Connecting Harvard, Israel, and COVID

The debate on how to test for COVID-19 is one of today’s greatest controversies. The goal is clear, to find the most efficient, effective, and accurate form of mass testing, in a dramatically expedited fashion. While governments worldwide have taken their own approaches, there is indeed a science behind these methods, and Israel has found itself on a new side of this debate.

Harvard University’s Robert Dorfman (1916-2002) has been making headlines lately with quotes and references, including a feature in The New York Times. Acclaimed as a significant figure in the development of political economics, statistics, group testing, and the process of coding theory, Dorfman completed his time at Harvard University with landmark discoveries that have become overwhelmingly relevant today.

Outlined in his paper, ‘The Detection of Defective Members of Large Populations’ Dorfman offers his solution to mass testing. The process is as such: in place of testing all individuals uniquely, which invites enormous efforts, expenses, and time, Dorfman’s method groups individuals into pools. These pools of individuals combine their blood samples to be tested as one single submission. If the result is negative, then all parties are negative. If the result is positive, then the pool will need to be revisited for additional testing.

Israel, by contrast, has developed a new algorithmically-optimized method called P-Best (Pooling-Based Efficient SARS-CoV-2 Testing). Outlined in a recent study published in the journal Science Advances,  Israeli expert scientists offer their review of P-Best.

This newly created method of mass testing challenges Dorfman’s Harvard study, as well as the unique global approaches to COVID-19 testing. In the most basic of definitions, P-Best testing does so by breaking up large groups of individuals into equal segments, with each person’s sample present in multiple pools.

After testing is completed, if one individual is positive, then each of the pools in which the individual was present will show up as positive. Finally, with the proper algorithmic approach, scientists can pinpoint individuals who are indeed positive.

There do exist a finite number of cases where this approach’s effectiveness would be thwarted, however, its efficiency and effectiveness do present opportunities for reduced financial strain. Meaning that the P-Best testing approach could be a government’s solution in a time of dramatic economic strain paired with urgency for receiving test results.