Real-time trends from surveillance data are important to assess and develop preparedness for influenza outbreaks. The overwhelming testing demand and limited capacity of testing laboratories for viral positivity render daily confirmed case data inaccurate and delay its availability in preparedness. Using Bayesian dynamic downscaling models, we obtained posterior estimates for daily influenza incidences from weekly estimates of the Centers for Disease Control and Prevention and daily reported constitutional and respiratory complaints during emergency department (ED) visits obtained from the state health departments. Our model provides one-day and seven-day lead forecasts along with 95$\%$ prediction intervals. Our hybrid Markov Chain Monte Carlo and Kalman filter algorithms facilitate faster computation and enable us to update our estimates as new data become available. Our method is tested and validated using the State of Michigan data over the years 2009-2013. Reported constitutional and respiratory complaints at the EDs showed strong correlations of 0.81 and 0.68 respectively, with influenza rates. In general, our forecast model can be adapted to track an outbreak with only one respiratory virus as a causative agent.

中文翻译：

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流感监测数据的动态降尺度和每日临近预报

监测数据的实时趋势对于评估和制定流感暴发的准备工作非常重要。巨大的检测需求和病毒阳性检测实验室的有限能力导致每日确诊病例数据不准确，并延迟了其准备工作的可用性。使用贝叶斯动态降尺度模型，我们从疾病控制和预防中心的每周估计中获得每日流感发病率的后验估计，并在从州卫生部门获得的急诊科 (ED) 就诊期间每日报告的体质和呼吸系统投诉。我们的模型提供 1 天和 7 天的领先预测以及 95$\%$预测区间。我们的混合马尔可夫链蒙特卡罗和卡尔曼滤波器算法有助于加快计算速度，并使我们能够在新数据可用时更新我们的估计。我们的方法使用密歇根州 2009-2013 年的数据进行了测试和验证。急诊科报告的体质和呼吸系统疾病与流感发病率的相关性分别为 0.81 和 0.68。一般来说，我们的预测模型可以适用于跟踪只有一种呼吸道病毒作为病原体的爆发。