Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data
Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data
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DOI：
10.1017/s0950268819001948
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发表时间：
2019-01-01
影响因子：
4.2
通讯作者：
Yasui, Y.
中科院分区：
文献类型：
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作者：
Martin, L. J.;Dong, H.;Yasui, Y.
预测与流感样疾病（ILI）相关的急诊科（ED）访问量的年度季节性高峰的大小，可以为开设流感护理诊所（ICCs）的决定提供信息，从而减轻急诊科的压力。利用来自加拿大艾伯塔省埃德蒙顿艾伯塔省实时综合征监测网的与流感样疾病相关的急诊科访问量数据，我们开发了（2004年8月1日至2008年7月31日的培训数据）并测试了（测试数据），（2008年8月1日至2014年2月19日）每日与肠病相关的访客的时空统计预测模型，以提前3天估计高访客量。我们的主模型基于随机截距的广义线性混合模型，结合了超过14天的预测残差，并捕获了峰值前观察到的体积增长。在季节性流感期间，我们的主要模型预测的交易量在67%-82%的高交易量日的+/- 30%范围内，在观测到的季节性高峰交易量的0.3%-21%范围内。在2009年H1N1流感大流行期间，模型预测并不成功。我们的模型可以在不同强度的季节性流感期间提供与ili相关的急诊科访问量增加的早期预警。这些预测可用于支持公共卫生决策，例如在季节性流感流行期间是否以及何时开放国际卫生中心。
Predicting the magnitude of the annual seasonal peak in influenza-like illness (ILI)-related emergency department (ED) visit volumes can inform the decision to open influenza care clinics (ICCs), which can mitigate pressure at the ED. Using ILI-related ED visit data from the Alberta Real Time Syndromic Surveillance Net for Edmonton, Alberta, Canada, we developed (training data, 1 August 2004-31 July 2008) and tested (testing data, 1 August 2008-19 February 2014) spatio-temporal statistical prediction models of daily ILI-related ED visits to estimate high visit volumes 3 days in advance. Our Main Model, based on a generalised linear mixed model with random intercept, incorporated prediction residuals over 14 days and captured increases in observed volume ahead of peaks. During seasonal influenza periods, our Main Model predicted volumes within +/- 30% of observed volumes for 67%-82% of high-volume days and within 0.3%-21% of observed seasonal peak volumes. Model predictions were not as successful during the 2009 H1N1 pandemic. Our model can provide early warning of increases in ILI-related ED visit volumes during seasonal influenza periods of differing intensities. These predictions may be used to support public health decisions, such as if and when to open ICCs, during seasonal influenza epidemics.