准确的流行病预测对于公共卫生响应,资源分配,和疫情干预,至关重要，但由于稀疏,噪声,和高度非平稳数据，仍然很困难。由于流行病在相互作用的区域中蔓延，, 时空方法自然成为改进预测的候选方法。尽管人们对空间信息的兴趣日益浓厚,，但不存在标准化基准,，并且当前的评估通常使用简单的按时间顺序排列的训练测试分割，而这些分割并不反映实时预测实践。我们通过 SpatialEpiBench, 解决了这一差距，这是现实公共卫生环境中时空流行病预测的具有挑战性的基准。 SpatialEpiBench 包括 11 个流行病数据集，具有标准化滚动评估和特定于疫情的指标。我们使用广泛使用的流行病先验来评估邻接知情预测模型，这些先验使一般模型适应流行病学,，但发现大多数方法在提前 1 天到 1 个月, 的情况下表现不佳，即使在疫情爆发期间并使用这些先验。我们确定了三种主要失败模式: (1) 疫情爆发预期不佳, (2) 处理稀疏性和噪音, 困难，(3) 流行病学空间信息的常见地理邻接的效用有限。我们在此 https URL 发布基准数据, 代码, 和说明，以支持开发可操作的流行病预测模型。
Accurate epidemic forecasting is crucial for public health response, resource allocation, and outbreak intervention, but remains difficult with sparse, noisy, and highly non-stationary data. Because epidemics unfold across interacting regions, spatiotemporal methods are natural candidates for improving forecasts. Despite growing interest in spatial information, no standardized benchmark exists, and current evaluations often use simple chronological train-test splits that do not reflect real-time forecasting practice. We address this gap with SpatialEpiBench, a challenging benchmark for spatiotemporal epidemic forecasting in realistic public-health settings. SpatialEpiBench includes 11 epidemic datasets with standardized rolling evaluations and outbreak-specific metrics. We evaluate adjacency-informed forecasting models with widely used epidemic priors that adapt general models to epidemiology, but find that most methods underperform a simple last-value baseline from 1 day to 1 month ahead, even during outbreaks and with these priors. We identify three major failure modes: (1) poor outbreak anticipation, (2) difficulty handling sparsity and noise, and (3) limited utility of common geographic adjacency for epidemiological spatial information. We release benchmark data, code, and instructions at this https URL to support development of operationally useful epidemic forecasting models.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)