Accurate and reliable forecasting models are critical for guiding public health responses and policy decisions during pandemics such as COVID-19. Retrospective evaluation of model performance is essential for improving epidemic forecasting capabilities. In this study, we used COVID-19 wastewater data from CDC's National Wastewater Surveillance System to generate sequential weekly retrospective forecasts for the United States from March 2022 through September 2024, both at the national level and for four major regions (Northeast, Midwest, South, and West). We produced 133 weekly forecasts using 11 models, including ARIMA, generalized additive models (GAM), simple linear regression (SLR), Prophet, and the n-sub-epidemic framework (top-ranked, weighted-ensemble, and unweighted-ensemble variants). Forecast performance was assessed using mean absolute error (MAE), mean squared error (MSE), weighted interval score (WIS), and 95% prediction interval coverage. The n-sub-epidemic unweighted ensembles outperformed all other models at 3-4-week horizons, particularly at the national level and in the Midwest and West. ARIMA and GAM performed best at 1-2-week horizons in most regions, whereas Prophet and SLR consistently underperformed across regions and horizons. These findings highlight the value of region-specific modeling strategies and demonstrate the utility of the n-sub-epidemic framework for real-time outbreak forecasting using wastewater surveillance data.

翻译：准确可靠的预测模型对于指导疫情期间（如COVID-19）的公共卫生应对措施和政策决策至关重要。对模型性能进行回顾性评估是提升疫情预测能力的关键。本研究利用美国疾病控制与预防中心国家废水监测系统的COVID-19废水数据，生成了2022年3月至2024年9月期间美国全国及四大区域（东北部、中西部、南部、西部）的连续周度回顾性预测。我们采用11种模型（包括ARIMA、广义加性模型、简单线性回归、Prophet以及n-子流行病框架的排名最优、加权集成与非加权集成变体）共生成了133次周度预测。使用平均绝对误差、均方误差、加权区间评分和95%预测区间覆盖率评估了预测性能。在3-4周预测期内，n-子流行病非加权集成在所有模型中表现最优，尤其在全国层面及中西部和西部地区。在大多数地区，ARIMA和广义加性模型在1-2周预测期内表现最佳，而Prophet和简单线性回归在各区域和预测期内均表现欠佳。这些发现凸显了区域特异性建模策略的价值，并展示了n-子流行病框架在利用废水监测数据进行实时疫情预测中的实用性。