School of Public Health and Social Work; Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia.
Science and Engineering Faculty, Mathematical and Statistical Science, Queensland University of Technology, Brisbane, Queensland, Australia.
Sci Rep. 2017 Sep 5;7(1):10437. doi: 10.1038/s41598-017-11195-z.
This study aims to assess the utility of internet search query analysis in pertussis surveillance. This study uses an empirical time series model based on internet search metrics to detect the pertussis incidence in Australia. Our research demonstrates a clear seasonal pattern of both pertussis infections and Google Trends (GT) with specific search terms in time series seasonal decomposition analysis. The cross-correlation function showed significant correlations between GT and pertussis incidences in Australia and each state at the lag of 0 and 1 months, with the variation of correlations between 0.17 and 0.76 (p < 0.05). A multivariate seasonal autoregressive integrated moving average (SARIMA) model was developed to track pertussis epidemics pattern using GT data. Reflected values for this model were generally consistent with the observed values. The inclusion of GT metrics improved detective performance of the model (β = 0.058, p < 0.001). The validation analysis indicated that the overall agreement was 81% (sensitivity: 77% and specificity: 83%). This study demonstrates the feasibility of using internet search metrics for the detection of pertussis epidemics in real-time, which can be considered as a pre-requisite for constructing early warning systems for pertussis surveillance using internet search metrics.
本研究旨在评估互联网搜索查询分析在百日咳监测中的效用。本研究使用基于互联网搜索指标的实证时间序列模型来检测澳大利亚的百日咳发病率。我们的研究表明，在时间序列季节性分解分析中，百日咳感染和谷歌趋势（GT）与特定搜索词都呈现出明显的季节性模式。互相关函数显示，GT 与澳大利亚和每个州的百日咳发病率在滞后 0 和 1 个月时存在显著相关性，相关性的变化范围在 0.17 到 0.76 之间（p<0.05）。建立了一个多变量季节性自回归综合移动平均（SARIMA）模型，使用 GT 数据来跟踪百日咳流行模式。该模型的反射值与观测值基本一致。纳入 GT 指标后，该模型的检测性能得到了提高（β=0.058，p<0.001）。验证分析表明，整体一致性为 81%（灵敏度：77%，特异性：83%）。本研究表明，使用互联网搜索指标实时检测百日咳流行是可行的，这可以被认为是使用互联网搜索指标构建百日咳监测预警系统的前提。
