最多等待 10 秒，若超时请稍后重试。
Vascular Biology Program and Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Department of Physics and Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115, USA.
NPJ Digit Med. 2025 Apr 11;8(1):204. doi: 10.1038/s41746-025-01606-1.
The novel coronavirus (COVID-19) pandemic has had a devastating global impact, profoundly affecting daily life, healthcare systems, and public health infrastructure. Despite the availability of treatments and vaccines, hospitalizations and deaths continue. Real-time surveillance of infection trends supports resource allocation and mitigation strategies, but reliable forecasting remains a challenge. While deep learning has advanced time-series forecasting, its effectiveness relies on large datasets, a significant obstacle given the pandemic's evolving nature. Most models use national or state-level data, limiting both dataset size and the granularity of insights. To address this, we propose the Fine-Grained Infection Forecast Network (FIGI-Net), a stacked bidirectional LSTM structure designed to leverage county-level data to produce daily forecasts up to two weeks in advance. FIGI-Net outperforms existing models, accurately predicting sudden changes such as new outbreaks or peaks, a capability many state-of-the-art models lack. This approach could enhance public health responses and outbreak preparedness.
新型冠状病毒（COVID-19）大流行对全球产生了毁灭性影响，深刻影响了日常生活、医疗系统和公共卫生基础设施。尽管有治疗方法和疫苗，但住院和死亡情况仍在持续。对感染趋势的实时监测有助于资源分配和缓解策略，但可靠的预测仍然是一项挑战。虽然深度学习推动了时间序列预测的发展，但其有效性依赖于大型数据集，鉴于大流行不断演变的性质，这是一个重大障碍。大多数模型使用国家或州级数据，这限制了数据集的大小和洞察的粒度。为解决这一问题，我们提出了细粒度感染预测网络（FIGI-Net），这是一种堆叠双向长短期记忆网络结构，旨在利用县级数据提前两周进行每日预测。FIGI-Net优于现有模型，能够准确预测新疫情爆发或高峰等突然变化，而这是许多最先进模型所缺乏的能力。这种方法可以加强公共卫生应对措施和疫情防范能力。
