Jing Yang, Jie Zhou, Tingyan Luo, Yulan Xie, Yiru Wei, Huanzhuo Mai, Yuecong Yang, Ping Cui, Li Ye, Hao Liang, Jiegang Huang
{"title":"使用百度搜索指数预测中国肺结核发病率：ARIMAX模型方法。","authors":"Jing Yang, Jie Zhou, Tingyan Luo, Yulan Xie, Yiru Wei, Huanzhuo Mai, Yuecong Yang, Ping Cui, Li Ye, Hao Liang, Jiegang Huang","doi":"10.1265/ehpm.23-00141","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Existing researches have established a correlation between internet search data and the epidemics of numerous infectious diseases. This study aims to develop a prediction model to explore the relationship between the Pulmonary Tuberculosis (PTB) epidemic trend in China and the Baidu search index.</p><p><strong>Methods: </strong>Collect the number of new cases of PTB in China from January 2011 to August 2022. Use Spearman rank correlation and interaction analysis to identify Baidu keywords related to PTB and construct a PTB comprehensive search index. Evaluate the predictive performance of autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models for the number of PTB cases.</p><p><strong>Results: </strong>Incidence of PTB had shown a fluctuating downward trend. The Spearman rank correlation coefficient between the PTB comprehensive search index and its incidence was 0.834 (P < 0.001). The ARIMA model had an AIC value of 2804.41, and the MAPE value was 13.19%. The ARIMAX model incorporating the Baidu index demonstrated an AIC value of 2761.58 and a MAPE value of 5.33%.</p><p><strong>Conclusions: </strong>The ARIMAX model is superior to ARIMA in terms of fitting and predicting accuracy. Additionally, the use of Baidu Index has proven to be effective in predicting cases of PTB.</p>","PeriodicalId":11707,"journal":{"name":"Environmental Health and Preventive Medicine","volume":"28 ","pages":"68"},"PeriodicalIF":4.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636285/pdf/","citationCount":"0","resultStr":"{\"title\":\"Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach.\",\"authors\":\"Jing Yang, Jie Zhou, Tingyan Luo, Yulan Xie, Yiru Wei, Huanzhuo Mai, Yuecong Yang, Ping Cui, Li Ye, Hao Liang, Jiegang Huang\",\"doi\":\"10.1265/ehpm.23-00141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Existing researches have established a correlation between internet search data and the epidemics of numerous infectious diseases. This study aims to develop a prediction model to explore the relationship between the Pulmonary Tuberculosis (PTB) epidemic trend in China and the Baidu search index.</p><p><strong>Methods: </strong>Collect the number of new cases of PTB in China from January 2011 to August 2022. Use Spearman rank correlation and interaction analysis to identify Baidu keywords related to PTB and construct a PTB comprehensive search index. Evaluate the predictive performance of autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models for the number of PTB cases.</p><p><strong>Results: </strong>Incidence of PTB had shown a fluctuating downward trend. The Spearman rank correlation coefficient between the PTB comprehensive search index and its incidence was 0.834 (P < 0.001). The ARIMA model had an AIC value of 2804.41, and the MAPE value was 13.19%. The ARIMAX model incorporating the Baidu index demonstrated an AIC value of 2761.58 and a MAPE value of 5.33%.</p><p><strong>Conclusions: </strong>The ARIMAX model is superior to ARIMA in terms of fitting and predicting accuracy. Additionally, the use of Baidu Index has proven to be effective in predicting cases of PTB.</p>\",\"PeriodicalId\":11707,\"journal\":{\"name\":\"Environmental Health and Preventive Medicine\",\"volume\":\"28 \",\"pages\":\"68\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636285/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Health and Preventive Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1265/ehpm.23-00141\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Health and Preventive Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1265/ehpm.23-00141","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Predicting pulmonary tuberculosis incidence in China using Baidu search index: an ARIMAX model approach.
Background: Existing researches have established a correlation between internet search data and the epidemics of numerous infectious diseases. This study aims to develop a prediction model to explore the relationship between the Pulmonary Tuberculosis (PTB) epidemic trend in China and the Baidu search index.
Methods: Collect the number of new cases of PTB in China from January 2011 to August 2022. Use Spearman rank correlation and interaction analysis to identify Baidu keywords related to PTB and construct a PTB comprehensive search index. Evaluate the predictive performance of autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models for the number of PTB cases.
Results: Incidence of PTB had shown a fluctuating downward trend. The Spearman rank correlation coefficient between the PTB comprehensive search index and its incidence was 0.834 (P < 0.001). The ARIMA model had an AIC value of 2804.41, and the MAPE value was 13.19%. The ARIMAX model incorporating the Baidu index demonstrated an AIC value of 2761.58 and a MAPE value of 5.33%.
Conclusions: The ARIMAX model is superior to ARIMA in terms of fitting and predicting accuracy. Additionally, the use of Baidu Index has proven to be effective in predicting cases of PTB.
期刊介绍：
The official journal of the Japanese Society for Hygiene, Environmental Health and Preventive Medicine (EHPM) brings a comprehensive approach to prevention and environmental health related to medical, biological, molecular biological, genetic, physical, psychosocial, chemical, and other environmental factors.
Environmental Health and Preventive Medicine features definitive studies on human health sciences and provides comprehensive and unique information to a worldwide readership.
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