Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning

20 Pages Posted: 12 Jan 2021

See all articles by Ned Augenblick

Ned Augenblick

University of California, Berkeley - Economic Analysis & Policy Group

Jonathan Kolstad

University of California, Berkeley - Haas School of Business; National Bureau of Economic Research (NBER)

Ziad Obermeyer

University of California, Berkeley

Ao Wang

University of California, Berkeley

Multiple version iconThere are 2 versions of this paper

Date Written: July 2020

Abstract

Group testing increases efficiency by pooling patient specimens and clearing the entire group with one negative test. Optimal grouping strategy is well studied in one-off testing scenarios with reasonably well-known prevalence rates and no correlations in risk. We discuss how the strategy changes in a pandemic environment with repeated testing, rapid local infection spread, and highly uncertain risk. First, repeated testing mechanically lowers prevalence at the time of the next test. This increases testing efficiency, such that increasing frequency by x times only increases expected tests by around √x rather than x. However, this calculation omits a further benefit of frequent testing: infected people are quickly removed from the population, which lowers prevalence and generates further efficiency. Accounting for this decline in intra-group spread, we show that increasing frequency can paradoxically reduce the total testing cost. Second, we show that group size and efficiency increases with intra-group risk correlation, which is expected in natural test groupings based on proximity. Third, because optimal groupings depend on uncertain risk and correlation, we show how better estimates from machine learning can drive large efficiency gains. We conclude that frequent group testing, aided by machine learning, is a promising and inexpensive surveillance strategy.

Suggested Citation

Augenblick, Ned and Kolstad, Jonathan and Obermeyer, Ziad and Wang, Ao, Group Testing in a Pandemic: The Role of Frequent Testing, Correlated Risk, and Machine Learning (July 2020). NBER Working Paper No. w27457, Available at SSRN: https://ssrn.com/abstract=3763836

Ned Augenblick (Contact Author)

University of California, Berkeley - Economic Analysis & Policy Group ( email )

Berkeley, CA 94720
United States

Jonathan Kolstad

University of California, Berkeley - Haas School of Business ( email )

545 Student Services Building, #1900
2220 Piedmont Avenue
Berkeley, CA 94720
United States

National Bureau of Economic Research (NBER)

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Cambridge, MA 02138
United States

Ziad Obermeyer

University of California, Berkeley ( email )

310 Barrows Hall
Berkeley, CA 94720
United States

Ao Wang

University of California, Berkeley

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