Group testing is a public health strategy that increases testing capacity by combining samples from multiple people into a single unit, or pool. This method allows more individuals to be screened at a lower cost and with fewer resources. The core principle is similar to checking an entire batch of products for a defect at once, rather than inspecting each item individually. If the batch passes, all items are cleared, making the approach useful for large-scale screening efforts.
The Group Testing Procedure
The most common method of group testing is a two-stage process first detailed by Robert Dorfman. The procedure begins when samples, such as blood or swabs, are collected from individuals. A small portion of each person’s sample is then combined with others to form a single pooled sample. For instance, instead of conducting 10 separate analyses for 10 people, technicians can create one pool containing material from all 10 samples.
This single pooled sample is then subjected to one test. If the result is negative, it clears every person included in the pool, meaning all 10 individuals are considered negative from that single test. This outcome saves considerable time and materials, which is the primary advantage of the strategy.
A positive result for the pool indicates that at least one person in the group is infected. When this occurs, the laboratory must go back to the original individual samples that were set aside. Each of the 10 samples in the positive pool must then be tested individually to determine which person or people are positive.
Optimal Conditions for Implementation
The effectiveness of group testing is highly dependent on the prevalence of the disease within the population being tested. The strategy is most efficient when the number of infected individuals is low. In a low-prevalence setting, the vast majority of pools will test negative, allowing large numbers of people to be cleared with a minimal number of tests.
There is a tipping point at which group testing loses its advantage. As disease prevalence rises, a greater percentage of pools will test positive, triggering the second round of individual testing for every member. If prevalence is high enough, the total number of tests performed can exceed the number of people being screened, making it less efficient than individual testing from the start.
Beyond prevalence, the technical specifications of the test itself are a consideration. The diagnostic test must have high sensitivity, which is the ability to detect a positive case even when the sample is diluted. A single positive sample is mixed with multiple negative ones, creating a dilution effect. If the test is not sensitive enough, it could produce a false negative, incorrectly clearing an infected individual. Therefore, pool size must be carefully calibrated to the test’s detection limits.
Applications in Public Health and Research
Group testing is not a new concept and has a long history of practical application in public health. Its first major use was proposed by Robert Dorfman during World War II to efficiently screen U.S. soldiers for syphilis. The strategy is also a standard procedure in modern blood and organ screening. Blood banks routinely use pooled testing to screen donated blood for infectious agents such as HIV and hepatitis viruses.
More recently, group testing was widely adopted during the COVID-19 pandemic. It was used for surveillance in settings like schools and workplaces to monitor for potential outbreaks. This allowed communities to conserve testing kits while still conducting broad monitoring to identify emerging hotspots. The method also extends to other scientific fields, including genetic research for identifying rare mutations and environmental science for testing water sources for contaminants.
Advanced Pooling Strategies
While the simple Dorfman method is common, scientists have developed more complex strategies to further improve efficiency. These advanced methods aim to identify positive cases with even fewer tests, particularly when multiple positive cases are expected within a testing cohort.
One such advanced method is matrix or 2D pooling. This approach can be visualized like a grid or a bingo card, where each sample is placed at the intersection of a unique row and column. Instead of one large pool, samples are pooled twice: once with all the samples in their row and once with all the samples in their column.
If a single individual is positive, two pools—one row and one column—will test positive. The infected person is identified by finding the intersection point of the positive row and column. This matrix design can pinpoint a positive case without requiring a second round of individual testing, offering improved efficiency over the standard procedure.