Quantifying the COVID-19-affected tourism education in China: a potential crisis in tourism recovery

Human resources is a crucial factor in supporting the development of tourism as a labour-intensive industry. This research enhances the understanding of China's tourism education associated with the spread of COVID-19 and its implications for tourism recovery. Initial findings imply that: COVID-19 had a profound lagging negative effect on the intention to apply for tourism-related majors of examinees, which is severe challenging for tourism recovery, and the impact was more pronounced in typical tourism-dependent cities than in non-tourism-dependent cities. The MICE Economics and Management was least affected, while the Sports Tourism was most affected. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Using the internet search data to investigate symptom characteristics of COVID-19: A big data study.

OBJECTIVE: Analyzing the symptom characteristics of Coronavirus Disease 2019(COVID-19) to improve control and prevention. METHODS: Using the Baidu Index Platform (http://index.baidu.com) and the website of Chinese Center for Disease Control and Prevention as data resources to obtain the search volume (SV) of keywords for symptoms associated with COVID-19 from January 1 to February 20 in each year from 2017 to 2020 and the epidemic data in Hubei province and the other top 9 impacted provinces in China. Data of 2020 were compared with those of the previous three years. Data of Hubei province were compared with those of the other 9 provinces. The differences and characteristics of the SV of COVID-19-related symptoms, and the correlations between the SV of COVID-19 and the number of newly confirmed/suspected cases were analyzed. The lag effects were discussed. RESULTS: Comparing the SV from January 1, 2020 to February 20, 2020 with those for the same period of the previous three years, Hubei's SV for cough, fever, diarrhea, chest tightness, dyspnea, and other symptoms were significantly increased. The total SV of lower respiratory symptoms was significantly higher than that of upper respiratory symptoms (P＜0.001). The SV of COVID-19 in Hubei province was significantly correlated with the number of newly confirmed/suspected cases (r confirmed = 0.723, r suspected = 0.863, both p < 0.001). The results of the distributed lag model suggested that the patients who searched relevant symptoms on the Internet may begin to see doctors in 2-3 days later and be confirmed in 3-4 days later. CONCLUSION: The total SV of lower respiratory symptoms was higher than that of upper respiratory symptoms, and the SV of diarrhea also increased significantly. It warned us to pay attention to not only the symptoms of the lower respiratory tract but also the gastrointestinal symptoms, especially diarrhea in patients with COVID-19. Internet search behavior had a positive correlation with the number of newly confirmed/suspected cases, suggesting that big data has an important role in the early warning of infectious diseases.

Did the COVID-19 pandemic impact urticaria information-seeking behavior in China? A retrospective longitudinal study.

Purpose: To investigate information-seeking behavior related to urticaria before and during the COVID-19 pandemic in China. Methods: Search query data for terms related to urticaria were retrieved using Baidu Index database from October 23, 2017 to April 23, 2022, and daily COVID-19 vaccination doses data were obtained from the website of the Chinese Center for Disease Control and Prevention. Among the 23 eligible urticaria search terms, four urticaria themes were generated as classification, symptom, etiology, and treatment of urticarial, respectively. Baidu Search Index (BSI) value for each term were extracted to analyze and compare the spatial and temporal distribution of online search behavior for urticaria before and after the COVID-19 pandemic, and to also explore the correlation between search query and daily COVID-19 vaccination doses. Results: The classification of urticaria accounted for nearly half of the urticaria queries on the internet. Regular seasonal patterns of BSI were observed in urticaria-related online search, by attaining its highest level in spring and summer and lowest level in winter. The BSIs of all urticaria themes significantly increased after the COVID-19 pandemic than that before the pandemic (all P<0.05). Xizang, Qinghai and Ningxia are the most active geographical areas for increased urticaria-searching activities after the COVID-19 pandemic. There was also a significant positive correlation between daily BSIs and daily COVID-19 vaccination doses in each urticaria theme. Cross-correlation analysis found that the search of symptom, etiology, and treatment attained their strongest correlation with daily COVID-19 vaccination doses at 11-27 days before the injection of vaccine, imply vaccination hesitation related to concerns of urticaria. Conclusions: This study used the internet as a proxy to provide evidence of public search interest and spatiotemporal characteristics of urticaria, and revealed that the search behavior of urticaria have increased significantly after the COVID-19 pandemic and COVID-19 vaccination. It is anticipated that the findings about such increase in search behavior, as well as the behavior of urticaria-related vaccine-hesitancy, will help guide public health education and policy regulation.

Big Data Statistics of Logistics Warehousing Supply and Demand in Key Cities of Central and Western China Based on Baidu Index and Warehouse in Cloud

Based on the Baidu Index, taking "warehousing"and "warehouse"as the keywords, the Baidu search index of "warehousing"and "warehouse"nationwide is statistically analyzed. It is found that the Baidu search index with "warehousing"and "warehouse"as the keywords has significantly increased before and after the COVID-19 epidemic, which shows that the basic role of logistics warehousing in the national economic and social development is increasingly obvious, and the corresponding demand for logistics warehousing is growing. Based on the big data of Warehouse in Cloud, incomplete statistics of "warehousing demand"of "demand location"in China's provinces are similar to the analysis of differences in the source places (regions and provinces) of different search groups through the "population portrait"of Baidu Index. The "warehousing demand"and "warehousing supply"of the key cities in central and Western China are counted. Focusing on the key cities in central and Western China, the correlation analysis of warehousing rent and demand area is carried out. It is found that, on the one hand, the regional logistics warehousing demand is 3 years (the lease term is less than 1 year or 1-3 years), with intra-period volatility. On the other hand, regional centers (National Central Cities) have absolute advantages in the attraction of regional logistics and warehousing. Furthermore, in recent years, due to the impact of the COVID-19 epidemic and extreme meteorological and geological disasters, the adverse impact on the regional economic and social development will show that the demand for logistics and warehousing will be interrupted, reduced and lagged, and the growth will be restored in subsequent years. The average rent of key cities in Western China is 22.52 yuan/m2·month, the average vacancy rate is 11.65%, and there are 1359 warehouses in the park. The average rent of key cities in the central region is 23.5 yuan/m2·month, the average vacancy rate is 13.86%, and there are 1070 warehouses in the park. From the perspective of rent, Changsha shows the highest rent, while Taiyuan shows the lowest rent. Furthermore, the vacancy rate of Chongqing and Xi'an are the highest and lowest, respectively. There is a correlation between the variable of warehousing rent in 2022 and the total retail sales of consumer goods in 2021 (Spearman correlation coefficient is significant). There is a correlation between the variable of average warehousing demand area in 2019-2021 and the sample of the third industry production value in 2021 and the sample variable of total import and export volume of goods in 2021 (Pearson correlation coefficient is significant). The variable of average warehousing demand area in 2019-2021 and the sample variable of resident population. There is a correlation between the total retail sales of social consumer goods in 2021 (Spearman correlation coefficient is significant). On the one hand, the statistical analysis of big data on the digital warehousing information platform can provide reference for the prediction of supply and demand of logistics warehousing and modern logistics service industry in the high-quality development of the region. On the other hand, the spatial econometric analysis of logistics industry and regional economic growth represented by logistics warehousing needs further research. CCS CONCEPTS •Human-centered computing ∼Collaborative and social computing ∼Collaborative and social computing theory, concepts and paradigms ∼Computer supported cooperative work © 2022 ACM.

Early warning signals for Omicron outbreaks in China: a retrospective study.

The Omicron variant has become the dominant COVID-19 variant worldwide due to its rapid and cryptic spread; therefore, successful early warning is of great importance to be able to control epidemics in their early phase, before developing into large outbreaks. COVID-19-related Baidu search index, which reflects human behavior to a certain degree, was used to retrospectively detect the warning signs for Omicron variant outbreaks in China in 2022. The characteristics and effects of warning signs were analyzed in detail. We detected the presence of early warning signs (both high and low thresholds) and found that these occurred 4-7 days earlier than traditional epidemiological surveillance and >20 days earlier than the implementation of the local "lockdown" policy. Compared with the "high threshold" warning, the early warning effect of the "low threshold" is also vital because it indicates a complacency about epidemic prevention and control. However, there is obvious heterogeneity in the optimal threshold for detecting early warning signs and their distribution in different cities. Multi-source and multi-point early warning systems should be established via combining internet-based big data in the future to conduct effective and early real-time warning. This would create precious time for the early control of COVID-19 outbreaks. This article is protected by copyright. All rights reserved.

Increasing public concern on insomnia during the COVID-19 outbreak in China: An info-demiology study.

Background: Since December 2019, an unexplained pneumonia has broken out in Wuhan, Hubei Province, China. In order to prevent the rapid spread of this disease, quarantine or lockdown measures were taken by the Chinese government. These measures turned out to be effective in containing the contagious disease. In spite of that, social distancing measures, together with disease itself, would potentially cause certain health risks among the affected population, such as sleep disorder. We herein conducted this web search analysis so as to examine the temporal and spatial changes of public search volume of the mental health topic of "insomnia" during COVID-19 pandemic in China. Methods: The data sources included Baidu Index (BDI) to analyze related search terms and the official website of the National Health Commission of the People's Republic of China to collect the daily number of newly confirmed COVID-19 cases. Following a descriptive analysis of the overall search situation, Spearman's correlation analysis was used to analyze the relationship between the daily insomnia-related search values and the daily newly confirmed cases. The means of search volume for insomnia-related terms during the COVID-19 outbreak period (January 23rd, 2020 to April 8th, 2020) were compared with those during 2016-2019 using Student's t test. Finally, by analyzing the overall daily mean of insomnia in various provinces, we further evaluated whether there existed regional differences in searching for insomnia during the COVID-19 outbreak period. Results: During the COVID-19 outbreak period, the number of insomnia-related searches increased significantly, especially the average daily the BDI for the term "1 min to fall asleep immediately". Spearman's correlation analysis showed that 6 out of the 10 insomnia-related keywords were significantly positively related to the daily newly confirmed cases. Compared with the same period in the past four years, a significantly increased search volume was found in 60.0% (6/10) insomnia-related terms during the COVID-19 outbreak period. We also found that Guangdong province had the highest number of searches for insomnia-related during the pandemic. Conclusions: The surge in the number of confirmed cases during the COVID-19 pandemic has led to an increase in concern and online searches on this topic of insomnia. Further studies are needed to determine whether the search behavior truly reflect the real-time prevalence profile of relevant mental disorders, and further to establish a risk prediction model to determine the prevalence risk of psychopathological disorders, including insomnia, using insomnia-related BDI and other well-established risk factors.

Using search trends to analyze web-based users' behavior profiles connected with COVID-19 in mainland China: infodemiology study based on hot words and Baidu Index.

Background: Mainland China, the world's most populous region, experienced a large-scale coronavirus disease 2019 (COVID-19) outbreak in 2020 and 2021, respectively. Existing infodemiology studies have primarily concentrated on the prospective surveillance of confirmed cases or symptoms which met the criterion for investigators; nevertheless, the actual impact regarding COVID-19 on the public and subsequent attitudes of different groups towards the COVID-19 epidemic were neglected. Methods: This study aimed to examine the public web-based search trends and behavior patterns related to COVID-19 outbreaks in mainland China by using hot words and Baidu Index (BI). The initial hot words (the high-frequency words on the Internet) and the epidemic data (2019/12/01-2021/11/30) were mined from infodemiology platforms. The final hot words table was established by two-rounds of hot words screening and double-level hot words classification. Temporal distribution and demographic portraits of COVID-19 were queried by search trends service supplied from BI to perform the correlation analysis. Further, we used the parameter estimation to quantitatively forecast the geographical distribution of COVID-19 in the future. Results: The final English-Chinese bilingual table was established including six domains and 32 subordinate hot words. According to the temporal distribution of domains and subordinate hot words in 2020 and 2021, the peaks of searching subordinate hot words and COVID-19 outbreak periods had significant temporal correlation and the subordinate hot words in COVID-19 Related and Territory domains were reliable for COVID-19 surveillance. Gender distribution results showed that Territory domain (the male proportion: 67.69%; standard deviation (SD): 5.88%) and Symptoms/Symptom and Public Health (the female proportion: 57.95%, 56.61%; SD: 0, 9.06%) domains were searched more by male and female groups respectively. The results of age distribution of hot words showed that people aged 20-50 (middle-aged people) had a higher online search intensity, and the group of 20-29, 30-39 years old focused more on Media and Symptoms/Symptom (proportion: 45.43%, 51.66%; SD: 15.37%, 16.59%) domains respectively. Finally, based on frequency rankings of searching hot words and confirmed cases in Mainland China, the epidemic situation of provinces and Chinese administrative divisions were divided into 5 levels of early-warning regions. Central, East and South China regions would be impacted again by the COVID-19 in the future.

Investor attention and the COVID-19 concept stocks in China's stock market

Purpose This paper investigates the impact of investor attention on the COVID-19 concept stocks in China's stock market from the perspectives of the macroeconomy, the stock market and the COVID-19 pandemic. Design/methodology/approach On the basis of controlling the time effects and individual fixed effects, this paper studies the impact of investor attention on the COVID-19 concept stocks in China's stock market through a set of fixed effect panel data models. Among them, investor attention focuses on macroeconomy, stock market and the COVID-19 pandemic, respectively, while stock indicators cover return, volatility and turnover. In addition, this paper also examines the heterogeneity influence of investor attention on the COVID-19 concept stocks from the perspective of time and stock classification. Findings Findings indicate that the attention to macroeconomy does not have a statistically significant effect on the return, unlike the attention to stock market and COVID-19 incident. Three types of investor attention have significant positive effects on the volatility and turnover rate. During the outbreak of the domestic epidemic, the impact of investor attention was significantly higher than that during the outbreak of the epidemic overseas. A finer-grained analysis shows that the attention to stock market has significantly increased the return of preventive type and treatment type stocks, while diagnostic-related stocks have been most affected by the attention to COVID-19 incident. Research limitations/implications The major limitation of this work is the construction of investor attention. Although Baidu index is widely used, investor attention can be assessed more accurately based on more unstructured data. In addition, the effect of the COVID-19 can also be investigated in a longer time domain. Further research can be combined with the dynamics of the COVID-19 pandemic to more comprehensively evaluate its impact on the stock market. Originality/value The research proves that investor attention plays an important role in stock pricing and provides empirical evidence on the behavioral foundations of the conceptual sector of the stock market under uncertainty. It also has practical implications for regulators and investors interested in conducting accurate asset allocation and risk assessment.

Online public attention toward allergic rhinitis in Wuhan, China: Infodemiology study using Baidu index and meteorological data.

Background: With the popularization of the Internet and medical knowledge, more and more people are learning about allergic rhinitis (AR) on the Internet. Objective: This study aims to analyze the epidemiological characteristics and online public attention to AR in Wuhan, China, utilizing the most popular search engine in mainland China and meteorological data of Wuhan. Methods: To study the Internet attention and epidemiological characteristics of AR in Wuhan, the search volume (SV) of "Allergic Rhinitis" in Mandarin and AR-related search terms from 1 January 2014 through 31 December 2021 were recorded. For user interest, the search and demand data were collected and analyzed. Results: The yearly average Baidu SV of AR in both Wuhan and China increased year by year but began to decline gradually after the COVID-19 pandemic. Baidu SV of AR in Wuhan exhibited significant seasonal variation, with the first peak was from March to May and the second peak occurring between September and October. Correlation analysis revealed a moderate positive correlation between the monthly average SV of "Allergic Rhinitis" and "Mites" and "Mites + Pollen Allergy" in Wuhan, a weak positive correlation between the monthly average SV of "Allergic Rhinitis" and "Pollen Allergy," and a positive correlation between monthly SV of "Allergic Rhinitis" and the meteorological index of pollen allergy (MIPA). Conclusion: The attention given to the topic on the internet, as measured by the search volume, was reflective of the situation in Wuhan, China. It has the potential to predict the epidemiological characteristics of AR and help medical professionals more effectively plan seasonal AR health education.

Utilizing Baidu Index to Track Online Interest in Influenza During the COVID-19 Pandemic in China.

Background Influenza is commonly called the flu which is a contagious respiratory illness caused by influenza viruses that infect the nose, throat, and sometimes the lungs, usually a self-limiting, febrile disease of global importance. It occurs every year and infects the respiratory tract and can lead to sporadic, local outbreaks of widespread epidemics. The global burden of influenza epidemics on incidence rate and mortality is considerable. It is noted that patients with early coronavirus disease-2019 (COVID-19) have symptoms such as headache, nasal congestion, sneezing, and cough, which are like those of influenza. And the outbreak of COVID-19 coincided with the winter and spring season in the northern hemisphere with a high incidence of influenza. And it leads to the public's attention to influenza. Method In order to better clarify the social concern of Chinese people about "influenza" during the COVID-19 pandemic, this study conducted a trends analysis using the Baidu index from January 1, 2018, to January 1, 2022, and compared the public's search index with "COVID-19" during this period. This study used ArcGIS version 10.4 (https://www.esri.com/) to conduct a Global Moran's I analysis of the public concern of "influenza" in 31 provinces (municipalities directly under the central government and autonomous regions) in China from 2018 to 2021, except for Hong Kong, Macao, and Taiwan and a Local Moran's I of the "influenza" concern in 2018 and 2021. Results We observed that before the outbreak of COVID-19, the search trend of the public for "influenza" was concentrated in the winter and spring of each year, showing seasonal characteristics. However, after the outbreak of COVID-19, the public's search trend for "influenza" increased sharply, and then it leveled off. This shows completely that there is a certain correlation between the COVID-19 outbreak and the online search for "influenza". Regarding the Global Moran's I, the spatial clustering of national "influenza" concerns was observed. During the COVID-19 pandemic, the spatial correlation between the magnitude of public concern and the spatial correlation became larger as the number of years increased and is greater than that before the outbreak of the COVID-19 pandemic. The results of Local Moran's I showed that the main types of local spatial autocorrelation in 2018 and 2021 were both positive high-high correlations, but the former was mainly concentrated in the eastern coastal region, while the latter began to spread to the central region. Conclusion The analysis of the Baidu Index shows that during the COVID-19 pandemic, the public's interest in "influenza" first increased and then decreased, and then remained at a trough, no longer showing the seasonal change characteristics before the outbreak of the COVID-19, indicating that there may be a correlation between COVID-19 and "influenza". The Moran's I indicate that the national "influenza" concern is spatially clustered, while the spatial correlation is increasing and greater than before the outbreak of the COVID-19 pandemic. This is most likely related to the daily update of information related to patients with COVID-19. Meanwhile, the "high-high" local clustering of "influenza" concerns in the central and eastern regions during the COVID-19 pandemic is related to the frequent human and logistic exchanges in the central and eastern regions, which contributed to the spread of the disease.

An online longitudinal study about search index reflexing public attention of vaccinate against COVID-19.

Background: The coronavirus disease of 2019 (COVID-19) has had catastrophic effects worldwide. Mounting efforts for vaccination against COVID-19 have achieved tremendous progress. Online searching is a voluntary behavior of people might reflect the public attention and awareness.. Screening and analyzing the details of vaccine related searches may help the government to grasp the trend of public opinion and provide a reference for vaccination strategies and future efforts to protect public health. Methods: Three terms related to COVID-19 and COVID-19 vaccine as well as daily relative search volumes (RSV) were retrieved in the Baidu Index (BDI) from 1 January 2020 to 1 July 2021 in China. Besides the national total data, those of the individual provinces/cities/region of Beijing, Shanghai, Guangdong, Heilongjiang, Sichuan, and Tibet were also included. Vaccine-related policies were also gathered during this period. The vaccination rates within China were derived from the National Health Commission of the People's Republic of China, from 23 March 2021 to 1 July 2021. The searching index was calculated by the searching volume and curve graphs were used to demonstrate the variation and the related trend of the RSV and vaccination rates. Results: A total of 548 days' BDI data were retrieved. The national and provincial curves of the BDI exhibited similar fluctuating upward trends, with 5 obvious rises, especially in COVID-19 vaccine searching volume. The vaccination number was correlated with the searching volume growth of COVID-19 vaccine and vaccine uptake (r=0.382, P<0.001; r=0.256, P=0.010). Relevant vaccination events corresponded to the variation searching trend and were attributed to or were influenced by the searching variation. Conclusions: Public awareness about vaccination against COVID-19 was related to the implementation of vaccine policies. Positive vaccine-related policy and high public awareness about vaccination could play a vital role in maximizing the vaccination uptake. Advanced internet data grabbing could consolidate public information in an efficient and timely manner. These findings would support efforts to utilize the big data monitoring of the public opinion to forecast and guide the public health policies. Dynamic monitoring as well as prevention and timely adjustment under this supervision could be expected.

The impact of COVID-19 on the attention to sustainable clothing consumption: Analysis of Baidu indexes on old clothes recycling, old clothes renovation and old clothes donation

The COVID-19 epidemic has exerted tremendous impact on the public lifestyle. Due to the shortage of living materials, people have consciously saved the necessities of life, indirectly cultivating the environmental protection awareness and sustainability awareness. However, the public attention to clothing sustainability has been inclined. To explore the impact degree, the Baidu indexes on old clothes recycling, old clothes renovation and old clothes donation were analyzed. The STL algorithm was used for the decomposition of the time series of the public attention to isolate the trend components that can reflect the change trend;the index weights of the specific behaviors of sustainable clothing consumption before and after the COVID-19 epidemic were calculated using entropy method. The results have shown that the COVID-19 epidemic has increased the public attention to sustainable clothing consumption;during the post-epidemic period, the public have paid more attention to the sustainable behavior of old clothes recycling, and the attention to old clothes renovation and donation has declined. Therefore, according to the trend of change, targeted suggestions were proposed on the implementation of sustainable clothing consumption behaviors during the post-epidemic period. © 2021 China Silk Association. All rights reserved.

Application of the Internet Platform in Monitoring Chinese Public Attention to the Outbreak of COVID-19.

Objectives: The internet data is an essential tool for reflecting public attention to hot issues. This study aimed to use the Baidu Index (BDI) and Sina Micro Index (SMI) to confirm correlation between COVID-19 case data and Chinese online data (public attention). This could verify the effect of online data on early warning of public health events, which will enable us to respond in a more timely and effective manner. Methods: Spearman correlation was used to check the consistency of BDI and SMI. Time lag cross-correlation analysis of BDI, SMI and six case-related indicators and multiple linear regression prediction were performed to explore the correlation between public concern and the actual epidemic. Results: The public's usage trend of the Baidu search engine and Sina Weibo was consistent during the COVID-19 outbreak. BDI, SMI and COVID-19 indicators had significant advance or lag effects, among which SMI and six indicators all had advance effects while BDI only had advance effects with new confirmed cases and new death cases. But compared with the SMI, the BDI was more closely related to the epidemic severity. Notably, the prediction model constructed by BDI and SMI can well fit new confirmed cases and new death cases. Conclusions: The confirmed associations between the public's attention to the outbreak of COVID and the trend of epidemic outbreaks implied valuable insights into effective mechanisms of crisis response. In response to public health emergencies, people can through the information recommendation functions of social media and search engines (such as Weibo hot search and Baidu homepage recommendation) to raise awareness of available disease prevention and treatment, health services, and policy change.

Impact of COVID-19 on latent emissiveness of residents in China: Based on the X-12-ARIMA additive seasonal adjustment model

With Census X-12 model and ARIMA model, this paper quantitatively analyzes the impact of the COVID-19 epidemic on the latent emissiveness of Chinese residents which happened at the end of 2019. The results show that: First, during this epidemic period, the overall latent emissiveness index of Chinese residents decreased by 53.51%, and showed certain spatial difference, and it is not the area with the largest number of confirmed cases, and the area with the most serious epidemic situation, the greater the loss of latent emissiveness. Second, the residents’ willingness to travel has a strong fit with the development stage of the epidemic, but there is also a certain lag. In other words, the more severe the epidemic, the lower the willingness of residents to travel;When the epidemic is under control, people’s willingness to travel will rise again, but the rise time will be delayed. Third, compared with the period of SARS in 2003, the impact of COVID-19 on China residents’ latent emissiveness is greater and more profound, three times as much as that of the SARS period. In view of the serious impact of the epidemic on Chinese residents’ willingness to travel, finally, several development suggestions are put forward on how to re-enhance people’s travel information and revitalize the tourism industry in the process of normalizing the epidemic prevention and control.

Forecast of the COVID-19 Epidemic Based on RF-BOA-LightGBM.

In this paper, we utilize the Internet big data tool, namely Baidu Index, to predict the development trend of the new coronavirus pneumonia epidemic to obtain further data. By selecting appropriate keywords, we can collect the data of COVID-19 cases in China between 1 January 2020 and 1 April 2020. After preprocessing the data set, the optimal sub-data set can be obtained by using random forest feature selection method. The optimization results of the seven hyperparameters of the LightGBM model by grid search, random search and Bayesian optimization algorithms are compared. The experimental results show that applying the data set obtained from the Baidu Index to the Bayesian-optimized LightGBM model can better predict the growth of the number of patients with new coronary pneumonias, and also help people to make accurate judgments to the development trend of the new coronary pneumonia.

Investor Sentiment and Stock Returns During the COVID-19 Pandemic.

In this paper, we regard the Baidu index as an indicator of investors' attention to China's epidemic stocks. We believe that when seeking information to guide investment decisions, investor sentiment is usually affected by the information provided by the Baidu search engine, which may cause stock prices to fluctuate. Therefore, we constructed a GARCH extended model including the Baidu index to predict the return of epidemic stocks and compared it with the benchmark model. The empirical research in this paper finds that the forecast model including the Baidu index is significantly better than the benchmark model. This has important reference value both for investors in predicting stock trends and for the government's formulation of policies to prevent excessive stock market volatility.

Detecting epidemiological relevance of adenoid hypertrophy, rhinosinusitis, and allergic rhinitis through an Internet search.

PURPOSE: This study aimed to detect the epidemiological relevance between adenoid hypertrophy (AH) and rhinosinusitis, and AH and allergic rhinitis (AR) through an Internet search. METHODS: Internet search query data from January 2011 to December 2019 in China were retrieved from the Baidu Index (BI). Spearman's correlation coefficients were used to detect the correlation among the search volumes of AH, rhinosinusitis, and AR. We also collected search data from the first 5 months of 2020, when quarantine was implemented in China due to the coronavirus disease 2019 epidemic. Then, we compared the search data to those obtained during the same period in 2019 to assess the effects of isolation on AH and AR. RESULTS: Statistically significant relevance was found between the search variations of AH and rhinosinusitis during 2011-2019 (R = 0.643, P < 0.05). However, the relationship between AH and AR was weak (R = - 0.239, P < 0.05) and that between rhinosinusitis and AR (R = - 0.022, P > 0.05) was not relevant. The average monthly search volume of AH and rhinosinusitis had a strong correlation (R = 0.846, P < 0.01), but AH and AR and rhinosinusitis and AR were not correlated (R = - 0.350, P > 0.05; R = - 0.042, P > 0.05, respectively). AH and rhinosinusitis search volumes decreased consistently during the first 5 months of 2020 (isolation), whereas that for AR increased during January-February. CONCLUSION: AH had an epidemiological relationship with rhinosinusitis, which was not consistent with AR. The decrease in public gathering effectively reduced the morbidities of AH and rhinosinusitis but not those of AR.

Baidu Index and COVID-19 Epidemic Forecast: Evidence From China.

With the global spread of the Coronavirus epidemic, search engine data can be a practical tool for decision-makers to understand the epidemic's trends. This article uses trend analysis data from the Baidu search engine, the most widely used in China, to analyze the public's attention to the epidemic and the demand for N95 masks and other anti-epidemic materials and information. This kind of analysis has become an important part of information epidemiology. We have analyzed the use of the keywords "Coronavirus epidemic," "N95 mask," and "Wuhan epidemic" to judge whether the introduction of real-time search data has improved the efficiency of the Coronavirus epidemic prediction model. In general, the introduction of the Baidu index, whether in-sample or out-of-sample, significantly improves the prediction efficiency of the model.

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

BACKGROUND: 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. METHODS: We 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. RESULTS: Daily 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. CONCLUSION: The 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.

Online Public Attention During the Early Days of the COVID-19 Pandemic: Infoveillance Study Based on Baidu Index.

BACKGROUND: The COVID-19 pandemic has become a global public health event, attracting worldwide attention. As a tool to monitor public awareness, internet search engines have been widely used in public health emergencies. OBJECTIVE: This study aims to use online search data (Baidu Index) to monitor the public's attention and verify internet search engines' function in public attention monitoring of public health emergencies. METHODS: We collected the Baidu Index and the case monitoring data from January 20, 2020, to April 20, 2020. We combined the Baidu Index of keywords related to COVID-19 to describe the public attention's temporal trend and spatial distribution, and conducted the time lag cross-correlation analysis. RESULTS: The Baidu Index temporal trend indicated that the changes of the Baidu Index had a clear correspondence with the development time node of the pandemic. The Baidu Index spatial distribution showed that in the regions of central and eastern China, with denser populations, larger internet user bases, and higher economic development levels, the public was more concerned about COVID-19. In addition, the Baidu Index was significantly correlated with six case indicators of new confirmed cases, new death cases, new cured discharge cases, cumulative confirmed cases, cumulative death cases, and cumulative cured discharge cases. Moreover, the Baidu Index was 0-4 days earlier than new confirmed and new death cases, and about 20 days earlier than new cured and discharged cases while 3-5 days later than the change of cumulative cases. CONCLUSIONS: The national public's demand for epidemic information is urgent regardless of whether it is located in the hardest hit area. The public was more sensitive to the daily new case data that represents the progress of the epidemic, but the public's attention to the epidemic situation in other areas may lag behind. We could set the Baidu Index as the sentinel and the database in the online infoveillance system for infectious disease and public health emergencies. According to the monitoring data, the government needs to prevent and control the possible outbreak in advance and communicate the risks to the public so as to ensure the physical and psychological health of the public in the epidemic.