Optimal Multi-Stage Group Partition for Efficient Coronavirus Screening

The recent outbreak of COVID-19 has affected the economy worldwide due to entire countries being on lockdown. This has been highly challenging for governments with constraints in terms of time and resources like the testing kits for the virus. Employing dynamic programming, this paper develops an optimal method for multiple-stage group partition for coronavirus screening. That is, in each stage, a group of people is divided into a certain number of subgroups, each will be tested as a whole. Only the subgroup(s) tested positive will be further divided into smaller subgroups in the next stage or individuals at the last stage. Our multiple-stage group partition scheme is able to minimize the total number of test kits and the number of stages. Stated differently, our scheme can help solve the test kit shortage problem and save time. Finally, numerical examples with useful managerial insights for further investigation are presented. The results confirm the advantages of the multi-stage sampling method over the existing binary tree method