Group testing with nested pools

TL;DR

This paper analyzes optimal nested pooling strategies for group testing to efficiently identify infected individuals, providing explicit schemes and conjectures supported by numerical evidence, with costs approaching theoretical lower bounds.

Contribution

It characterizes the optimal nested pooling schemes for different infection probabilities and proposes a conjecture on the structure of these optimal schemes, supported by extensive numerical evidence.

Findings

Optimal schemes are among four explicitly described options.

Cost of best schemes scales as O(p log(1/p)), close to entropy bounds.

Conjecture on the structure of optimal schemes is supported by numerical evidence.

Abstract

In order to identify the infected individuals of a population, their samples are divided in equally sized groups called pools and a single laboratory test is applied to each pool. Individuals whose samples belong to pools that test negative are declared healthy, while each pool that tests positive is divided into smaller, equally sized pools which are tested in the next stage. In the $(k+1)$-th stage all remaining samples are tested. If $p<1-3^{-1/3}$, we minimize the expected number of tests per individual as a function of the number $k+1$ of stages, and of the pool sizes in the first $k$ stages. We show that for each $p\in (0, 1-3^{-1/3})$ the optimal choice is one of four possible schemes, which are explicitly described. We conjecture that for each $p$, the optimal choice is one of the two sequences of pool sizes $(3^k\text{ or }3^{k-1}4,3^{k-1},\dots,3^2,3 )$, with a precise description of the range of $p$'s where each is optimal. The conjecture is supported by overwhelming numerical evidence for $p>2^{-51}$. We also show that the cost of the best among the schemes $(3^k,\dots,3)$ is of order $O\big(p\log(1/p)\big)$, comparable to the information theoretical lower bound $p\log_2(1/p)+(1-p)\log_2(1/(1-p))$, the entropy of a Bernoulli$(p)$ random variable.