Mpox is an orthopoxvirus that infects humans and animals and is transmitted primarily through close physical contact. The episodic and spatially heterogeneous dynamics of Mpox transmission underscores the need for timely, area-specific forecasts to support targeted public health responses in the U.S. We develop a Vector Autoregression model with Lasso regularization (VAR-Lasso) to generate rolling two-week-ahead forecasts of weekly Mpox cases for eight high-incidence U.S. jurisdictions using national surveillance data from the Centers for Disease Control and Prevention (CDC). The VAR-Lasso model identifies significant long-lag, cross-jurisdictional predictors. For a case study in San Diego County (SDC), these statistical predictors align with phylogenetic analysis that traces a 2023 cluster in SDC to an outbreak in Illinois six months earlier. As the need for public health action is often greatest when incidence is increasing, our performance evaluation focuses on positive-slope weighted error metrics. Forecast performance of the VAR-Lasso model is compared to a uni-variate Auto-Regressive (AR) Lasso model and a naive moving-average estimate. The models are compared using slope-weighted Root Mean Squared Error (RMSE), slope-weighted Mean Absolute Error (MAE), and slope-weighted bias. Across all observations, the VAR-Lasso model reduces slope-weighted RMSE, MAE, and bias by 12%, 7%, and 66% relative to the AR model, and by 16%, 13%, and 76% relative to the naive benchmark. Our findings highlight the value of sparse multivariate time-series models that leverage cross-jurisdictional case data for early forecasting of Mpox outbreaks. Such forecasting can aid health departments in proactively providing timely resources and messaging to mitigate the risks of a future outbreak.

翻译：猴痘是一种感染人类和动物的正痘病毒，主要通过密切身体接触传播。猴痘传播的偶发性和空间异质性特征，凸显了在美国实施及时、针对特定区域的预测以支持精准公共卫生应对的必要性。本研究基于美国疾病控制与预防中心（CDC）的全国监测数据，开发了一种带Lasso正则化的向量自回归模型（VAR-Lasso），用于对美国八个高发病率辖区每周猴痘病例数进行滚动式两周前瞻预测。VAR-Lasso模型识别出具有显著长滞后期的跨辖区预测因子。以圣地亚哥县（SDC）的案例研究为例，这些统计预测因子与系统发育分析结果一致，该分析将2023年SDC的一个聚集性病例追溯到六个月前伊利诺伊州的疫情暴发。由于公共卫生行动的需求通常在发病率上升时最为迫切，我们的性能评估侧重于正斜率加权误差指标。将VAR-Lasso模型的预测性能与单变量自回归（AR）Lasso模型及朴素移动平均估计进行比较。模型比较采用斜率加权均方根误差（RMSE）、斜率加权平均绝对误差（MAE）和斜率加权偏差。在所有观测中，相较于AR模型，VAR-Lasso模型将斜率加权RMSE、MAE和偏差分别降低了12%、7%和66%；相较于朴素基准模型，分别降低了16%、13%和76%。我们的研究结果凸显了利用跨辖区病例数据的稀疏多元时间序列模型在猴痘疫情早期预测中的价值。此类预测可协助卫生部门主动提供及时资源和信息传递，以降低未来疫情暴发的风险。