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：对病原体变异动态进行估计和预测的能力可为公共卫生响应提供信息，包括应对传播增强或致病性加重、人群免疫水平变化，以及疫苗或疗法有效性改变等规划。COVID-19大流行表明，通过病毒基因组测序监测SARS-CoV-2变异株进化具有重要意义，这使得预测模型能够估计近期、当前和短期未来的变异株频率。协作式预测中心在大流行期间为病例数、住院人数和死亡人数等流行病学指标的预测建模提供了有效的集中化途径；然而，此前并未建立针对变异株动态的预测中心。本文阐述了美国SARS-CoV-2变异株即时预测中心的创建过程，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与挑战及其评分流程。利用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）提交的预测数据，我们对五个独立模型和一个基线模型进行了评估。研究发现，整合全美序列数据的基线模型整体表现良好，而多数独立模型表现与之相近或略逊。测序量较低地区的模型性能变异性更大。针对单一地区提交的模型优于针对所有地区提交的模型，这可能源于本地数据具有更高的及时性和数据量。关于不同变异株涌现阶段下模型相对性能的比较仍有待深入探究，我们最后提出了该中心内外的未来研究方向。