The ability to estimate and predict pathogen variant dynamics can inform public health responses, including planning for increased transmission or severity, shifts in population immunity, or changes to vaccine or therapeutic effectiveness. The COVID-19 pandemic demonstrated the importance of monitoring SARS-CoV-2 variant evolution through viral genome sequencing, enabling predictive models to estimate variant frequencies in the recent past, present, and short-term future. Collaborative forecasting Hubs provided a valuable way to centralize predictive modeling of epidemiological indicators such as cases, hospitalizations, and deaths during the pandemic; however, none existed for variant dynamics. Here, we discuss the creation of the United States SARS-CoV-2 Variant Nowcast Hub, designed to solicit estimates of the relative abundance of a specified set of SARS-CoV-2 variants at the U.S. state level. We discuss the design decisions and challenges in building the Hub and its scoring procedures. Using submissions from the Hub's first respiratory virus season (nowcast dates October 9th, 2024 to June 4th, 2025), we evaluate five individual models and a baseline model. We found that the baseline model, which pools sequences across the U.S., performs well overall, with most individual models performing similarly or slightly worse. Locations with lower sequencing volumes exhibited greater variability in model performance. Models submitted for a single location outperformed those submitted for all locations, potentially due to greater timeliness and magnitude of local data. Much remains to be investigated regarding relative model performance across different phases of variant emergence, and we conclude by proposing future directions within and beyond this Hub.

翻译：病原体变异动态的估计与预测能力可为公共卫生响应提供信息支撑，包括规划传播增强或严重度变化、人群免疫变迁，以及疫苗或疗法有效性调整。COVID-19大流行揭示了通过病毒基因组测序监测SARS-CoV-2变异株演变的重要性，使预测模型能够估计近期、当前及短期未来的变异株频率。协作预测中心在大流行期间为病例数、住院人数、死亡人数等流行病学指标的预测建模提供了宝贵的集中化途径，但针对变异株动态的预测中心尚未建立。本文阐述了美国SARS-CoV-2变异株即时预测中心的构建过程，该中心旨在收集美国各州层面特定SARS-CoV-2变异株相对丰度的估计值。我们讨论了中心建设中的设计决策、挑战及其评分机制。基于中心首个呼吸道病毒流行季（即时预测日期为2024年10月9日至2025年6月4日）的提交结果，评估了五个个体模型与一个基线模型。研究发现，采用全美序列合并处理的基线模型整体表现良好，多数个体模型性能与之相当或略逊。测序量较低地区的模型性能表现出更大变异性。针对单一地点提交的模型优于面向所有地点的模型，这可能归因于本地数据的更高时效性与信息量。关于不同变异株涌现阶段中模型相对性能的比较仍需深入探究，我们最后提出了该中心内外的未来研究方向。