Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: \u2013 evidence from Baidu index

Bizhi Tu,Yaya Jia,Laifu Wei,Jun Qian

doi:10.1186/s12879-020-05740-x

Abstract

BackgroundNew coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19.MethodsWe collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman’s correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed.ResultsDaily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10− 6; cough: rs=0.592, p=4.485× 10− 4; fatigue: rs=0.629, p=1.494× 10− 4; sputum production: rs=0.648, p=8.206× 10− 5; shortness of breath: rs=0.656, p=6.182× 10–5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value’s optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever.ConclusionThe searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public’s attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

Highlights

New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic
Among the 34 provinces/regions in China, we found significant correlations between daily growth of confirmed cases (DGCC) and Daily Baidu Index values (DBIV); we observed that the number of daily confirmed cases tended to increase when Baidu searches for terms related to fever, cough, fatigue, and shortness of breath were increasing (Table 1)
We found that the optimal time lag of DBIV of fever, cough, fatigue, sputum production, and shortness of breath was 0, 4, 2, 3, 1 day/days, Fig. 3 Days earlier of serarch-to-comfrimed interval (STCI) among the top ten provinces in the cumulative number of confirmed cases. e.g., The red line represents the absolute value of the negative value respectively

Summary

Introduction

New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. The astonishing spread speed of the epidemic, to some extent, was failing to monitor and manage the potentially infected persons, which may pose a substantial infection control challenge [4]. Recognizing the potential quantity of infected persons timely and taking corresponding management measures to control the further spread of COVID19 is in urgent need. According to the 45th China statistical report on internet development, there were over 904 million Internet users in China, with the penetration rate of search engine use reached 83% [5]. Among Internet users, 80% of them tended to use electronic devices to acquire the information they are interested in [6]

Methods

Results

Discussion

Conclusion