  Detection of rare traits or diseases in a large population is challenging.
Pool testing allows covering larger swathes of population at a reduced cost,
while simplifying logistics. However, testing precision decreases as it becomes
unclear which member of a pool made the global test positive.
  In this paper we discuss testing strategies that provably approach
best-possible strategy - optimal in the sense that no other strategy can give
exact results with fewer tests. Our algorithms guarantee that they provide a
complete and exact result for every individual, without exceeding $1/0.99$
times the number of tests the optimal strategy would require.
  This threshold is arbitrary: algorithms closer to the optimal bound can be
described, however their complexity increases, making them less practical.
  Moreover, the way the algorithms process input samples leads to some
individuals' status to be known sooner, thus allowing to take urgency into
account when assigning individuals to tests.
