Test and Contain: A Resource-Optimal Testing Strategy for COVID-19

Citation:

Jonnerby J, Lazos P, Lock E, Marmolejo-Cossío F, Ramsey CB, Sridhar D. Test and Contain: A Resource-Optimal Testing Strategy for COVID-19, in AI for Social Good Workshop. ; 2020.

Abstract:

We propose a novel testing and containment strategy to limit the spread of SARS-CoV2 while minimising the impact on the social and economic fabric of countries struggling with the pandemic. Our approach recognises the fact that testing capacities in many low and middle-income countries (LMICs) are severely constrained. In this setting, we show that the best way to utilise a limited number of tests during a pandemic can be found by solving an allocation problem. Our problem formulation takes into account the heterogeneity of the population and uses pooled testing to identify and isolate individuals while prioritising key workers and individuals with a higher risk of spreading the disease. In order to demonstrate the efficacy of our strategy, we perform simulations using a network-based SIR model. Our simulations indicate that applying our mechanism to a population of 10, 000 individuals with only 1 test per day reduces the peak number of infected individuals by approximately 27%, when compared to the scenario where no intervention is implemented, and requires at most 2% of the population to self-isolate at any given point.

Back to AI for Social Good event