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Test & Contain

Increasing the effectiveness of a limited number of COVID-19 tests in resource-constrained environments


The Problem

Testing resources for viral infections are often scarce due to several factors, including limited access to reagents, shortages in trained lab technicians, and deficient logistics. At the same time, testing is an essential tool for effectively combating epidemics, as it gives crucial estimates of virus prevalence, and allows the identification of infected asymptomatic and symptomatic individuals that form the basis of sophisticated containment policies. For this reason, novel strategies are required that maximise the benefit from each available test.

Group testing

Our methodology is based on the principle of group testing. In group testing, multiple samples are pooled into a single test. The result indicates whether any given person in the pool is infected, or conversely – and more informatively – if none are infected.  

Group A

Group B

Group testing.png

Illustration of group tests of size 6. Red dots represent infected individuals. In the above scenario, a single group test on Group A will return negative, since no individual is infected. The test for Group B will return positive, since some individuals are infected.

Proven benefits

Not only is there a substantial literature regarding group testing in the Computational Learning Theory community, but the underlying method has been successfully used in practice to fight HIV. The most compelling benefit of group testing is its ability to amplify the reach of a limited number of tests to larger population segments by allocating tests to disjoint groups of individuals. 

Our solution

Two proposed solutions

Instead of determining the number of tests required for a given testing regime, we turn the problem on its head and formulate the problem of maximising the use of limited testing resources as a resource allocation problem. Our approach is designed for settings with severely limited testing capacities.

Preventative infection surveillance

Using testing resource allocation to identify infected individuals and prevent them from spreading the virus

Post-lockdown reintegration

Using testing resource allocation to identify healthy individuals who can be reintegrated into their normal routines


Publications + Working Papers

Optimal Testing and Containment Strategies for Universities in Mexico amid COVID-19

Edwin Lock, Francisco Javier Marmolejo-Cossío, Jakob Jonnerby, Ninad Rajgopal, Héctor Alonso Guzmán-Gutiérrez, Luis Alejandro Benavides-Vázquez, José Roberto Tello-Ayala, Philip Lazos

The inaugural ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '21)

Maximising the Benefits of an Acutely Limited Number of COVID-19 tests

J. Jonnerby, P. Lazos, E. Lock, F. Marmolejo-Cossio, C. Bronk Ramsey, D. Sridhar

Harvard CRCS Workshop on AI for Social Good (2020)

Finalist - Global Challenges in Economics and Computation

Welfare-maximizing pooled testing

S Finster, M. González Amador, E. Lock, F. Marmolejo-Cossio, E. Micha, A. Procaccia

ACM Conference on Economics and Computation (2023)

(Exemplary track paper award)


The Team


Research Fellow,

University of Oxford


PhD Fellow,

Maastricht University


Postdoctoral Fellow,

Harvard University


Simon Finster

DPhil Student in Economics,

University of Oxford


Prof. Rubén López-Revilla


Dr. Cesaré Ovando Vázquez


Dr. Philip Lazos


Prof. Angel Alpuche Solis


Jakob Jonnerby

Imperial College London

Prof. Salvador Ruiz Correa


Dr. Divya Sridhar

 University of Oxford

Dr. Ninad Rajgopal

University of Warwick

Luis Alejandro Benavides-Vázquez

Monterrey Institute of Technology and Higher Education

Dr. Héctor Alonso Guzmán Gutiérrez



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