摘要翻译：
快速准确地检测社区疫情对于应对新冠肺炎疫情死灰复燃的威胁至关重要。疫情检测的一个实际挑战是平衡准确性和速度。特别是，随着拟合窗口的延长，估计精度提高，但速度下降。本文提出了一个基于广义随机森林(GRF)的机器学习框架来平衡这种权衡，并将其应用于县级新冠肺炎疫情的检测。该算法根据影响疾病传播的相关特征，如社交距离政策的变化，为每个县选择自适应拟合窗口大小。实验结果表明，在新冠肺炎疫情爆发前7天的病例数预测中，我们的方法优于任何非自适应窗口大小选择。
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英文标题：
《Estimating County-Level COVID-19 Exponential Growth Rates Using
  Generalized Random Forests》
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作者：
Zhaowei She, Zilong Wang, Turgay Ayer, Asmae Toumi, Jagpreet Chhatwal
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最新提交年份：
2020
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  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.
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