Retrospective Parameter Estimation and Forecast of Respiratory Syncytial Virus in the United States
Retrospective Parameter Estimation and Forecast of Respiratory Syncytial Virus in the United States
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DOI：
10.1371/journal.pcbi.1005133
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发表时间：
2016-10-01
影响因子：
4.3
通讯作者：
Shaman, Jeffrey
中科院分区：
文献类型：
--
作者：
Reis, Julia;Shaman, Jeffrey
最近的研究表明，结合数学建模和贝叶斯推理方法的系统可用于生成对未来传染病发病率的实时预测。本研究开发了一套呼吸道合胞病毒（RSV）的预测预报系统。RSV是急性下呼吸道感染和细支气管炎的最常见原因。对RSV患者激增的流行时间和数量的提前警告有可能减少急诊科有据可查的治疗延误。我们使用一个易感染的恢复（SIR）模型结合集合调整卡尔曼滤波器（EAKF）和十年的区域美国样本数据由疾病控制和预防中心提供。数据和EAKF用于优化SIR模型，i）估计每次爆发过程中的关键流行病学参数，ii）生成回顾性预测。在所有季节和地点，基本生殖数R-0估计为3.0（标准差0.6）。RSV爆发的峰值量级在预测峰值之前四周以接近70%的准确度进行预测（即，接近70%的预测在实际峰值的25%内）。这项工作代表了实时RSV预测系统开发的第一步。
Recent studies have shown that systems combining mathematical modeling and Bayesian inference methods can be used to generate real-time forecasts of future infectious disease incidence. Here we develop such a system to study and forecast respiratory syncytial virus (RSV). RSV is the most common cause of acute lower respiratory infection and bronchiolitis. Advanced warning of the epidemic timing and volume of RSV patient surges has the potential to reduce well-documented delays of treatment in emergency departments. We use a susceptible-infectious-recovered (SIR) model in conjunction with an ensemble adjustment Kalman filter (EAKF) and ten years of regional U.S. specimen data provided by the Centers for Disease Control and Prevention. The data and EAKF are used to optimize the SIR model and i) estimate critical epidemiological parameters over the course of each outbreak and ii) generate retrospective forecasts. The basic reproductive number, R-0, is estimated at 3.0 (standard deviation 0.6) across all seasons and locations. The peak magnitude of RSV outbreaks is forecast with nearly 70% accuracy (i.e. nearly 70% of forecasts within 25% of the actual peak), four weeks before the predicted peak. This work represents a first step in the development of a real-time RSV prediction system.