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Home Events
Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
6Dec
Seminar Calendar, Information and Innovation Management

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

6 Dec 2023 | 10:30 a.m.— 12:00 noon
KK 315, K. K. Leung Building, HKU
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SPEAKER

Dr. Zhaowei She
Assistant Professor of Operations Management
Lee Kong Chian School of Business
Singapore Management University

ABSTRACT

The COVID-19 pandemic has dealt a devastating blow to the world economy and continues to take a massive toll on human lives. To swiftly detect and contain the resurgence of outbreaks, the COVID-19 case growth rate is a key epidemiological parameter to monitor and estimate. 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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Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
About Speaker
Dr. Zhaowei She

Assistant Professor of Operations Management | Lee Kong Chian School of Business | Singapore Management University

Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
6 Dec 202310:30 a.m.— 12:00 noon
KK 315, K. K. Leung Building, HKU
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