Using networks to combine "big data" and traditional surveillance to improve influenza predictions.
Using networks to combine "big data" and traditional surveillance to improve influenza predictions.
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DOI：
10.1038/srep08154
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发表时间：
2015-01-29
影响因子：
4.6
通讯作者：
Radin JM
中科院分区：
文献类型：
--
作者：
Davidson MW;Haim DA;Radin JM
季节性流感每年感染大约5-20%的美国人口，导致超过20万人住院治疗。更准确地评估感染水平和预测哪些地区在未来一段时间内感染风险更高的能力可以指导有针对性的预防和治疗工作，特别是在流行病期间。谷歌流感趋势（GFT）带来了巨大的希望，即“大数据”可以成为估计疾病负担和传播的有效工具。GFT产生的估计是实时的，比美国疾病控制和预防中心（CDC）收集的传统监测数据早两周。然而，GFT有一些臭名昭著的错误，并且在跟踪实验室确诊病例方面明显不如综合征型流感样疾病（ILI）病例准确。我们使用CDC数据构建了一个经验网络，并将其与GFT联合收割机相结合，以大幅提高其性能。这种改进的模型预测了未来一周的感染情况，GFT预测了现在，在最有可能促进流感传播的地区和流行病期间做得特别好。
Seasonal influenza infects approximately 5–20% of the U.S. population every year, resulting in over 200,000 hospitalizations. The ability to more accurately assess infection levels and predict which regions have higher infection risk in future time periods can instruct targeted prevention and treatment efforts, especially during epidemics. Google Flu Trends (GFT) has generated significant hope that “big data” can be an effective tool for estimating disease burden and spread. The estimates generated by GFT come in real-time – two weeks earlier than traditional surveillance data collected by the U.S. Centers for Disease Control and Prevention (CDC). However, GFT had some infamous errors and is significantly less accurate at tracking laboratory-confirmed cases than syndromic influenza-like illness (ILI) cases. We construct an empirical network using CDC data and combine this with GFT to substantially improve its performance. This improved model predicts infections one week into the future as well as GFT predicts the present and does particularly well in regions that are most likely to facilitate influenza spread and during epidemics.