Download This PaperRobust Combination Testing: Methods and Application to COVID-19 Detection
49 Pages Posted: 25 Jan 2022 Last revised: 24 Aug 2022
Date Written: January 19, 2022
Abstract
Situations where simple and affordable testing tools are available but not accurate enough to be operationally relevant are ubiquitous. For COVID-19 detection, rapid point-of-care tests are cheap and provide results in minutes, but largely fail policymakers' accuracy requirements. We propose an analytical methodology, based on robust optimization, that provides a structured way for policymakers to break this trade-off by optimally combining results from cheap tests for increased predictive accuracy. Our methodology is robust to noisy and partially missing input data and incorporates operational constraints--relevant considerations in practice. We apply our methodology to two datasets containing individual-level results of multiple COVID-19 rapid antibody and antigen tests, respectively, to generate Pareto-dominating receiver operating characteristic (ROC) curves. We find that combining only three rapid tests increases out-of-sample area under the curve (AUC) by 4% (6%) compared with the best performing individual test for antibody (antigen) detection. We also find that a policymaker who requires specificity of at least 0.9 can improve sensitivity by 14% and 10% for antibody and antigen testing, respectively, relative to available combination testing heuristics. Our numerical analysis demonstrates that robust optimization is a powerful tool to avoid overfitting, accommodate missing data, and improve out-of-sample performance. Based on our analytical and empirical results, policymakers should consider approving and deploying a curated combination of cheap point-of-care tests in settings where `gold standard' tests are too expensive.
Note:
Funding: None to declare.
Declaration of Interests: None to declare.
Keywords: Diagnostic Operations, Combination Testing, Knapsack, Robust Optimization, Healthcare Analytics
Suggested Citation: Suggested Citation
Kamalini Ramdas
London Business School - Department of Management Science and Operations ( email )
Sussex Place
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London, London NW1 4SA
United Kingdom