论文标题
COVID-19使用Internet搜索数据的住院预测
COVID-19 Hospitalizations Forecasts Using Internet Search Data
论文作者
论文摘要
随着COVID-19在全球范围内传播以及Covid-19的新变体继续发生，可靠的Covid-19实时预测对于公共卫生对医疗资源分配的决策至关重要，例如ICU床，通风机，人员和人员，可以为Covid-19 Pandemics的激增做准备。受公众搜索行为与住院接纳之间的密切关联的启发，我们扩展了以前提供的流感跟踪模型Argo（带有Google搜索数据的自动估计），以预测未来的2周国家和州级Covid-19-19-19新的医院入院。利用COVID-19相关时间序列信息和Google搜索数据，我们的方法能够稳健地捕获新的Covid-19变体的浪费，并在国家和州一级自我纠正。基于我们在12个月比较期内的回顾性样本外评估，我们的方法平均比从COVID-19预测中心收集的最佳替代模型达到了15 \％的误差降低。总体而言，我们表明我们的方法是灵活的，自我的，健壮的，准确的，准确和可解释的，这使其成为帮助医疗保健官员和决策的潜在强大工具，以解决当前和未来的传染病爆发。
As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decision on medical resources allocations such as ICU beds, ventilators, and personnel to prepare for the surge of COVID-19 pandemics. Inspired by the strong association between public search behavior and hospitalization admission, we extended previously-proposed influenza tracking model, ARGO (AutoRegression with GOogle search data), to predict future 2-week national and state-level COVID-19 new hospital admissions. Leveraging the COVID-19 related time series information and Google search data, our method is able to robustly capture new COVID-19 variants' surges, and self-correct at both national and state level. Based on our retrospective out-of-sample evaluation over 12-month comparison period, our method achieves on average 15\% error reduction over the best alternative models collected from COVID-19 forecast hub. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist health-care officials and decision making for the current and future infectious disease outbreak.
