RMSLE for the forecast periods of 7 and 14 days.

COVID-19 forecasting models have been critical in guiding decision-making on surveillance testing, social distancing, and vaccination requirements. Beyond influencing public health policies, an accurate COVID-19 forecasting model can impact community spread by enabling employers and university leaders to adapt worksite policies and practices to contain or mitigate outbreaks. While many such models have been developed for COVID-19 forecasting at the national, state, county, or city level, only a few models have been developed for workplaces and universities. Furthermore, COVID-19 forecasting models have rarely been validated against real COVID-19 case data. Here we present the systematic parameter fitting and validation of an agent-based compartment model for the forecasting of daily COVID-19 cases in single-site workplaces and universities with real-world data. Our approaches include manual fitting, where initial model parameters are chosen based on historical data, and automated fitting, where parameters are chosen based on candidate case trajectory simulations that result in best fit to prevalence estimation data. We use a 14-day fitting window and validate our approaches on 7- and 14-day testing windows with real COVID-19 case data from one employer. Our manual and automated fitting approaches accurately predicted COVID-19 case trends and outperformed the baseline model (no parameter fitting) across multiple scenarios, including a rising case trajectory (RMSLE values: 2.627 for baseline, 0.562 for manual fitting, 0.399 for automated fitting) and a decreasing case trajectory (RMSLE values: 1.155 for baseline, 0.537 for manual fitting, 0.778 for automated fitting). Our COVID-19 case forecasting model allows decision-makers at workplaces and universities to proactively respond to case trend forecasts, mitigate outbreaks, and promote safety.新型冠状病毒肺炎（COVID-19）预测模型在指导监测检测、社交距离管控及疫苗接种政策制定的决策过程中发挥了关键作用。除对公共卫生政策制定产生影响外，精准的COVID-19预测模型还可协助雇主与高校管理者调整工作场所的政策与操作流程，以遏制或缓解疫情暴发，进而对社区传播态势产生影响。尽管目前已针对国家、州、县或城市层面的COVID-19预测开发了诸多此类模型，但针对工作场所与高校的相关模型却寥寥无几。此外，现有COVID-19预测模型极少依托真实COVID-19病例数据开展验证工作。本研究针对单场地工作场所与高校的每日COVID-19病例预测任务，提出了基于真实世界数据的基于智能体的隔室模型（agent-based compartment model）的系统参数拟合与验证方法。我们的方法包含手动拟合与自动拟合两类：手动拟合阶段将基于历史数据选取模型初始参数；自动拟合阶段则通过候选病例轨迹模拟选取最优参数，以实现与流行率估算数据的最佳匹配。我们采用14天的拟合窗口，并依托某雇主提供的真实COVID-19病例数据，在7天与14天的测试窗口上对所提方法进行验证。我们的手动与自动拟合方法能够精准预测COVID-19病例趋势，且在多种场景下均优于未进行参数拟合的基准模型：在病例数上升场景下，基准模型的均方根对数误差（Root Mean Squared Logarithmic Error，RMSLE）为2.627，手动拟合为0.562，自动拟合为0.399；在病例数下降场景下，基准模型的RMSLE为1.155，手动拟合为0.537，自动拟合为0.778。本COVID-19病例预测模型可帮助工作场所与高校的决策者主动应对病例趋势预测结果、缓解疫情暴发并提升场所安全水平。