Forecasts and metrics for baseline method from Improved forecasts of influenza-associated hospitalization rates with Google Search Trends

Reliable forecasts of influenza-associated hospitalizations during seasonal outbreaks can help health systems better prepare for patient surges. Within the USA, public health surveillance systems collect and distribute near real-time weekly hospitalization rates, a key observational metric that makes real-time forecast of this outcome possible. In this paper, we describe a method to forecast hospitalization rates using a population level transmission model in combination with a data assimilation technique. Using this method, we generated retrospective forecasts of hospitalization rates for 5 age groups and the overall population during 5 seasons in the USA and quantified forecast accuracy for both near-term and seasonal targets. Additionally, we describe methods to correct for under-reporting of hospitalization rates (backcast) and to estimate hospitalization rates from publicly available online search trends data (nowcast). Forecasts based on surveillance rates alone were reasonably accurate in predicting peak hospitalization rates (within ± 25% of the actual peak rate, three weeks before peak). The error in predicting rates one to four weeks ahead, remained constant for the duration of the seasons, even during periods of increased influenza incidence. An improvement in forecast quality across all age groups, seasons and targets was observed when backcasts and nowcasts supplemented surveillance data. These results suggest that the model-inference framework can provide reasonably accurate real-time forecasts of influenza hospitalizations; backcasts and nowcasts offer a way to improve system tolerance to observational errors.

季节性暴发期间流感相关住院事件的可靠预测，可助力医疗系统更好地应对患者激增状况。在美国境内，公共卫生监测系统会收集并发布近实时周住院率——这是实现该结局实时预测的关键观测指标。本文提出一种结合人群水平传播模型（population level transmission model）与数据同化技术（data assimilation technique）的住院率预测方法。借助该方法，我们针对美国5个流感季的5个年龄组及整体人群的住院率生成了回顾性预测，并量化了近期与季候性预测目标的准确率。此外，本文还阐述了两类方法：一是校正住院率漏报的回溯预测（backcast）方法，二是基于公开在线搜索趋势数据估算住院率的即时预测（nowcast）方法。仅基于监测数据的预测在预测峰值住院率方面表现较为准确：在峰值出现前三周，预测值与实际峰值的误差控制在±25%以内。提前1至4周的住院率预测误差在整个流感季期间均保持稳定，即便在流感发病率升高的阶段亦是如此。当回溯预测与即时预测补充监测数据后，所有年龄组、流感季及预测目标的预测质量均得到显著提升。上述结果表明，模型推断框架（model-inference framework）可实现流感相关住院事件的可靠实时预测；回溯预测与即时预测为提升系统对观测误差的耐受能力提供了可行路径。