Working Paper: NBER ID: w27457
Authors: Ned Augenblick; Jonathan T. Kolstad; Ziad Obermeyer; Ao Wang
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.
Keywords: group testing; COVID-19; machine learning; testing efficiency; public health
JEL Codes: I1; I18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| increased testing frequency (C22) | lower prevalence (I12) |
| lower prevalence (I12) | lower expected number of tests needed (C12) |
| increased testing frequency (C22) | lower expected number of tests needed (C12) |
| intragroup risk correlation (C92) | increased testing efficiency (C90) |