Improving epidemic testing and containment strategies using machine learning
Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of...
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Zusammenfassung: | Containment of epidemic outbreaks entails great societal and economic costs.
Cost-effective containment strategies rely on efficiently identifying infected
individuals, making the best possible use of the available testing resources.
Therefore, quickly identifying the optimal testing strategy is of critical
importance. Here, we demonstrate that machine learning can be used to identify
which individuals are most beneficial to test, automatically and dynamically
adapting the testing strategy to the characteristics of the disease outbreak.
Specifically, we simulate an outbreak using the archetypal
susceptible-infectious-recovered (SIR) model and we use data about the first
confirmed cases to train a neural network that learns to make predictions about
the rest of the population. Using these prediction, we manage to contain the
outbreak more effectively and more quickly than with standard approaches.
Furthermore, we demonstrate how this method can be used also when there is a
possibility of reinfection (SIRS model) to efficiently eradicate an endemic
disease. |
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DOI: | 10.48550/arxiv.2011.11717 |