论文标题
使用广义随机森林估算县级COVID-19指数增长率
Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests
论文作者
论文摘要
快速准确地发现社区爆发对于解决Covid-19的复兴浪潮的威胁至关重要。爆发检测中的一个实用挑战是平衡准确性与速度。特别是，尽管估计精度随着较长的窗口而提高，但速度会降低。本文提出了一个机器学习框架，以使用广义随机森林（GRF）来平衡这一权衡，并将其应用于县级Covid-19-19爆发。该算法根据影响疾病传播的相关特征为每个县选择一个自适应拟合窗口大小，例如社会疏远政策的变化。实验结果表明，我们的方法的表现优于前7天Covid-19-19爆发案例数预测的任何非自适应窗口大小的选择。
Rapid and accurate detection of community outbreaks is critical to address the threat of resurgent waves of COVID-19. A practical challenge in outbreak detection is balancing accuracy vs. speed. In particular, while estimation accuracy improves with longer fitting windows, speed degrades. This paper presents a machine learning framework to balance this tradeoff using generalized random forests (GRF), and applies it to detect county level COVID-19 outbreaks. This algorithm chooses an adaptive fitting window size for each county based on relevant features affecting the disease spread, such as changes in social distancing policies. Experiment results show that our method outperforms any non-adaptive window size choices in 7-day ahead COVID-19 outbreak case number predictions.
