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 predictions, 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.

遏制流行病爆发需要巨大的社会和经济成本。具有成本效益的遏制策略依赖于有效识别受感染的个体，从而最大限度地利用可用的测试资源。因此，快速确定最佳测试策略至关重要。在这里，我们证明了机器学习可用于识别哪些个体最有利于测试，自动和动态地使测试策略适应疾病爆发的特征。具体来说，我们使用原型易感感染恢复 (SIR) 模型模拟爆发，并使用有关首例确诊病例的数据来训练神经网络，该网络学习对其余人群进行预测。使用这些预测，与标准方法相比，我们设法更有效、更迅速地控制了疫情。此外，我们还演示了如何在存在再感染的可能性（SIRS 模型）时使用这种方法来有效根除地方病。