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

Introduction

The coronavirus disease of 2019 (COVID-19), a highly contagious viral pneumonia, has caused catastrophic effects according to the World Health Organization (WHO) (1). Due to its rapid spread, at the time of writing this manuscript, the number of confirmed cases of COVID-19 has reached 205,338,159, including 4,333,094 deaths (2). Globally, to contain the pandemic, remarkable efforts incorporating technology from related domains have contributed to the sharp emergence and development of COVID-19 vaccines (3).

Previous studies have suggested that the reproductive number (R0) of the COVID-19 virus was approximately 3, which means that one carrier can infect 3 healthy individuals (4,5). The newly emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have presented even further challenges to the global anti-epidemic efforts (6-8). Briefly, vaccination plays a crucial role in diminishing the route of transmission (9,10). Therefore, the willingness of the public to accept immunization has become a key feature of epidemic control.

The epidemic in China has been fully under control since 2020 (11) due to strong public health prevention measures, as well as vaccination. With the promotion of various media and government policies, the COVID-19 vaccination rate in China is impressive, but further efforts are still required. Current studies reported many factors that contributed to COVID-19 vaccine hesitancy, such as the concerns of effectiveness, safety and the vague side-effect about the existing vaccine (12-15). COVID-19 vaccine hesitancy presented to be a noteworthy public health issue.

A massive amount of traffic gives an internet search engine (SE) greater pertinence. Previous researches reported the searching behaviors of internet users reflect the popularity of issues, and has even been shown to be the “outpost” of events under certain circumstances (16). An SE could obtain more, comprehensive, and real-time public information in a short time. During the last year in China according to StatCounter (https://statcounter.com/), Baidu captured more than 70% of the market within China, making it the most popular SE (17). The predictive effect of SE has been verified by the most recently reported research (16,18-21). Thus, it is conceivable that the public opinion of vaccination could be monitored by such SEs.

Recently, infodemiology studies concentrated more on the correlations of online SE trends with COVID-19 incidence and confirmed cases (16,22-24). However, in concern of the rapid-mutate viruses as well as the constant pandemic situation, it is necessary to explore the correlation between the online searching that reflected the public concern and the vaccination rates. Hence, the purpose of this paper was to use Baidu Index (BDI; https://index.baidu.com/v2/index.html#/) to explore the awareness about COVID-19 vaccination among the Chinese public, as well as to verify the role of internet SEs in monitoring public opinion during public health emergencies, and to assess the social impact of the related policies. We supposed that the findings from this study could help the government to grasp the trend of public opinion and provide a reference for policy making decisions.

Methods

BDI data collection

The algorithm of BDI is based on the search volume of the terms on the internet, further filtering and weighting the data to reflect the variation tendency of search scale for certain terms in Baidu during specific periods of time (25). In consideration of data collection from BDI and the searching behaviors of public, the final retrieval terms we selected were “Novel Coronavirus Pneumonia (xin xing guan Zhuang bing du fei yan, xin guang)”, “COVID-19 vaccine (xin guan yi miao)”, and “vaccine (yi miao)”. These daily relative search volumes (RSV) data were retrieved from BDI on 1 January 2020 to 1 July 2021. Besides the national total data, 6 representative provinces/cities/region were also included: Beijing, Shanghai, Guangdong, Heilongjiang, Sichuan, and Tibet. All the RSV data we calculated were absolute searching volumes.

Data sources

The number of vaccinations of China were derived from the National Health Commission of the People’s Republic of China (26). As the daily official report of COVID-19 vaccinations was instigated 23 March 2021, the data of daily vaccination numbers was gathered from 23 March 2021 to 1 July 2021. To ensure the reliability of policy information sources, we collected the official information and policies of vaccination from the National Health Commission of the People’s Republic of China (26), Chinese Center for Disease Control and Prevention (CDC) (27), Xinhua net (28), and People’s Daily (29). The related events were collected by chronological peak order. The data we collected were from an open publicly accessible website. There was no funding for this study.

Statistical analysis

This is a correlation analysis. The first confirmed case of COVID-19 in China was reported in January 2020 and the epidemic trend has been controlled since 22 April 2020. In the view of reflecting clearer variation and illustrating a longer tendency, the analysis time was set from 1 January 2020 to 1 July 2021.

Based on the data of more than one year, statistical analysis was performed using the software SPSS 25.0 (IBM Corp., Armonk, NY, USA) and GraphPad Prism 9 (GraphPad Software Inc., San Diego, CA, USA). We applied 3-day smoothing in a curve graph to reduce the unnecessary variation. The RSV and the number of vaccinations were separately superimposed on the 2 period curves for better comparison. Pearson correlation testing was used for further analysis. Differences were considered significant at P<0.05.

Results

BDI nationwide general trend

All the BDI data including searches conducted on personal computer (PC) and mobile were collected from 1 January 2020 to 1 July 2021, a total of 548 days.

As shown in Figure 1, the RSV curves of the COVID-19 and the vaccine exhibited some waves in specific periods, while the COVID-19 vaccine RSV curve exhibited an upward-fluctuating trend. Besides the initial growth in March 2020, the 4 obvious increases of the COVID-19 vaccine RSV curve appeared in August 2020, the end of 2020, March 2021, and May 2021, which related to the corresponding vaccine events. The peak value of each increase grew higher each time and reached 148,151 in the latest growth. For a better description, we selected the 5 periods with the most significant upward trend by taking the national BDI data as the reference standard. Peak I to V were set as 1 January 2020 to 25 February 2020, 4 June 2020 to 17 August 2020, 6 December 2020 to 31 January 2021, 25 February 2021 to 29 March 2021, and 29 April 2021 to 18 June 2021. In our following study, these growths were connected to the timing of vaccine policy. Table 1 shows the related events about vaccination against COVID-19 and the relevant time points. A total of 30 events are presented in order.

Figure 1 Total relative search volumes curves and vaccination cases. BDI, Baidu Index; COVID-19, coronavirus disease of 2019.

As the COVID-19 RSV grew from zero, the vaccine RSV ranged with it. The dominant searching status of vaccine RSV was replaced in August 2020 by COVID-19 vaccine according to the curve graph. During this period however, the variation of vaccine and COVID-19 vaccine RSV was correlated with the COVID-19 RSV.

As shown in Figure 1 and Table 2, the sharp increase of the vaccination number is correlated with the growth of COVID-19 vaccine RSV and vaccine RSV (r=0.382, P<0.001; r=0.256, P=0.010). Further, the COVID-19 vaccine RSV is correlated with the COVID-19 RSV and vaccine RSV (r=0.232, P<0.001; r=0.778, P<0.001).

BDI trend by provinces/cities/region

Among all the 6 selected provinces/cities/region, the form of RSV curves shared remarkable similarity to the national curves, especially that of the COVID-19 vaccine. Notably, Guangdong’s closest RSV curves to the national make it stand out, with upward fluctuating trends (Figure 2). On account of the particularity of the epidemic, the RSV of vaccine and COVID-19 vaccine after the episode of COVID-19 were usually associated with it.

Figure 2 Relative search volumes curves in provinces/cities/region. BDI, Baidu Index; COVID-19, coronavirus disease of 2019.

The results of the mean and max daily RSV among the different provinces/cities/region are shown in Table 3. An obviously smaller volume occurred in Tibet, which ranked last on all 3 terms’ RSV during the time of analysis (COVID-19 RSV mean =92.08, COVID-19 vaccine RSV mean =121.45, vaccine RSV mean =78.26). When we focused on the searching of COVID-19 vaccine, all these provinces/cities/region exhibited remarkable increases in the last 3 peaks. Guangdong ranked first by absolute advantages on mean volume in every peak (peak I mean =23.93, peak II mean =1,252.51, peak IV mean =2,910.91, peak V mean =11,300.88) except peak III, for which Beijing hit the highest (peak III mean =3,509.04).

Provincial daily average BDI contribution

Figure 3, in which the contribution of daily average BDI in different provinces/cities/region during the 548 days is represented, illustrates a prominent discrepancy. The percentage of Guangdong RSV was the maximum within the selected 6 provinces/cities/region among 3 terms, and that of Tibet was the lowest. In addition, as shown in Figure 3A, these 6 provinces/cities/region accounted for more than half the searching of COVID-19 in China during this period (54.56%). The parallels appeared in vaccine, with even less proportion of other provinces/cities/region (35.26%) (Figure 3B). Key differences in the distribution of COVID-19 vaccine daily average BDI among provinces/cities/region are displayed in Figure 3C. Compared to the other 2 RSV proportions, the selected provinces/cities/region here comprise less of the total volume (38.38%), leading to a more balanced national searching situation.

Figure 3 Daily average BDI contribution of provinces/cities/region in total. The contribution of daily average BDI of (A) “COVID-19”, (B) “vaccine”, and (C) “COVID-19 vaccine” in different provinces/cities/region. COVID-19, coronavirus disease of 2019; BDI, Baidu Index.

Discussion

The present study investigated the vaccine searching volume in BDI during the COVID-19 outbreak. The data were collected from 1 January 2020 to 1 July 2021. We found that the results revealed the public concern about COVID-19 vaccine and the tendency, which was influenced by the epidemic and related vaccine policy, provided a reference to optimize the allocation of prevention supplies resources and adjust the order of the epidemic prevention and control.

As we all know, vaccination decisions are determined by an interaction within individual social factors, historical and cultural factors, policy and regulations, and so on (30,31). The coverage of COVID-19 vaccination in China reflects a positive tendency of vaccination decisions. Considering the particularity of the epidemic period, COVID-19 vaccine information has almost solely come from internet. Social media had been thought of as the main resource of such medical information when there was a lack of research data (5,32,33). However, before we express an individual opinion on social media, we usually search the terms of interest online to fortify our knowledge. As an outpost of searching trend (16), BDI embodies the vaccine needs after the epidemic outbreak in time. The successive rising trend of 5 peaks reveals the public increasing attention about COVID-19 vaccine. There are several reasons that may explain the trend. Firstly, concerns about the repeated outbreaks of COVID-19 and the virus variation may add to the volume of searching. For another, vaccine approval policy by government would be another motivator. In addition, the sufficient production and the effectiveness of the COVID-19 vaccine, which has been supported by research, will prolong the relevance of the issue.

In details we noticed, the first peak, in which vaccine RSV held the line with COVID-19 vaccine RSV, is correlated with the issue that the COVID-19 epidemic had been declared and the concern about and full recognition of the SARS-CoV-2 virus had commenced, thanks to the vigorous promotion by mainstream media. The obvious growth in June and August 2020 matched the period during which the epidemic was basically under control. During this time, countries around the world were boosting their efforts towards COVID-19 vaccine development because the epidemic was spreading around the world causing damage and loss. Russia announced the registration of the world’s first COVID-19 vaccine on 11 August 2020, which might have triggered an increase in COVID-19 vaccine searching. During this period, the search words “volunteer”, “Russian vaccine” appeared most frequently.

With the successive development of 5 COVID-19 vaccines in China, the third peak arose in December 2020 to January 2021. When the Chinese CDC announced that China’s first COVID-19 vaccine had been conditionally released and would be free for all, the public interest for COVID-19 vaccines ran high. Consequently, the contraindication, side-effects, and other vaccine-associated issues hit the concerns of public. In addition, people paid more attention to the reserve of the COVID-19 vaccine considering its great demand within a short time. Still, some people questioned the vaccination necessity, which has been reported in previous studies (34,35). The higher searching peak in February 2021 to March 2021 reflects the new wave of vaccination that China’s inoculation exceeded 100 million during this time. This would have been contributed to by the strong policies pursued by the local government to ensure that all people who should be vaccinated are vaccinated. Several COVID-19 vaccines came into service within this period. Side-effects, expiration dates, as well as contraindications comprised the searching volume. Pregnancy was also included in the list of concerning words, which could be attributed to the worry about the side-effects of the COVID-19 vaccine for the pre-pregnant couples (36,37). Simultaneously, the vaccination number has been published since 23 March 2021 and updated daily, revealing the vast increase of the vaccination coverage.

The last but the highest peak emerged in the latest time, showing the huge searching volume about the vaccination to prevent COVID-19. The stronger and wider support-vaccination policy inspired the public willingness to receive the COVID-19 vaccine. As a consequence, people had more desire to obtain more vaccine-related information before and after vaccination. In addition to the already present vaccine-related attributes, with several marketed vaccines to prevent COVID-19 in China, the options of the type and the difference among varies vaccines also attracted public interest.

With regard to the local data of vaccine RSV, the variation tendency was basically the same compared to the total. Additionally, Figure 3 reveals a large proportion of vaccine RSV for Beijing, Shanghai, and Guangdong, and the western and northern cities account for less visually. This geographic distribution difference might be attributed to the economic development level as well as the population distribution and quality, on account that education plays a vital role in willingness to vaccinate (35,38-41). Gross domestic product (GDP) in Guangdong ranks first in China and has considerable floating population. These factors might contribute to its large searching volume. In addition, the epidemic situation and sporadic imported cases could be other drivers effecting the interest in vaccination. Comprehensively collected data of all regions might make the comparative research more powerful, and not surprisingly, could provide a reference for the implementation of local government policies.

According to CDC public data, more than 1.2 billion doses of COVID-19 vaccine had been administrated in China up to 1 July 2021, indicating a great achievement in COVID-19 epidemic prevention in China. The BDI data also reflects the vaccine hesitancy trend and public response to vaccine-related policies. People who concentrated more on the news of the COVID-19 vaccine might have a better understanding of it, and this in turn would make them more willing to receive the vaccine (31,42). Thereby, the more sufficient vaccine-related information is, the more motivated people become to receive the vaccine (43). The publicity and education about the implementation of COVID-19 vaccination among people, especially the elderly, may affect their willingness and reluctance to receive the vaccine.

At present, most of the previous studies on the willingness to vaccinate against COVID-19 have been online surveys, and have been conducted by students, medical staff, and on the global scale (44-47). The survey scope and sampling offset are greatly affected by human factors. In addition, most of them have been cross-sectional studies, which may not adequately reflect real-time changes and may not be easily used as a relevant decision-making reference. Besides, it is difficult to cover all aspects due to the lack of macro and micro considerations on factors influencing vaccination intention. However, information collection based on the SE in this paper can largely avoid the problem of information limitations and ensure the authenticity, reliability, and timeliness of information sources.

This study has several limitations. First of all, the data only collected from Baidu Index but not included other SE such as Google, Bing. Second, BDI has been accused for its lagged effect. For instance, person could not get vaccination immediately after searching online. Further exploration might take this into consideration. What’s more, due to the privacy protection, we lack the data about the age and occupation information, which might restrict the profound correlation analysis.

Including specific values and general tendency, this study collected data from the online SE, showing the strong capacities for gathering and consolidating data. The public concerns about vaccination against COVID-19 could predict and indicate the implementation of vaccine policies, and policies also effect the vaccine concerns. In addition to vaccination, we need to innovate new preventative approaches to this pandemic. Gathering the efforts globally from the general public, management, bio-medical domain, informatics, scientific domain, and policy makers will help to create a strong, cooperative shield against the COVID-19. We hope these findings may serve as a guidepost for further advancement of this epidemic battle. By advanced big data analysis, rapid adjustment and detailed solutions are expected in future challenges.

Acknowledgments

Funding: None.

Footnote

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-3064/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Jones)