Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China.
Key Laboratory of Zoonosis Research, Jilin University Department of Epidemiology and Biostatistics, School of Public Health, Changchun, 130021, China.
Sci Rep. 2020 Apr 3;10(1):5896. doi: 10.1038/s41598-020-62517-7.
Reporting on brucellosis, a relatively rare infectious disease caused by Brucella, is often delayed or incomplete in traditional disease surveillance systems in China. Internet search engine data related to brucellosis can provide an economical and efficient complement to a conventional surveillance system because people tend to seek brucellosis-related health information from Baidu, the largest search engine in China. In this study, brucellosis incidence data reported by the CDC of China and Baidu index data were gathered to evaluate the relationship between them. We applied an autoregressive integrated moving average (ARIMA) model and an ARIMA model with Baidu search index data as the external variable (ARIMAX) to predict the incidence of brucellosis. The two models based on brucellosis incidence data were then compared, and the ARIMAX model performed better in all the measurements we applied. Our results illustrate that Baidu index data can enhance the traditional surveillance system to monitor and predict brucellosis epidemics in China.
布鲁氏菌病是一种由布鲁氏菌引起的相对罕见的传染病,在中国传统疾病监测系统中,其报告往往存在延迟或不完整的情况。与布鲁氏菌病相关的互联网搜索引擎数据可以为传统监测系统提供经济高效的补充,因为人们往往倾向于从中国最大的搜索引擎百度上搜索布鲁氏菌病相关的健康信息。在这项研究中,我们收集了中国疾病预防控制中心报告的布鲁氏菌病发病率数据和百度指数数据,以评估它们之间的关系。我们应用了自回归综合移动平均(ARIMA)模型和将百度搜索指数数据作为外部变量的 ARIMA 模型(ARIMAX)来预测布鲁氏菌病的发病率。然后比较了基于布鲁氏菌病发病率数据的两个模型,结果表明,在我们应用的所有测量指标中,ARIMAX 模型表现更好。我们的研究结果表明,百度指数数据可以增强传统监测系统,以监测和预测中国的布鲁氏菌病疫情。