AUTHOR=Wang Yue , Zhou Haitao , Zheng Li , Li Min , Hu Bin 

TITLE=Using the Baidu index to predict trends in the incidence of tuberculosis in Jiangsu Province, China

JOURNAL=Frontiers in Public Health

VOLUME=11

YEAR=2023

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2023.1203628

DOI=10.3389/fpubh.2023.1203628

ISSN=2296-2565

ABSTRACT=<sec id="sec1"><title>Objective</title><p>To analyze the time series in the correlation between search terms related to tuberculosis (TB) and actual incidence data in China. To screen out the “leading” terms and construct a timely and efficient TB prediction model that can predict the next wave of TB epidemic trend in advance.</p></sec><sec id="sec2"><title>Methods</title><p>Monthly incidence data of tuberculosis in Jiangsu Province, China, were collected from January 2011 to December 2020. A scoping approach was used to identify TB search terms around common TB terms, prevention, symptoms and treatment. Search terms for Jiangsu Province, China, from January 2011 to December 2020 were collected from the Baidu index database.<xref rid="fn0001" ref-type="fn"><sup>1</sup></xref> Correlation coefficients between search terms and actual incidence were calculated using Python 3.6 software. The multiple linear regression model was constructed using SPSS 26.0 software, which also calculated the goodness of fit and prediction error of the model predictions.</p></sec><sec id="sec3"><title>Results</title><p>A total of 16 keywords with correlation coefficients greater than 0.6 were screened, of which 11 were the leading terms. The R<sup>2</sup> of the prediction model was 0.67 and the MAPE was 10.23%.</p></sec><sec id="sec4"><title>Conclusion</title><p>The TB prediction model based on Baidu Index data was able to predict the next wave of TB epidemic trends and intensity 2 months in advance. This forecasting model is currently only available for Jiangsu Province.</p></sec>