生成可配置的疫情模拟数据，用于对比传统模型与机器学习方法。
BIGBOY1.2: Generating Realistic Synthetic Data for Disease Outbreak Modelling and Analytics
- 基于SEIR/SIR框架构建可调节的疫情时间序列
- 支持季节性波动与真实报告噪声，增强数据真实性
- 适合评估传染病模型与机器学习算法性能
由于监测数据不完整、存在噪声且缺乏标准化数据集，疾病暴发建模仍具挑战。我们开发了BIGBOY1.2，一个开源的合成数据生成器，可生成可配置的流行病时间序列和人群级轨迹，适用于建模、预测与可视化任务的基准测试。该框架支持SEIR和SIR类分室逻辑、自定义季节性特征以及噪声注入，以模拟真实报告偏差。BIGBOY1.2可生成具有多样化特征的数据集，适用于对比传统流行病学模型（如SIR、SEIR）与现代机器学习方法（如SVM、神经网络）的性能。
原文摘要 · Abstract (English)
Modelling disease outbreak models remains challenging due to incomplete surveillance data, noise, and limited access to standardized datasets. We have created BIGBOY1.2, an open synthetic dataset generator that creates configurable epidemic time series and population-level trajectories suitable for benchmarking modelling, forecasting, and visualisation. The framework supports SEIR and SIR-like compartmental logic, custom seasonality, and noise injection to mimic real reporting artifacts. BIGBOY1.2 can produce datasets with diverse characteristics, making it suitable for comparing traditional epidemiological models (e.g., SIR, SEIR) with modern machine learning approaches (e.g., SVM, neural networks).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。