  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.
