Search trends and prediction of human brucellosis using Baidu index data from 2011 to 2018 in China

Songyu Wu,Kai An,Wenchao Wu,Hetian Xue,Qing Zhen,Chenhao Zhao,Yuhan Yang

doi:10.1038/s41598-020-62517-7

Abstract

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.

Highlights

Human brucellosis is one of the most common zoonotic diseases, among many infectious diseases, including zoonoses[1]
The information generated by the Baidu search data could be applied to epidemiological research
Based on previous studies of using search index data for the prediction of disease incidence, we believe that the Baidu index could provide a new approach to monitor the incidence of brucellosis

Summary

Introduction

Human brucellosis is one of the most common zoonotic diseases, among many infectious diseases, including zoonoses[1]. A well-known example of the use of Internet data in health is the monitoring of influenza outbreaks, where it is as accurate as traditional methods[11]. In the United States, researchers have successfully predicted flu outbreaks by analyzing the Google Trends and accurately estimated flu levels 1–2 weeks earlier than the published CDC report[16]. In Zhejiang Province of China, the prevalence of Norwalk virus has been determined through Internet monitoring (Baidu Index), and an appropriate model has been established to predict potential Norwalk virus infection[19]. Based on previous studies of using search index data for the prediction of disease incidence, we believe that the Baidu index could provide a new approach to monitor the incidence of brucellosis

Methods

Results

Conclusion