowing down the detection of infected cases. Our findings reveal that improving test sensitivity alone does not always lead to effective pandemic control, indicating that policymakers should balance the trade-off between high sensitivity and high false positive rates when designing containment measures for infectious diseases, such as COVID-19, particularly when navigating limited test capacity.

Keywords: COVID-19; test sensitivity; test specificity; infections (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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