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
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.
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
Simon Finster
DPhil Student in Economics,
University of Oxford
Advisors
Prof. Rubén López-Revilla
IPICyT
Dr. Cesaré Ovando Vázquez
IPICyT
Dr. Philip Lazos
IOHK
Prof. Angel Alpuche Solis
IPICyT
Jakob Jonnerby
Imperial College London
Prof. Salvador Ruiz Correa
IPICyT
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
IPICyT and UNAM
Collaborators
