基于进化信息模型的2017-2018年美国流感季节发病率预测

Xiangjun Du, Mercedes Pascual
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引用次数: 5

摘要

简介:季节性流感在美国和世界范围内造成了很高的疾病负担。提前预测疫情规模可为卫生保健和疫苗接种规划提供信息,有助于及时控制季节性流感。方法:最近,开发了一种基于过程的模型,用于在流感季节开始之前预测发病率动态,并通过几种统计标准验证了该方法,包括在过去的2016-2017流感季节开始之前对其进行准确的实时预测。结果:基于该模型和截至2017年6月的数据,对即将到来的2017-2018年流感季节进行了预测,表明以H3N2亚型为主的高于平均水平的中度严重疫情。讨论:这一预测与目前的监测数据是一致的,这些数据已经表明H3N2的优势。对即将到来的2017-2018年流感季节的预测强调了正在进行的疫苗接种运动的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Incidence Prediction for the 2017-2018 Influenza Season in the United States with an Evolution-informed Model.

Incidence Prediction for the 2017-2018 Influenza Season in the United States with an Evolution-informed Model.
Introduction: Seasonal influenza is responsible for a high disease burden in the United States and worldwide. Predicting outbreak size in advance can contribute to the timely control of seasonal influenza by informing health care and vaccination planning. Methods: Recently, a process-based model was developed for forecasting incidence dynamics ahead of the season, with the approach validated by several statistical criteria, including an accurate real-time prediction for the past 2016-2017 influenza season before it started. Results: Based on this model and data up to June 2017, a forecast for the upcoming 2017-2018 influenza season is presented here, indicating an above-average, moderately severe, outbreak dominated by the H3N2 subtype. Discussion: The prediction is consistent with surveillance data so far, which already indicate the predominance of H3N2. The forecast for the upcoming 2017-2018 influenza season reinforces the importance of the on-going vaccination campaign.
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