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

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Listed:
  • Zhaowei She
  • Zilong Wang
  • Turgay Ayer
  • Asmae Toumi
  • Jagpreet Chhatwal

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.

Suggested Citation

  • Zhaowei She & Zilong Wang & Turgay Ayer & Asmae Toumi & Jagpreet Chhatwal, 2020. "Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests," Papers 2011.01219, arXiv.org, revised Nov 2020.
    • Handle: RePEc:arx:papers:2011.01219
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    References listed on IDEAS

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    1. Abadie, Alberto, 2003. "Semiparametric instrumental variable estimation of treatment response models," Journal of Econometrics, Elsevier, vol. 113(2), pages 231-263, April.
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