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

• URL: http://arxiv.org/abs/2312.04110v1
• Date: Thu, 7 Dec 2023 07:53:00 GMT
• Title: Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
• Authors: Zhaowei She, Zilong Wang, Jagpreet Chhatwal, Turgay Ayer
• Abstract summary: 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. We develop a machine learning algorithm, which we call Transfer Learning Generalized Random Forest (TLGRF), that balances this accuracy-speed tradeoff.
• Score: 4.478818286947472
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 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.

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