Chen Huayong, Xiao Mimi
School of Public Health, Research Center for Medical and Social Development, Chongqing Medical University, 1 Yixueyuan Road, Yuzhong District, 400016, Chongqing, P. R. China.
BMC Infect Dis. 2024 Apr 23;24(1):432. doi: 10.1186/s12879-024-09301-4.
Influenza-like illness (ILI) imposes a significant burden on patients, employers and society. However, there is no analysis and prediction at the hospital level in Chongqing. We aimed to characterize the seasonality of ILI, examine age heterogeneity in visits, and predict ILI peaks and assess whether they affect hospital operations.
The multiplicative decomposition model was employed to decompose the trend and seasonality of ILI, and the Seasonal Auto-Regressive Integrated Moving Average with exogenous factors (SARIMAX) model was used for the trend and short-term prediction of ILI. We used Grid Search and Akaike information criterion (AIC) to calibrate and verify the optimal hyperparameters, and verified the residuals of the multiplicative decomposition and SARIMAX model, which are both white noise.
During the 12-year study period, ILI showed a continuous upward trend, peaking in winter (Dec. - Jan.) and a small spike in May-June in the 2-4-year-old high-risk group for severe disease. The mean length of stay (LOS) in ILI peaked around summer (about Aug.), and the LOS in the 0-1 and ≥ 65 years old severely high-risk group was more irregular than the others. We found some anomalies in the predictive analysis of the test set, which were basically consistent with the dynamic zero-COVID policy at the time.
The ILI patient visits showed a clear cyclical and seasonal pattern. ILI prevention and control activities can be conducted seasonally on an annual basis, and age heterogeneity should be considered in the health resource planning. Targeted immunization policies are essential to mitigate potential pandemic threats. The SARIMAX model has good short-term forecasting ability and accuracy. It can help explore the epidemiological characteristics of ILI and provide an early warning and decision-making basis for the allocation of medical resources related to ILI visits.
流感样疾病（ILI）给患者、雇主和社会带来了沉重负担。然而，重庆市在医院层面尚无相关分析和预测。我们旨在描述ILI的季节性特征，研究就诊的年龄异质性，预测ILI高峰并评估其是否影响医院运营。
采用乘法分解模型分解ILI的趋势和季节性，使用带外生因素的季节性自回归积分移动平均（SARIMAX）模型对ILI进行趋势和短期预测。我们使用网格搜索和赤池信息准则（AIC）来校准和验证最优超参数，并验证乘法分解和SARIMAX模型的残差均为白噪声。
在12年的研究期内，ILI呈持续上升趋势，在冬季（12月至1月）达到高峰，在2 - 4岁重症高危组中5 - 6月出现一个小高峰。ILI的平均住院时长（LOS）在夏季（约8月）左右达到峰值，0 - 1岁和≥65岁的极高危组的LOS比其他组更不规则。我们在测试集的预测分析中发现了一些异常情况，这与当时的动态清零政策基本一致。
ILI患者就诊呈现出明显的周期性和季节性模式。ILI防控活动可每年按季节开展，在卫生资源规划中应考虑年龄异质性。有针对性的免疫政策对于减轻潜在的大流行威胁至关重要。SARIMAX模型具有良好的短期预测能力和准确性。它有助于探索ILI的流行病学特征，为ILI就诊相关医疗资源的分配提供预警和决策依据。
