论文标题
使用机器学习改善流行性测试和遏制策略
Improving epidemic testing and containment strategies using machine learning
论文作者
论文摘要
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人，从而使可用测试资源的最佳使用。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以用来确定哪些人对测试，自动和动态地将测试策略适应疾病暴发的特征。具体而言，我们使用原型易感性侵袭性（SIR）模型模拟爆发，并使用有关第一个确认病例的数据来训练一个学会对其他人群进行预测的神经网络。使用这些预测，我们设法比标准方法更有效，更快地控制爆发。此外，我们证明了当有可能有效地消除特有疾病的可能性（SIRS模型）时，如何也可以使用该方法。
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.
