IDOBE：传染病爆发预测基准生态系统
IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem
AI总结：
本文提出IDOBE，一个专注于爆发预测的编纂流行病时间序列数据集，通过衍生分割生成超过10000个爆发案例，采用多种信息理论和分布度量方法评估数据集多样性，并利用11种基线模型进行多时间跨度短期预测，评估NMSE、MAPE及NWIS等指标。
摘要：
本文提出IDOBE，一个专注于爆发预测的编纂流行病时间序列数据集，通过衍生分割生成超过10000个爆发案例，采用多种信息理论和分布度量方法评估数据集多样性，并利用11种基线模型进行多时间跨度短期预测，评估NMSE、MAPE及NWIS等指标。
英文摘要：
Epidemic forecasting has become an integral part of real-time infectious disease outbreak response. While collaborative ensembles composed of statistical and machine learning models have become the norm for real-time forecasting, standardized benchmark datasets for evaluating such methods are lacking. Further, there is limited understanding on performance of these methods for novel outbreaks with limited historical data. In this paper, we propose IDOBE, a curated collection of epidemiological time series focused on outbreak forecasting. IDOBE compiles from multiple data repositories spanning over a century of surveillance and across U.S. states and global locations. We perform derivative-based segmentation to generate over 10,000 outbreaks covering multiple outcomes such as cases and hospitalizations for 13 diseases. We consider a variety of information-theoretic and distributional measures to quantify the epidemiological diversity of the dataset. Finally, we perform multi-horizon short-term forecasting (1- to 4-week-ahead) through the progression of the outbreak using 11 baseline models and report on their performance. In addition to standard metrics such as NMSE and MAPE for point forecasts, we include probabilistic scoring rules such as Normalized Weighted Interval Score (NWIS) to quantify the performance. We find that MLP-based methods have the most robust performance, with statistical methods having a slight edge during the pre-peak phase. IDOBE dataset along with baselines are released publicly on https://github.com/NSSAC/IDOBE to enable standardized, reproducible benchmarking of outbreak forecasting methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
