  We propose a novel testing and containment strategy in order to contain the
spread of SARS-CoV2 while permitting large parts of the population to resume
social and economic activity. Our approach recognises the fact that testing
capacities are severely constrained in many countries. In this setting, we show
that finding the best way to utilise this limited number of tests during a
pandemic can be formulated concisely as an allocation problem. Our problem
formulation takes into account the heterogeneity of the population and uses
pooled testing to identify and isolate individuals while prioritising key
workers and individuals with a higher risk of spreading the disease. In order
to demonstrate the efficacy of our testing and containment mechanism, we
perform simulations using a network-based SIR model. Our simulations indicate
that applying our mechanism on a population of $100,000$ individuals with only
$16$ tests per day reduces the peak number of infected individuals by
approximately $20\%$, when compared to the scenario where no intervention is
implemented.
