Small-Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection
本文提出一种基于迁移学习随机森林的统计学习框架，用于改进小区域疫情增长率估计，能在本地数据有限时更早更可靠地发现疫情暴发，对公共卫生决策者和流行病学研究者有参考价值。
A Data-Driven Early Warning System for Disease Outbreaks Early detection of infectious disease outbreaks is essential for timely public health response, yet local case data are often sparse and noisy, making reliable monitoring difficult. In their paper, “Small-Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection,” Zhaowei She, Zilong Wang, Turgay Ayer, and Jagpreet Chhatwal propose a new statistical learning framework, that is, transfer learning random forests (TLRF), that improves the estimation of epidemic growth rates across small geographic regions. The approach combines ideas from small-area estimation and transfer learning with modern machine learning tools, specifically random forest models, to borrow information across counties and time periods. This data-driven strategy produces more stable estimates of infection growth even when local observations are limited. Using COVID-19 data from across the United States, the authors show that their method detects emerging outbreaks more quickly and reliably than conventional approaches. The results demonstrate how advanced analytics can strengthen epidemic surveillance and support faster, better-informed public health decision making.