Current work on forecasting emergency department (ED) admissions focuses on disease aggregates or singular disease types. However, given differences in the dynamics of individual diseases, it is unlikely that any single forecasting model would accurately account for each disease and for all time, leading to significant forecast model uncertainty. Yet, forecasting models for ED admissions to-date do not explore the utility of forecast combinations to improve forecast accuracy and stability. It is also unknown whether improvements in forecast accuracy can be yield from (1) incorporating a large number of environmental and anthropogenic covariates or (2) forecasting total ED causes by aggregating cause-specific ED forecasts. To address this gap, we propose high-dimensional forecast combination schemes to combine a large number of forecasting individual models for forecasting cause-specific ED admissions over multiple causes and forecast horizons. We use time series data of ED admissions with an extensive set of explanatory lagged variables at the national level, including meteorological/ambient air pollutant variables and ED admissions of all 16 causes studied. We show that the simple forecast combinations yield forecast accuracies of around 3.81%-23.54% across causes. Furthermore, forecast combinations outperform individual forecasting models, in more than 50% of scenarios (across all ED admission categories and horizons) in a statistically significant manner. Inclusion of high-dimensional covariates and aggregating cause-specific forecasts to provide all-cause ED forecasts provided modest improvements in forecast accuracy. Forecasting cause-specific ED admissions can provide fine-scale forward guidance on resource optimization and pandemic preparedness and forecast combinations can be used to hedge against model uncertainty when forecasting across a wide range of admission categories.

翻译：当前关于急诊科(ED)入院人数预测的研究主要集中于疾病总量或单一疾病类型。然而，考虑到不同疾病动态特征的差异，任何单一预测模型都难以长期准确预测各类疾病，这导致了显著的预测模型不确定性。但迄今为止的急诊科入院预测模型尚未探索利用预测组合来提升预测精度与稳定性。同时，以下两种方法能否提高预测精度尚不明确：(1)纳入大量环境与人为协变量；(2)通过聚合病因特异性预测来获得急诊科总病例预测。为填补这一空白，我们提出了高维预测组合方案，通过整合大量个体预测模型来实现多病因、多预测时域的病因特异性急诊科入院人数预测。我们采用国家级急诊科入院时间序列数据，包含气象/环境空气污染物变量及全部16种研究病因的急诊科入院记录等大量解释性滞后变量。研究表明，简单预测组合在各病因上的预测精度可达约3.81%-23.54%。此外，在超过50%的场景中（涵盖所有急诊科入院类别和预测时域），预测组合以统计学显著的方式优于个体预测模型。纳入高维协变量以及聚合病因特异性预测来提供全病因急诊科预测，仅能有限提升预测精度。病因特异性急诊科入院预测可为资源优化和疫情防控提供精细化的前瞻指引，而预测组合可在广泛入院类别预测中有效对冲模型不确定性。