Pooled testing to unlock economy

This time-tested method will help us leverage our scarce resources to deliver millions of tests in a short time

By Author Tarun Jain   |   Published: 3rd Jun 2020   12:05 am Updated: 2nd Jun 2020   11:46 pm

As India exits a historic lockdown to halt the spread of Covid-19, the country must look ahead to a long-term strategy to prevent the spread of the disease. The lockdown offered the government space to identify infected individuals, isolate them and trace their contacts for further testing. This is a laborious process, currently yielding 35 tests for every one lakh individuals. In Telangana, the number of tests conducted by the middle of May was one-tenth compared with Tamil Nadu, Andhra Pradesh and Karnataka.

The unlock phase will involve large-scale community testing while the economy reopens. Such testing is critical for several reasons. First, large-scale testing will identify those infected, so that they can be quarantined or treated, especially if they missed being identified in the first wave of targeted testing. Second, community testing will deliver warning signs of community transmission or resurgence of the virus. These early warning signs will help develop policies in response.

Third, community testing, even if it delivers large numbers of negative results, will be critical in reassuring many workers, and their employers, of their infection status, and facilitate their return to the workplace. An “all-clear” is what many people want to hear right now.

Possible Solution

Yet test kits, laboratory time and trained personnel are in short supply. The promise of rapid antibody tests has fizzled out. The most common test, RT-PCR, takes hours to deliver results, even as the tests are run in large batches in a small number of sophisticated laboratories. Not every district has lab facilities or trained technicians.

In Telangana, private labs have been barred from conducting tests. These practical constraints make community testing difficult. In these circumstances, how can we leverage scarce resources to deliver millions of tests in a short time?

One answer is pooled testing (also called group testing). The idea involves pooling together samples from multiple individuals and testing it as a single sample. If the test is negative, then all individuals are assumed to be and declared infection-free. In rare instances, where the test is positive, then individual tests can be performed to identify those who are infected. Since a negative outcome on a pooled test is far more common, pooled testing can save millions of test kits, millions of hours of laboratory time and effort by lab technicians.

Pooled testing

Positive Results

The approach of pooled testing is well-known and time-tested. First developed by economist Robert Dorfman of Harvard University in the 1940s to test for syphilis among American military soldiers, group testing has been repeatedly validated for public health use and analysed by statisticians and computer scientists, the latter developing wireless network protocols.

Stanford University in California and Technion in Israel reported positive results from laboratory tests of pooled testing specifically for Covid-19. In India, the Centre for Cellular and Molecular Biology in Hyderabad and other institutions have conducted pooled tests, and the Indian Council of Medical Research (ICMR) has issued the relevant guidelines.

The ICMR guidelines suggest pooling together five samples in a single test. This will require 75% fewer test kits with a 1% infection rate, and 57% fewer kits with a 5% infection rate.

Testing Teams

Pooled testing of high-risk groups would be particularly fruitful as India unlocks in phases and economic activities restart. Here are some specific sectors where pooled testing could help in ruling out infection of teams:

• Maintaining an infection-free healthcare workforce is critical. Doctors and nurses face inordinate risks from exposure to Covid-positive patients, and can also spread the infection when they come in contact with others. Frequent testing is the first step. Medical and health workers (all at the same hospitals) could be collectively tested every week – if the test is negative, all are clear to continue duties. This will reassure not only healthcare workers but also stop potential transmission to patients.

• Police personnel bear the brunt of enforcing the lockdown and have been infected at disproportionate rates around the country. A single infection implies that everyone around them is quarantined, which severely impacts police operations. Frequent collective testing of all those attached to the same station will help clear everyone for further duty, or treatment if required.

• Students, especially in residential schools and colleges, live and study in close contact where social distancing is difficult. Infectious diseases spread quickly among students. They could be collectively tested as colleges restart. If the test of the pooled sample is negative, all students can attend classes and stay in hostels.

• In the unlock phase, factories will resume production, potentially with workers who are positive. Factory owners could test all workers at a factory collectively. If all are negative, then operations can resume, and facilitate economic recovery. In this case, the cost of the test should be borne by factory owners rather than workers. After all, testing everyone and having a safe workplace that is open is surely cheaper than a factory that is shut and sealed owing to Covid infection. The same principle applies to offices and other workplaces.

Test Quick

The efficiency of pooled testing can be boosted further. For example, if a couple is tested together, then a positive result should be interpreted as positive for both since the quarantine would be together as well.

One downside of pooled testing is that the number of test kits saved decreases as the infection rates increase. So the strategy will work better if implemented right after the lockdown ends, rather than try and wait for the infection to spread. Another concern is that the test sensitivity decreases as the pools become larger, so the strategy works best with higher quality tests.

(The author is Associate Professor of Economics at Indian Institute of Management, Ahmedabad)


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