Department of Management Science and Information System, Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, China.
School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
J Med Internet Res. 2023 Oct 17;25:e45085. doi: 10.2196/45085.
Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve.
This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods.
We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend.
This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China.
Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.
流感疫情对全球公共卫生构成重大威胁。传统的监测系统和简单的算法往往难以准确、及时地预测流感疫情。大数据和现代技术为疾病监测和预测提供了新的手段。流感样疾病可以作为一种有价值的监测工具，用于监测流感和 COVID-19 等新兴呼吸道传染病，尤其是当报告病例数据可能无法完全反映实际疫情曲线时。
本研究旨在通过结合百度搜索查询数据和传统病毒学监测数据，建立流感疫情预测模型。目的是提高中国南北部流感疫情的早期检测和准备能力，为补充现代智能疫情监测方法提供证据。
我们收集了国家流感监测网络的病毒学数据和 2011 年 1 月至 2018 年 7 月的百度搜索查询数据，分别为 3691865 份和 1563361 份。确定了与流感相关的相关搜索词，并通过皮尔逊相关分析分析了它们与流感阳性率的相关性。使用分布式滞后非线性模型评估搜索词与流感活动的滞后相关性。随后，基于门控循环单元和多个注意力机制开发了一个预测模型，以预测流感阳性趋势。
本研究揭示了特定的百度搜索词与中国南北部流感阳性率之间存在高度相关性，除了 1 个术语。搜索词分为 4 组：流感的基本事实、流感症状、流感治疗和药物、流感预防，均与流感阳性率相关。流感预防和流感症状组的滞后相关性分别为 1.4-3.2 和 5.0-8.0 天。百度搜索词可以帮助预测中国南部地区的流感阳性率提前 14-22 天，但会干扰中国北部的流感监测。
将基于网络数据的信息补充到传统疾病监测系统中，可以帮助更早地发现流感疫情的预警信号。然而，用搜索引擎信息补充现代监测系统应该谨慎。这种方法为数字流行病学提供了有价值的见解，并有可能在呼吸道传染病监测中得到更广泛的应用。进一步的研究应该探索优化和定制不同地区和语言的搜索词，以提高流感预测模型的准确性。
