Effective Smart-Testing Strategies for Reducing Infection and Death Rates During Pandemics

In the midst of the ongoing COVID-19 pandemic, it has become increasingly clear that traditional testing methods are insufficient in containing the spread of the virus. As such, researchers have been exploring alternative approaches to identifying infected individuals more efficiently. One promising strategy is smart-testing, which uses data-driven algorithms to rapidly trace infected individuals and reduce infection and death rates.

In a recent study, our team demonstrated the effectiveness of smart-testing in significantly reducing infection and death rates compared to current policies, with or without vaccination. Using an agent-based model simulating New York City, we showed that the algorithm samples individuals to test in a manner that rapidly identifies infected individuals. This active sampling method works in conjunction with vaccination and quarantine policies, making it adaptive to changes in real-time data.

The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. These approaches have been widely applied in various fields, but their application to pandemic control is relatively new. By leveraging these techniques, we can choose individuals to test for infection more effectively during a pandemic like COVID-19.

Our research presents methods for choosing individuals to test for infection during a pandemic characterized by high contagion and the presence of asymptomatic carriers. These methods have significant implications for public health policy and could potentially save lives by reducing the spread of infectious diseases.

Overall, smart-testing offers a promising solution for combating pandemics like COVID-19. By combining data-driven algorithms with vaccination and quarantine policies, we can reduce infection and death rates and ultimately save lives.

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