Improving epidemic testing and containment strategies using machine learning

Laura Natali; Saga Helgadottir; Onofrio Marago; Giovanni Volpe

doi:10.1117/12.2593594

1 August 2021 Improving epidemic testing and containment strategies using machine learning

Laura Natali, Saga Helgadottir, Onofrio Marago, Giovanni Volpe

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041B (2021) https://doi.org/10.1117/12.2593594
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

We present an application of machine learning to deal with the optimization of testing strategies in the event of large-scale epidemic outbreaks. We describe the disease using the archetypal SIR model. Cost-effective containment relies on making the best possible use of the available resources to identify infectious cases. We present a neural-network-powered strategy that adapts to an epidemic without knowing the underlying parameters of the model. The neural network results are more effective than standard approaches, also in the presence of asymptomatic cases. We envision that similar methods can be employed in public health to control epidemic outbreaks.

Conference Presentation

Citation Download Citation

Laura Natali, Saga Helgadottir, Onofrio Marago, and Giovanni Volpe "Improving epidemic testing and containment strategies using machine learning", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041B (1 August 2021); https://doi.org/10.1117/12.2593594

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