What is it about?
The Dorfman pooled testing scheme is a process in which individual specimens (e.g., blood samples from different patients) are pooled and tested together; if the merged sample tests positive for infection, each specimen from the pool is tested individually. Under small prevalence levels, this method is known to be economical compared to testing everyone individually. In my previous research (https://link.springer.com/article/10.1007/s10729-023-09650-7) I showed that, when implementing Dorfman testing, it is recommended that we pool together those with similar probabilities of infection, i.e., that we implement ordered pooling, as this procedure tends to minimizes the expected number of tests, the expected number of false negatives, and the expected number of false positive classifications. One potential limitation of implementing ordered pooling, however, is that this may incentivize some subjects to misreport their types to the tester. Indeed, if subjects wish to avoid being detected as infected, ordered pooling would incentivize them to falsely claim that they have a low probability of infection (assuming that pooled testing is subject to dilution effects). These incentives would disappear if subjects were matched randomly, regardless of their probability of infection. In this article, I derive conditions under which ordered pooling outperforms matching subjects randomly, despite these incentives.
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Why is it important?
In spite of ordered pooling being optimal, many laboratories still match samples randomly or by the order in which they arrive for testing. Even though strategic incentives may indeed diminish the benefits of implementing ordered pooling, the article shows that ordered pooling tends to outperform matching subjects randomly. So, one takeaway from this result is that ordered pooling should be implemented more often in practical settings.
Perspectives
I hope the article may help healthcare professionals make optimal decisions when it comes to implementing pooled testing. I also hope this article may inspire other researchers, in particular, Economists, who often possess a vast knowledge in Game Theory and strategic incentives, to design and/or analyze pooled testing mechanisms taking into account agents' strategic incentives to manipulate the mechanism in their favor.
Gustavo Quindere Saraiva
Pontificia Universidad Catolica de Chile
Read the Original
This page is a summary of: Strategic incentives when implementing Dorfman testing with assortative matching, Economics Letters, November 2023, Elsevier,
DOI: 10.1016/j.econlet.2023.111314.
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