The annual influenza outbreak leads to significant public health and economic burdens making it desirable to have prompt and accurate probabilistic forecasts of the disease spread. The United States Centers for Disease Control and Prevention (CDC) hosts annually a national flu forecasting competition which has led to the development of a variety of flu forecast modeling methods. Beginning in 2013, the target to be forecast was weekly percentage of patients with an influenza-like illness (ILI), but in 2021 the target was changed to weekly hospitalizations. Reliable hospitalization data has only been available since 2021, but ILI data has been available since 2010 and has been successfully forecast for several seasons. In this manuscript, we introduce a two component modeling framework for forecasting hospitalizations utilizing both hospitalization and ILI data. The first component is for modeling ILI data using a nonlinear Bayesian model. The second component is for modeling hospitalizations as a function of ILI. For hospitalization forecasts, ILI is first forecast then hospitalizations are forecast with ILI forecasts used as a predictor. In a simulation study, the hospitalization forecast model is assessed and two previously successful ILI forecast models are compared. Also assessed is the usefulness of including a systematic model discrepancy term in the ILI model. Forecasts of state and national hospitalizations for the 2023-24 flu season are made, and different modeling decisions are compared. We found that including a discrepancy component in the ILI model tends to improve forecasts during certain weeks of the year. We also found that other modeling decisions such as the exact nonlinear function to be used in the ILI model or the error distribution for hospitalization models may or may not be better than other decisions, depending on the season, location, or week of the forecast.

翻译：年度流感爆发带来显著的公共卫生和经济负担，因此需要及时、准确的疾病传播概率预测。美国疾病控制与预防中心（CDC）每年举办全国流感预测竞赛，推动了多种流感预测建模方法的发展。自2013年起，预测目标为流感样病例（ILI）周就诊百分比，但2021年该目标更改为周住院人数。可靠的住院数据仅从2021年开始可获得，而ILI数据自2010年起即可获得，并已成功预测多个流感季。本文提出一种利用住院数据和ILI数据的双组分建模框架用于住院人数预测。第一组分通过非线性贝叶斯模型对ILI数据进行建模。第二组分将住院人数建模为ILI的函数。对于住院预测，首先预测ILI数据，随后将ILI预测值作为预测因子进行住院人数预测。在模拟研究中，评估了住院预测模型，并比较了两种先前成功的ILI预测模型。同时评估了在ILI模型中引入系统性模型差异项的有效性。针对2023-24流感季的州级和国家级住院人数进行预测，并比较了不同建模决策。研究发现，在ILI模型中引入差异组分往往能改善年度特定周次的预测效果。同时发现，其他建模决策（如ILI模型中采用的具体非线性函数或住院模型的误差分布）是否优于其他决策，取决于预测的季节、地域或具体周次。