Robust Combination Testing: Methods and Application to COVID-19 Detection

49 Pages Posted: 25 Jan 2022 Last revised: 24 Aug 2022

See all articles by Sanjay Jain

Sanjay Jain

University of Cambridge - Faculty of Economics

Jónas Oddur Jónasson

MIT Sloan School of Management

Jean Pauphilet

London Business School

Kamalini Ramdas

London Business School - Department of Management Science and Operations

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

Jain, Sanjay and Jónasson, Jónas Oddur and Pauphilet, Jean and Ramdas, Kamalini, Robust Combination Testing: Methods and Application to COVID-19 Detection (January 19, 2022). MIT Sloan Research Paper 6538-21, Available at SSRN: https://ssrn.com/abstract=4012658 or http://dx.doi.org/10.2139/ssrn.4012658

Sanjay Jain

University of Cambridge - Faculty of Economics ( email )

Sidgwick Avenue
Cambridge, CB3 9DD
United Kingdom

Jónas Oddur Jónasson (Contact Author)

MIT Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

Jean Pauphilet

London Business School ( email )

Sussex Place
Regent's Park
London, London NW1 4SA
United Kingdom

Kamalini Ramdas

London Business School - Department of Management Science and Operations ( email )

Sussex Place
Regent's Park
London, London NW1 4SA
United Kingdom

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
221
Abstract Views
1,125
Rank
295,653
PlumX Metrics