Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests

• URL: http://arxiv.org/abs/2011.01219v4
• Date: Sat, 14 Nov 2020 17:22:17 GMT
• Title: Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests
• Authors: Zhaowei She, Zilong Wang, Turgay Ayer, Asmae Toumi, Jagpreet Chhatwal
• Abstract summary: A practical challenge in outbreak detection is balancing accuracy vs. speed. This paper presents a machine learning framework to balance this tradeoff using generalized random forests (GRF) Algorithm chooses an adaptive fitting window size for each county based on relevant features affecting the disease spread. Experiment results show that our method outperforms any non-adaptive window size choices in 7-day ahead COVID-19 outbreak case number predictions.
• Score: 2.1379208289090594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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