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.
Environ Int. 2018 Aug;117:284-291. doi: 10.1016/j.envint.2018.05.016. Epub 2018 May 16.
The discovery of the dynamics of seasonal and non-seasonal influenza outbreaks remains a great challenge. Previous internet-based surveillance studies built purely on internet or climate data do have potential error.
We collected influenza notifications, temperature and Google Trends (GT) data between January 1st, 2011 and December 31st, 2016. We performed time-series cross correlation analysis and temporal risk analysis to discover the characteristics of influenza epidemics in the period. Then, the seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to track influenza epidemics using GT and climate data.
Influenza infection was significantly corrected with GT at lag of 1-7 weeks in Brisbane and Gold Coast, and temperature at lag of 1-10 weeks for the two study settings. SARIMA models with GT and temperature data had better predictive performance. We identified autoregression (AR) for influenza was the most important determinant for influenza occurrence in both Brisbane and Gold Coast.
Our results suggested internet search metrics in conjunction with temperature can be used to predict influenza outbreaks, which can be considered as a pre-requisite for constructing early warning systems using search and temperature data.
季节性和非季节性流感爆发的动态发现仍然是一个巨大的挑战。以前纯粹基于互联网或气候数据的基于互联网的监测研究确实存在潜在的错误。
我们收集了 2011 年 1 月 1 日至 2016 年 12 月 31 日之间的流感通知、温度和 Google 趋势（GT）数据。我们进行了时间序列交叉相关分析和时间风险分析，以发现该时期流感流行的特征。然后，使用 GT 和气候数据开发季节性自回归综合移动平均（SARIMA）模型和回归树模型来跟踪流感流行。
在布里斯班和黄金海岸，流感感染与 GT 呈 1-7 周的滞后相关，与温度呈 1-10 周的滞后相关；对于这两个研究环境，SARIMA 模型与 GT 和温度数据的预测性能更好。我们发现，自回归（AR）是布里斯班和黄金海岸流感发生的最重要决定因素。
我们的结果表明，互联网搜索指标与温度相结合可以用于预测流感爆发，这可以被认为是使用搜索和温度数据构建预警系统的前提条件。
