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 individuals reintegrating into normalcy during or after a wave of the pandemic. Indeed, a negative testing can serve as proof of the individual's ability to resume work, return to studies, or participate in normal everyday activity. Ultimately, novel strategies are required to maximise the benefit from each available test, to allow maximum number of people to resume their normal lives, while also balancing the different needs of different individuals in the population.
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.
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.
Under the supervision of Prof. Angel Alpuche Solis, the Laboratorio Nacional de biotecnología agrícola,médica y ambiental (LANBAMA) has led efforts to implement the group testing solution via a key concentration step, as per research from the Oxford University Pharmacology Department.
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.
The proposed solution
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.
Compute individual's utility and infection risk
Our testing strategy requires computing each individual's utility of normal activity over lockdown as well as their probability of infection.
Compute Optimal Testing Strategy
Based on these population characteristics, we provide an optimal allocation of group tests to the population, under the containment assumption that only those in negative tests will return to in-person activities.
Implement Tests and Communicate Results
We help the LANBAMA coordinate the group tests required, and communicate the results to relevant individuals.
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
Maximizing the utility of limited COVID-19 tests for post-lockdown reintegration: Using tests to find the healthy
E. Lock, F. Marmolejo-Cossio, E. Micha, A. Procaccia
DPhil Student in Economics,
University of Oxford
Prof. Rubén López-Revilla
Dr. Cesaré Ovando Vázquez
Prof. Angel Alpuche Solis
Prof. Ariel Procaccia
Prof. Salvador Ruiz Correa
University of Toronto