Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection

TL;DR

This paper introduces TLRF, a transfer learning framework using random forests to improve COVID-19 outbreak detection by accurately estimating case growth rates across different regions and times.

Contribution

The paper presents a novel transfer learning approach with adaptive window sizing for better growth rate estimation in small samples, enhancing outbreak detection accuracy.

Findings

TLRF outperforms existing methods in growth rate estimation.

Case study shows TLRF improves outbreak detection timeliness by up to 224%.

Developed a publicly available outbreak detection tool used nationwide.

Abstract

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 provide a transfer learning framework, which we call Transfer Learning Random Forest (TLRF), for an effective implementation of the random forests algorithm that balances this accuracy-speed tradeoff. Specifically, we develop an identification strategy that converts the growth rate estimation problem into a regression task, which enables effective transfer learning across space and time through random forests' adaptive weighting mechanism. As such, through adaptively choosing fitting window sizes based on relevant day-level and county-level features affecting the disease spread, TLRF can accurately estimate case growth rates for counties with small sample sizes. Out-of-sample prediction analysis shows that TLRF 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 TLRF could improve timely detections of outbreaks up to 224% when compared to the decisions made by Colorado's Department of Health and Environment (CDPHE). To demonstrate practical implementation, we developed a publicly available outbreak detection tool that operated from September 2020 through March 2023, receiving substantial attention from policymakers across all 50 states.