Small Area Estimation of Case Growths for Timely COVID-19 Outbreak
  Detection
Zhaowei She Zilong Wang Jagpreet Chhatwal Turgay Ayer
The COVID-19 pandemic has exerted a profound impact on the global economy and
continues to exact a significant toll on human lives. The COVID-19 case growth
rate stands as a key epidemiological parameter to estimate and monitor for
effective detection and containment of the resurgence of outbreaks. A
fundamental challenge in growth rate estimation and hence outbreak detection is
balancing the accuracy-speed tradeoff, where accuracy typically degrades with
shorter fitting windows. In this paper, we develop a machine learning (ML)
algorithm, which we call Transfer Learning Generalized Random Forest (TLGRF),
that balances this accuracy-speed tradeoff. Specifically, we estimate the
instantaneous COVID-19 exponential growth rate for each U.S. county by using
TLGRF that chooses an adaptive fitting window size based on relevant day-level
and county-level features affecting the disease spread. Through transfer
learning, TLGRF can accurately estimate case growth rates for counties with
small sample sizes. Out-of-sample prediction analysis shows that TLGRF
outperforms established growth rate estimation methods. Furthermore, we
conducted a case study based on outbreak case data from the state of Colorado
and showed that the timely detection of outbreaks could have been improved by
up to 224% using TLGRF when compared to the decisions made by Colorado's
Department of Health and Environment (CDPHE). To facilitate implementation, we
have developed a publicly available outbreak detection tool for timely
detection of COVID-19 outbreaks in each U.S. county, which received substantial
attention from policymakers.