Uncovering Adverse reactions following COVID-19 Monovalent XBB.1.5 Vaccination from Active Surveillance: A Text Mining Approach

Uncovering Adverse reactions following COVID-19 Monovalent XBB.1.5 Vaccination from Active Surveillance: A Text Mining Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Uncovering Adverse reactions following COVID-19 Monovalent XBB.1.5 Vaccination from Active Surveillance: A Text Mining Approach Hye Ah Lee, Bomi Park, Chung Ho Kim, Yeonjae Kim, Hyunjin Park, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5315120/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Unstructured text data collected through a surveillance system for vaccine safety monitoring can identify previously unreported adverse reactions and provide the information necessary to improve the surveillance system. Therefore, this study explored adverse reactions using text data gathered through an active surveillance system following monovalent XBB.1.5 COVID-19 vaccination. Methods A text mining analysis was conducted on 2,608 records from 1,864 individuals who reported any health conditions experienced within 7 days after vaccination in text format. Frequency analysis of key terms was performed, with subsequent analysis by sex, age, and concurrent influenza vaccination. Furthermore, semantic network analysis was conducted on terms reported simultaneously. Results The analysis identified various common (≥ 1%) adverse events, such as sleep disturbances, lumbago, and indigestion, which had not been frequently reported in prior literature. Moreover, although not common (≥ 0.1% to < 1%), adverse reactions affecting the eyes, ears, and oral cavity were also noted. These adverse reactions showed no significant differences in occurrence with or without simultaneous influenza vaccination. Through cooccurrence analysis and correlation coefficient assessments, associations were found between diarrhea and abdominal pain, as well as between musculoskeletal symptoms and cold-related symptoms. Conclusion This study used text mining to reveal previously unrecognized adverse reactions related to COVID-19 vaccination, thus expanding our understanding of the vaccine’s safety profile. The insights gained could further the scope of future investigations into adverse reactions to vaccines and improve the processing of text data in surveillance systems. Health sciences/Diseases Health sciences/Risk factors Biological sciences/Drug discovery/Drug safety Adverse reactions Data mining Pharmacovigilance Safety Vaccination Figures Figure 1 Figure 2 Figure 3 1. Introduction To respond to the unprecedented COVID-19 pandemic and control its public health impact, the development, approval, and distribution of safe and effective vaccines were rapidly accelerated [1]. Consequently, each country has been establishing and implementing a monitoring system for the safety of COVID-19 vaccines [1–3]. In Korea, a web-based passive vaccine safety surveillance system and a text message-based active vaccine safety surveillance system have been implemented [4,5]. These systems have significantly contributed to the identification of unexpected and undesirable adverse reactions, which can provide a foundation for predicting adverse reactions to vaccines against future COVID-19 variants or other viral infections. The Korea Disease Control and Prevention Agency (KDCA) endorsed a monovalent XBB.1.5 vaccine for COVID-19 prevention and announced a vaccination plan for September 2023 [6]. Starting from October 19, 2023, vaccinations began for individuals aged ≥ 65 years, immunocompromised individuals aged 12–64 years, and residents and workers of facilities vulnerable to infections, such as nursing hospitals and care facilities. During this period, it was recommended to administer the COVID-19 vaccination concurrently with the 2023–2024 seasonal influenza vaccination. Although several studies have explored adverse reactions to coadministration of the COVID-19 and influenza vaccines, vigilant monitoring of adverse reactions in the general population remains essential for a comprehensive understanding. Monitoring through spontaneous reporting systems can quickly provide awareness of emergent situations and information about new adverse reactions of concern [1]. In Korea, as the COVID-19 vaccine program was rolled out alongside the influenza vaccination, an active surveillance system based on text messages was implemented. This system encouraged participants to report any adverse reactions they experienced beyond those typically reported within the first 7 days after vaccination via text. Indeed, it facilitated the expansion of the investigative scope regarding adverse events by enhancing the questionnaire [7]. Text-based data can offer insights for exploring adverse reactions that have not previously been reported. Text mining approaches can reveal hidden knowledge by identifying patterns in massive unstructured texts [8]. Therefore, this study performed a text mining analysis to identify potential adverse reactions reported following the administration of the COVID-19 mRNA vaccine from October 19, 2023, to November 6, 2023. We also evaluated whether there were differences in reporting adverse reactions depending on whether the influenza vaccine was administered simultaneously. 2. Methods 2 . 1 . Data source This study used anonymized data collected by the KDCA. The KDCA conducted active surveillance following the administration of the COVID-19 monovalent XBB.1.5 vaccine during the 2023–2024 season using the Immunization Registry Information System [9]. Further details on the surveillance system are described elsewhere [4,5]. The vaccination campaign, which commenced on October 19, 2023, expanded to include all individuals aged 12–64 from November 1 [6]. A text-message (also known as short message service) based surveillance survey was conducted among vaccine recipients who consented to participate via smartphone, monitoring adverse reactions and health status for 7 days after vaccination. Data were collected from 10,000 respondents, targeted per vaccine manufacturer. Questionnaires related to adverse reactions after vaccination were divided into categories for local and systemic reactions (closed-ended questionnaire). Respondents were able to report multiple adverse reactions. In addition, an open-ended questionnaire was provided for respondents to describe any other health issues experienced after vaccination in text form. Among people (10,099 Pfizer-BioNTech and 10,083 Moderna) who received the COVID-19 vaccine between October 19, 2023 and November 6, 2023, we included adverse reaction data responded to via text at least once during the survey period. Consequently, for text mining analysis, the analytical dataset consisted of 2,608 records from 1,864 individuals. Data on concurrent COVID-19 and seasonal influenza vaccinations were also collected from the vaccination registration system. Variables including sex, age, vaccine manufacturer, and concurrent administration of the influenza vaccine were considered in the analysis. All participants provided informed consent for inclusion in the database for participation. The study was conducted according to the guidelines of the Declaration of Helsinki and the study protocol was reviewed and approved by the Institutional Review Board of Ewha Womans University (No: ewha-202401-0011-01). 2 . 2 Data pre-processing for text-mining Text preprocessing is crucial in text mining to eliminate noise and extract meaningful information [10]. This process is described in the Supplementary material . 2 . 3 Data analysis 2 . 3 . 1 Descriptive analysis A basic descriptive analysis was conducted on 1,864 COVID-19 vaccine recipients. Categorical data are presented as frequencies and percentages, while continuous variables are shown as means with standard deviations and medians with interquartile ranges. We also assessed the impact of concurrent influenza vaccination on basic characteristics using the chi-square test, t-test, and Mann-Whitney U test. 2 . 3 . 2 Word frequencies The frequency of adverse reaction-related words was classified into categories: very common (≥ 10%), common (≥ 1% to < 10%), and uncommon (≥ 0.1% to < 1%) [11]. In addition, the daily adverse reaction reporting percentage was calculated. Details are provided in the Supplementary material . 2 . 3 . 3 Semantic network To uncover patterns in the cooccurrence of adverse reactions, we conducted cooccurrence analysis and phi-coefficient analysis, visualizing the results to facilitate interpretation. In network analysis, linguistic units act as nodes, and the relationships observed in actual language use are expressed as links. Keywords with high centrality are considered core keywords, which can be represented as degree centrality, betweenness centrality, or closeness centrality. Degree centrality indicates how directly one keyword is connected to other keywords. Betweenness centrality measures the extent to which a node acts as an intermediary in connecting other nodes, and closeness centrality is determined based on the connection distance between nodes to illustrate how close one node is to another. These metrics can be quantified [12]. To examine the context in which words were used, network analysis included words such as ‘injection.’ The cooccurrence network depicted relationships through lines, where the thickness of each line represented the frequency of term pairs appearing together, regardless of their order. The relationship between keywords reported > 10 times was visualized, with degree centrality indicated by the node size. The phi-coefficient quantifies the degree to which a pair of terms appears together compared to how often the terms appear individually, and is equivalent to the correlation coefficient for a binary variable [13]. For the correlation matrix of terms, we visualized as a network only those pairs of terms with a correlation > 0.1, with the thickness of the line representing the phi-correlation coefficient. The Infomap algorithm was used to group nodes, which were represented by the same node color. All analyses were performed using R statistical software (version 4.3.1: R Foundation for Statistical Computing, Vienna, Austria), and R packages such as ‘KoNLP,’ ‘tidyr,’ ‘tidytext,’ and ‘ggraph’ were used. 2 . 4 . Data availability This study used anonymized data from the KDCA, collected through active surveillance of the COVID-19 XBB.1.5 vaccine during the 2023–2024 season via the Immunization Registry Information System. Since this is not open data, access is subject to approval from the KDCA. For data availability inquiries, please contact the KDCA directly. 3. Results Of the 1,864 respondents, 65.7% were male, with an average age of 68.1 years, and each individual reported adverse reactions about 1.4 times on average. In addition, 38.2% (n = 712) of the subjects received the seasonal influenza vaccine concurrently. There were no significant differences compared to the proportion in all participants. Compared to those who received only the COVID-19 vaccine, individuals who received both COVID-19 and influenza vaccines concurrently had a higher proportion of people > 65 years of age, and most of these individuals received the Moderna vaccine ( Table 1 ). In total, 2,608 adverse reactions following vaccination were reported by text message by 1,864 subjects. Adverse reactions were most commonly reported the day after vaccination, and reports decreased over time. There were no significant differences in the percentage of adverse reactions reported during the first 7 days after vaccination between groups based on whether they received concurrent influenza vaccine ( Supplemental Figure 1 ). Table 2 shows the terms reported > 1% among those who reported adverse reactions through text. The most frequently reported term was ‘body aches’ (21.9%), and terms for cold-related symptoms were also frequently reported. Sleep-related symptoms such as ‘drowsiness’ (2.5%) and ‘sleep disturbance’ (1.7%) were noted. In addition, terms such as ‘chest tightness’ (1.4%), ‘shortness of breath’ (1.4%), ‘indigestion’ (1.3%), ‘heart palpitations’ (1.3%), and ‘loss of appetite’ (1.1%) were also described. Although the frequency is classified as uncommon (<1%), eye-related adverse reactions such as ‘vision abnormality’ (0.8%), ‘eye pain’ (0.4%), and ‘dry eyes’ (<0.1%) were reported. Ear-related adverse reactions, including ‘tinnitus’ (0.4%), ‘earache’ (0.3%), and ‘ear fullness’ (0.2%), were also documented. From the 5th day after vaccination, four individuals reported ‘stomatitis’ (0.2%) as an adverse reaction. Other oral cavity-related adverse reactions included ‘gum pain’ (0.2%), ‘dry mouth’ (0.2%), ‘gum swelling’ (0.1%), and ‘tooth pain’ (0.1%). In addition, ‘hoarseness’ (0.7%), ‘blushing’ (0.6%), ‘increased blood pressure’ (0.4%), and ‘brain fog’ (0.4%) were reported. ‘Memory impairments’ were reported by 3 people (0.2%), all of whom had only received the COVID-19 vaccine ( Supplemental Table 1 ). Terms related to frequently reported adverse reactions showed differences in ranking between groups, but the reported percentages were generally similar across groups. Some terms, however, showed differences between groups; ‘sore throat’ and ‘palpitations’ were reported more often when only the COVID-19 vaccine was administered than when both the COVID-19 and influenza vaccines were administered together. The terms ‘headache,’ ‘cold sweat,’ ‘shortness of breath,’ ‘vomiting,’ and ‘indigestion’ were reported more frequently by women than by men ( Figure 1 ). The frequency of reporting ‘joint pain’ differed depending on age groups and was reported more frequently in younger age groups. However, other terms appeared similar ( Supplemental Figure 2 ). Reporting percentages of specific terms that were frequently reported during the survey period remained constant, decreased, or increased over the first 7 days following vaccination. The term ‘body aches’ was most frequently mentioned the day after vaccination and tended to decrease thereafter. Conversely, the terms ‘sputum,’ ‘cough,’ ‘runny nose,’ ‘sore throat,’ and ‘cold’ tended to increase as time passed following vaccination ( Figure 2 ). Figure 3 shows the results of the analysis of cooccurring terms. ‘injection’ and ‘headache’ were frequently reported along with other terms. ‘injection’ was mainly reported along with ‘pain,’ ‘myalgia,’ and ‘swelling,’ and ‘headache’ was mainly reported along with ‘fatigue,’ ‘body aches,’ and ‘dizziness.’ It was confirmed that various systemic adverse reactions were reported together. The results of the phi-coefficient analysis for the pairs of two terms are shown in Supplemental Figure 3 . The terms ‘injection’ and ‘pain’ showed the highest correlation coefficients, followed by the pair ‘abdominal pain’ and ‘diarrhea,’ and the pair ‘cough’ and ‘sputum’ ranking third. 4. Discussion Using a text mining approach, we identified various common (≥ 1%) adverse reactions among individuals vaccinated with the COVID-19 vaccine from October 19, 2023, to November 6, 2023, which were not commonly reported in the literature. Moreover, the reporting percentages for most identified adverse reactions remained consistent, irrespective of concurrent influenza vaccination. In early 2021, COVID-19 vaccination campaigns were initiated across various countries, including the United States [14]. The efficacy of these vaccines has been validated through both randomized controlled trials and observational studies [15]. However, given the unprecedented speed of vaccine approval, our understanding of potential adverse events remains incomplete. Although considerable time has elapsed, we continue to implement our COVID-19 vaccination program and monitor vaccine safety to respond to new variants and prevent outbreaks. Most studies have focused primarily on identifying a narrow spectrum of severe adverse reactions associated with the vaccines. Text analytics provide an opportunity to uncover a broader range of actual and potential concerns by analyzing keywords within data [16]. Therefore, we performed text analysis to gain a more comprehensive understanding of vaccine safety. Analyzing data from social media, such as Twitter, on topics related to COVID-19 has frequently involved studying the evolution of emotions and sentiments during the pandemic [17–19], and analyzing shared information topics [20–22]. Regarding the COVID-19 vaccine, text analysis has been conducted on reasons for hesitancy about vaccination [14], and sentiment analysis regarding the vaccine has been performed [23]. Methods such as sentiment analysis and topic modeling have been used to examine public perceptions. However, few studies have conducted text analysis specifically to identify potential adverse reactions. Some studies have reported temporary adverse events that were not previously documented in the literature. Similar to previous studies [24,25], symptoms related to colds were identified, and adverse reactions such as sleep disturbance, indigestion, lumbago, and loss of appetite were also observed. In addition, our text analysis identified uncommon adverse reactions (≥ 0.1% to < 1%) related to the eyes, ears, and oral cavity. Although previous vaccine safety surveillance results prompted investigations into symptoms such as chest pain, dizziness, and whole-body redness, our analysis detected various additional adverse reactions. Although there were no reports of widespread vision-related side effects related to the COVID-19 vaccine, dry eyes and transient vision loss/amaurosis fugax were reported in one patient at each dose among adverse reactions after the first and second doses of BNT162b2 mRNA (Pfizer-BioNTech, Comirnaty) among healthcare workers in Italy [26]. Another study conducted in China to monitor inactivated COVID-19 vaccines (WIV04 and HB02 vaccines) reported blurred vision in two patients with the second dose, and bloodshot congested eyes in two and three patients with each dose, respectively. It also reported uncommon adverse reactions such as palpitation, oral infection, and irregular menstruation [27]. A study conducted in the UK among people vaccinated between December 8, 2020, and May 17, 2021, also reported adverse reactions related to the eyes and ears [28]. For practical reasons, the coadministration of the COVID-19 vaccine and the 2023–2024 seasonal influenza vaccine was recommended [6,29]. Regarding the safety of simultaneous vaccination, compared to those who received only the COVID-19 vaccine, those who received both the seasonal influenza vaccine and the COVID-19 mRNA booster at the same time reported that any systemic reactions were frequent (8% for Pfizer-BioNTech booster and 11% for Moderna booster), but mild and passed quickly [28,30]. The timing and sequence of vaccination against COVID-19 and influenza (TACTIC) study, a randomized clinical trial, found that coadministration of influenza and COVID-19 vaccines showed a lower response of quantitative and functional antibodies than the COVID-19 vaccine alone [25]. However, no differences in adverse reactions were observed [25], aligning with previous findings [31,32]. In this study, from day 0–7 after vaccination, sore throat and palpitation reactions were reported more frequently in those who received only the COVID-19 vaccination compared to those who received the simultaneous vaccination, but other adverse reactions were similar. However, it should be noted that spontaneous reporting of adverse events allows for a cross-sectional assessment of actual response, which can often result in underreporting or missing data [33,34]. Therefore, discussing causality is not appropriate. If we investigate the list of various adverse reactions by improving the investigation system, differences can be verified later. The advantage of text analysis lies in its ability to uncover the main topics actively and potentially discussed within a text, while also clarifying the semantic relationships and meanings. We observed that reporting patterns of adverse reactions after vaccination varied over time, with reports of cold-related adverse reactions increasing as time progressed. Our findings align with studies indicating that the occurrence patterns of symptoms differ [28], mirroring our results. Reporting patterns showed that pain-related adverse reactions and cold-related adverse reactions were more often reported together, while ‘diarrhea’ and ‘abdominal pain’ were less often reported together with other adverse reactions. Interestingly, the frequently reported ‘sleep disturbance’ and ‘cold sweat’ were not observed in the cooccurrence network and correlation analysis. One study reported cooccurring symptoms of COVID-19 using social media data [35], but there were no studies on adverse events following COVID-19 vaccination. Through data collected through text, self-reported health problems were segmented, and various health problems were identified for each body organ. It also provides insight into understanding potential adverse reactions. When interpreting the results of this study, in addition to the limitations reported in a previous study [3], the following points need to be kept in mind. Because similar responses were mixed, local, and systemic adverse reactions were not analyzed separately. This may be because the meaning of the question was not conveyed well or due to differences in understanding. Because symptoms are not confirmed according to clinical diagnostic criteria, there may be problems with the accuracy of reported adverse reactions. In addition, with data collected through self-reporting, there may be issues with under-reporting and missing data of adverse reactions. As only text responses are considered, adverse reactions investigated through closed-ended questions are likely to be underestimated. However, most respondents answered closed-ended questions and also responded in text, which would have resulted in redundant reporting. In addition, despite the extensive time devoted to preprocessing for text analysis, the accuracy of the results may have been impacted by factors such as inaccurate answers, responses not related to adverse reactions, and reports of underlying disease history. Nevertheless, we were able to explore in detail potential adverse reactions in addition to those previously reported through text mining analysis. We also explored whether there were differences in adverse reactions reported in text depending on the coadministration of influenza at the same time. This provides a broader understanding of concurrent adverse reactions by visualizing them through continuously reported phi-coefficient analysis and cooccurrence analysis. Carrying out timely public health responses to protect public health is even more important during a pandemic. The active surveillance system for vaccine safety communicates directly with vaccine recipients and can quickly detect safety issues. However, this presents a realistic barrier to identifying potentially hidden adverse reactions in text-collected data. Therefore, it is necessary to develop a system that can automatically classify data collected as text, and the results of this study can be used to lay the foundation for this. 5. Conclusions This study explored hidden adverse reactions among the data collected through the active surveillance system for COVID-19 vaccines using a text mining approach. Our results contribute to a better understanding of vaccine adverse reactions and provide the information needed to improve future adverse reaction data management systems. Declarations Declaration of interests All authors declare that no conflict of interest. Author contribution Conceptualization: Park Hyesook, Lee HA, Park B; Data curation: Kim CH, Kim Y, Park Hyunjin, Jun S, Lee Hyelim; Formal analysis: Lee HA, Park B, Kim CH, Kim Y; Methodology: Lee HA, Park B; Project administration: Park Hyunjin, Jun S, Lee Hyelim, Kwon SL, Heo Y, Lee Hyungmin; Visualization: Lee HA, Park B, Kim CH, Kim Y; Writing - original draft: Lee HA, Park B; Writing - review & editing: all authors. Funding This study received financial support from the COVID-19 Vaccination Program (6400-6434-300), which is administered by the Korea Disease Control and Prevention Agency. Acknowledgement We would like to express our sincere gratitude to all the participants who took part in the survey for this study. References Durand J, Dogné JM, Cohet C, Browne K, Gordillo-Marañón M, Piccolo L, Zaccaria C, Genov G. Safety Monitoring of COVID-19 Vaccines: Perspective from the European Medicines Agency. Clin Pharmacol Ther. 2023;113(6):1223-1234. 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EudraVigilance - European Database of Suspected Adverse Drug Reaction Reports; 2021. Wu J, Wang L, Hua Y, Li M, Zhou L, Bates DW, Yang J. Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study. J Med Internet Res. 2023 Mar 14;25:e45419. doi: 10.2196/45419. PMID: 36812402; PMCID: PMC10131634. Tables Table 1 Characteristics of study subjects who reported to adverse reactions via text after receiving mRNA COVID-19 vaccine (October 19, 2023 to November 6, 2023, n = 1,864) Total (n = 1864) COVID-19 vaccine only (n = 1152, 61.80%) Coadministration COVID-19 and influenza vaccines (n = 712, 38.20%) P value Sex Male 1224 (65.67%) 743 (64.50%) 481 (67.56%) 0.177 Female 640 (34.33%) 409 (35.50%) 231 (32.44%) Age 68.11 (12.03) 67.17 (12.53) 69.63 (11.01) < 0.001 < 65 years 360 (19.31%) 260 (22.57%) 100 (14.04%) < 0.001 65–74 years 1019 (54.67%) 617 (53.56%) 402 (56.46%) ≥ 75 years 485 (26.02%) 275 (23.87%) 210 (29.49%) Vaccine manufacturer Moderna 1022 (54.83%) 580 (50.35%) 442 (62.08%) < 0.001 Pfizer-BioNTech 842 (45.17%) 572 (49.65%) 270 (37.92%) Frequency of reports Mean (SD) 1.40 (0.83) 1.41 (0.84) 1.39 (0.81) 0.598 Median (IQR) 1.0 (1.0–2.0) 1.0 (1.0–2.0) 1.0 (1.0–1.0) 0.564 SD; standard deviation, IQR; interquartile range Table 2 Terms frequently reported as adverse reactions during the first 7 days following mRNA COVID-19 vaccination (October 19, 2023 to November 6, 2023) Category Rank Adverse reactions Frequency Percentage (%) Very common (≥ 10.0%) 1 Body aches 408 21.9 2 Fatigue 379 20.3 3 Headache 272 14.6 4 Pain 240 12.9 5 Fever 224 12.0 Common (≥ 1.0% to < 10.0%) 6 Dizziness 144 7.7 7 Myalgia 128 6.9 8 Chills 126 6.8 9 Cold 121 6.5 10 Cough 111 6.0 11 Runny nose 75 4.0 12 Sore throat 73 3.9 13 Sputum 51 2.7 14 Nausea 46 2.5 15 Drowsiness 46 2.5 16 Diarrhea 44 2.4 17 Arm pain 43 2.3 18 Cold sweat 42 2.3 19 Itching 41 2.2 20 Swelling 39 2.1 21 Sleep disturbance 31 1.7 22 Shoulder pain 28 1.5 23 Chest tightness 27 1.4 24 Chest pain 26 1.4 25 Lumbago 26 1.4 26 Shortness of breath 26 1.4 27 Indigestion 25 1.3 28 Palpitations 24 1.3 29 Joint pain 23 1.2 30 Vomiting 22 1.2 31 Loss of appetite 20 1.1 If an individual reported the same adverse reaction repeatedly over several days, it was treated as a single event. Percentage (%) was calculated for 1,864 individuals. Gray background indicates newly identified adverse reactions via text analysis. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5315120","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":370071507,"identity":"92aecd8e-df0b-4b80-a0a8-28623c014514","order_by":0,"name":"Hye Ah Lee","email":"","orcid":"","institution":"Ewha Womans University Mokdong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hye","middleName":"Ah","lastName":"Lee","suffix":""},{"id":370071508,"identity":"a735e93c-4ed8-40a9-92eb-9e4c4bdf59ad","order_by":1,"name":"Bomi Park","email":"","orcid":"","institution":"Chung-Ang University","correspondingAuthor":false,"prefix":"","firstName":"Bomi","middleName":"","lastName":"Park","suffix":""},{"id":370071509,"identity":"dc53e5c5-adc7-41ac-8db7-e8c4bd73ee5a","order_by":2,"name":"Chung Ho Kim","email":"","orcid":"","institution":"Chung-Ang University","correspondingAuthor":false,"prefix":"","firstName":"Chung","middleName":"Ho","lastName":"Kim","suffix":""},{"id":370071510,"identity":"243f4ef5-e9f7-4fda-8607-6f2b2ef49113","order_by":3,"name":"Yeonjae Kim","email":"","orcid":"","institution":"Chung-Ang University","correspondingAuthor":false,"prefix":"","firstName":"Yeonjae","middleName":"","lastName":"Kim","suffix":""},{"id":370071511,"identity":"abf04dc5-c7ba-43c0-918b-a3d52d1e49fa","order_by":4,"name":"Hyunjin Park","email":"","orcid":"","institution":"Ewha Womans 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Agency","correspondingAuthor":false,"prefix":"","firstName":"Seunghyun","middleName":"Lewis","lastName":"Kwon","suffix":""},{"id":370071515,"identity":"f68c2098-ef07-4ef0-b0df-f4e3655358ed","order_by":8,"name":"Yeseul Heo","email":"","orcid":"","institution":"Korea Disease Control and Prevention Agency","correspondingAuthor":false,"prefix":"","firstName":"Yeseul","middleName":"","lastName":"Heo","suffix":""},{"id":370071516,"identity":"18eb78e3-86fb-4d28-97ef-1d3e2cd5224d","order_by":9,"name":"Hyungmin Lee","email":"","orcid":"","institution":"Korea Disease Control and Prevention Agency","correspondingAuthor":false,"prefix":"","firstName":"Hyungmin","middleName":"","lastName":"Lee","suffix":""},{"id":370071517,"identity":"b052455d-f7ca-4ded-9db1-6057098c635b","order_by":10,"name":"Hyesook Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBACAyBmbKiAMIAggVgtZ0jW0thGihZz6eZnH2fOs0nczn6A8cMPhrR8glos5xwznrlxW1rizp4EZskehhzLBoIOu5FgzPhw2+HcDQcSGKQZGCoMCNpicCP9M+PDOf9zN5x/wPybSC05xowbGw7kbriRwAa0JYcILXfOFDPOOJZcv+HGwzbLHoM0IrTcbt/M2FNjZ2xwPvnwjR8VyYS1MEjAWYwN8NghVssoGAWjYBSMAhwAANCtP+pkpJYkAAAAAElFTkSuQmCC","orcid":"","institution":"Ewha Womans University","correspondingAuthor":true,"prefix":"","firstName":"Hyesook","middleName":"","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2024-10-23 02:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5315120/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5315120/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67687620,"identity":"d90f3305-f92b-4d46-8f9b-8dbb87ebfda4","added_by":"auto","created_at":"2024-10-28 16:54:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216187,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences of frequent terms for adverse reactions reported via text during the first 7 days following mRNA COVID-19 vaccination (October 19, 2023 to November 6, 2023), according to (A) with or without a concurrent influenza vaccine and (B) sex\u003c/p\u003e\n\u003cp\u003eAsterisksindicate statistical differences (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) between groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5315120/v1/bcfdba3a4e433089ae33cea9.png"},{"id":67687621,"identity":"40a87d29-868a-4643-9aa0-5b4ae9ce925f","added_by":"auto","created_at":"2024-10-28 16:54:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":320270,"visible":true,"origin":"","legend":"\u003cp\u003ePercentages (%) of daily reports of frequently reported terms during the first 7 days following mRNA COVID-19 vaccination (October 19, 2023 to November 6, 2023)\u003c/p\u003e\n\u003cp\u003eThe percentages were calculated using the frequency of reported terms, focusing on those who provided adverse reactions via text on each day.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5315120/v1/ddfc06f3f9403bef1faa8290.png"},{"id":67687623,"identity":"0fdf8a6e-5492-4ce8-8612-fc360746bfc8","added_by":"auto","created_at":"2024-10-28 16:54:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":204152,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence network diagram for terms related to adverse reactions reported via text during the first 7 days following mRNA COVID-19 vaccination (October 19, 2023 to November 6, 2023)\u003c/p\u003e\n\u003cp\u003eNode size indicates centrality and edge thickness indicates the frequency of term pairs. It is organized around key terms (n=24) from pairs that have been reported more than 10 times.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5315120/v1/6d81bff5cc20425dd85de3e8.png"},{"id":75576468,"identity":"9ec9b490-5138-47c3-a12e-20b76d7383b2","added_by":"auto","created_at":"2025-02-06 04:38:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1441832,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5315120/v1/5292d8c7-5991-4df3-8e29-6ab29331e239.pdf"},{"id":67688067,"identity":"b56c249f-c827-49ae-8784-f710fd667843","added_by":"auto","created_at":"2024-10-28 17:02:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":233565,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-5315120/v1/f636d9e7dc3da87babbf68f9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Uncovering Adverse reactions following COVID-19 Monovalent XBB.1.5 Vaccination from Active Surveillance: A Text Mining Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTo respond to the unprecedented COVID-19 pandemic and control its public health impact, the development, approval, and distribution of safe and effective vaccines were rapidly accelerated [1]. Consequently, each country has been establishing and implementing a monitoring system for the safety of COVID-19 vaccines [1\u0026ndash;3]. In Korea, a web-based passive vaccine safety surveillance system and a text message-based active vaccine safety surveillance system have been implemented [4,5]. These systems have significantly contributed to the identification of unexpected and undesirable adverse reactions, which can provide a foundation for predicting adverse reactions to vaccines against future COVID-19 variants or other viral infections.\u003c/p\u003e \u003cp\u003eThe Korea Disease Control and Prevention Agency (KDCA) endorsed a monovalent XBB.1.5 vaccine for COVID-19 prevention and announced a vaccination plan for September 2023 [6]. Starting from October 19, 2023, vaccinations began for individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, immunocompromised individuals aged 12\u0026ndash;64 years, and residents and workers of facilities vulnerable to infections, such as nursing hospitals and care facilities. During this period, it was recommended to administer the COVID-19 vaccination concurrently with the 2023\u0026ndash;2024 seasonal influenza vaccination. Although several studies have explored adverse reactions to coadministration of the COVID-19 and influenza vaccines, vigilant monitoring of adverse reactions in the general population remains essential for a comprehensive understanding.\u003c/p\u003e \u003cp\u003eMonitoring through spontaneous reporting systems can quickly provide awareness of emergent situations and information about new adverse reactions of concern [1]. In Korea, as the COVID-19 vaccine program was rolled out alongside the influenza vaccination, an active surveillance system based on text messages was implemented. This system encouraged participants to report any adverse reactions they experienced beyond those typically reported within the first 7 days after vaccination via text. Indeed, it facilitated the expansion of the investigative scope regarding adverse events by enhancing the questionnaire [7]. Text-based data can offer insights for exploring adverse reactions that have not previously been reported. Text mining approaches can reveal hidden knowledge by identifying patterns in massive unstructured texts [8]. Therefore, this study performed a text mining analysis to identify potential adverse reactions reported following the administration of the COVID-19 mRNA vaccine from October 19, 2023, to November 6, 2023. We also evaluated whether there were differences in reporting adverse reactions depending on whether the influenza vaccine was administered simultaneously.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2\u003c/em\u003e.\u003cem\u003e1\u003c/em\u003e. \u003cem\u003eData source\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis study used anonymized data collected by the KDCA. The KDCA conducted active surveillance following the administration of the COVID-19 monovalent XBB.1.5 vaccine during the 2023\u0026ndash;2024 season using the Immunization Registry Information System [9]. Further details on the surveillance system are described elsewhere [4,5]. The vaccination campaign, which commenced on October 19, 2023, expanded to include all individuals aged 12\u0026ndash;64 from November 1 [6]. A text-message (also known as short message service) based surveillance survey was conducted among vaccine recipients who consented to participate via smartphone, monitoring adverse reactions and health status for 7 days after vaccination. Data were collected from 10,000 respondents, targeted per vaccine manufacturer.\u003c/p\u003e \u003cp\u003eQuestionnaires related to adverse reactions after vaccination were divided into categories for local and systemic reactions (closed-ended questionnaire). Respondents were able to report multiple adverse reactions. In addition, an open-ended questionnaire was provided for respondents to describe any other health issues experienced after vaccination in text form.\u003c/p\u003e \u003cp\u003eAmong people (10,099 Pfizer-BioNTech and 10,083 Moderna) who received the COVID-19 vaccine between October 19, 2023 and November 6, 2023, we included adverse reaction data responded to via text at least once during the survey period. Consequently, for text mining analysis, the analytical dataset consisted of 2,608 records from 1,864 individuals. Data on concurrent COVID-19 and seasonal influenza vaccinations were also collected from the vaccination registration system. Variables including sex, age, vaccine manufacturer, and concurrent administration of the influenza vaccine were considered in the analysis.\u003c/p\u003e \u003cp\u003eAll participants provided informed consent for inclusion in the database for participation. The study was conducted according to the guidelines of the Declaration of Helsinki and the study protocol was reviewed and approved by the Institutional Review Board of Ewha Womans University (No: ewha-202401-0011-01).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2\u003c/em\u003e.\u003cem\u003e2 Data pre-processing for text-mining\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eText preprocessing is crucial in text mining to eliminate noise and extract meaningful information [10]. This process is described in the \u003cb\u003eSupplementary material\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2\u003c/em\u003e.\u003cem\u003e3 Data analysis\u003c/em\u003e\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e2\u003c/em\u003e.\u003cem\u003e3\u003c/em\u003e.\u003cem\u003e1 Descriptive analysis\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eA basic descriptive analysis was conducted on 1,864 COVID-19 vaccine recipients. Categorical data are presented as frequencies and percentages, while continuous variables are shown as means with standard deviations and medians with interquartile ranges. We also assessed the impact of concurrent influenza vaccination on basic characteristics using the chi-square test, t-test, and Mann-Whitney U test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e2\u003c/em\u003e.\u003cem\u003e3\u003c/em\u003e.\u003cem\u003e2 Word frequencies\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe frequency of adverse reaction-related words was classified into categories: very common (\u0026ge;\u0026thinsp;10%), common (\u0026ge;\u0026thinsp;1% to \u0026lt;\u0026thinsp;10%), and uncommon (\u0026ge;\u0026thinsp;0.1% to \u0026lt;\u0026thinsp;1%) [11]. In addition, the daily adverse reaction reporting percentage was calculated. Details are provided in the \u003cb\u003eSupplementary material\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e\u003cem\u003e2\u003c/em\u003e.\u003cem\u003e3\u003c/em\u003e.\u003cem\u003e3 Semantic network\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo uncover patterns in the cooccurrence of adverse reactions, we conducted cooccurrence analysis and phi-coefficient analysis, visualizing the results to facilitate interpretation. In network analysis, linguistic units act as nodes, and the relationships observed in actual language use are expressed as links. Keywords with high centrality are considered core keywords, which can be represented as degree centrality, betweenness centrality, or closeness centrality. Degree centrality indicates how directly one keyword is connected to other keywords. Betweenness centrality measures the extent to which a node acts as an intermediary in connecting other nodes, and closeness centrality is determined based on the connection distance between nodes to illustrate how close one node is to another. These metrics can be quantified [12]. To examine the context in which words were used, network analysis included words such as \u0026lsquo;injection.\u0026rsquo;\u003c/p\u003e \u003cp\u003eThe cooccurrence network depicted relationships through lines, where the thickness of each line represented the frequency of term pairs appearing together, regardless of their order. The relationship between keywords reported\u0026thinsp;\u0026gt;\u0026thinsp;10 times was visualized, with degree centrality indicated by the node size.\u003c/p\u003e \u003cp\u003eThe phi-coefficient quantifies the degree to which a pair of terms appears together compared to how often the terms appear individually, and is equivalent to the correlation coefficient for a binary variable [13]. For the correlation matrix of terms, we visualized as a network only those pairs of terms with a correlation\u0026thinsp;\u0026gt;\u0026thinsp;0.1, with the thickness of the line representing the phi-correlation coefficient. The Infomap algorithm was used to group nodes, which were represented by the same node color.\u003c/p\u003e \u003cp\u003eAll analyses were performed using R statistical software (version 4.3.1: R Foundation for Statistical Computing, Vienna, Austria), and R packages such as \u0026lsquo;KoNLP,\u0026rsquo; \u0026lsquo;tidyr,\u0026rsquo; \u0026lsquo;tidytext,\u0026rsquo; and \u0026lsquo;ggraph\u0026rsquo; were used.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003e2\u003c/em\u003e.\u003cem\u003e4\u003c/em\u003e. \u003cem\u003eData availability\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThis study used anonymized data from the KDCA, collected through active surveillance of the COVID-19 XBB.1.5 vaccine during the 2023\u0026ndash;2024 season via the Immunization Registry Information System. Since this is not open data, access is subject to approval from the KDCA. For data availability inquiries, please contact the KDCA directly.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOf the 1,864 respondents, 65.7% were male, with an average age of 68.1 years, and each individual reported adverse reactions about 1.4 times on average. In addition, 38.2% (n = 712) of the subjects received the seasonal influenza vaccine concurrently. There were no significant differences compared to the proportion in all participants. Compared to those who received only the COVID-19 vaccine, individuals who received both COVID-19 and influenza vaccines concurrently had a higher proportion of people \u0026gt; 65 years of age, and most of these individuals received the Moderna vaccine (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn total, 2,608 adverse reactions following vaccination were reported by text message by 1,864 subjects. Adverse reactions were most commonly reported the day after vaccination, and reports decreased over time. There were no significant differences in the percentage of adverse reactions reported during the first 7 days after vaccination between groups based on whether they received concurrent influenza vaccine (\u003cstrong\u003eSupplemental Figure 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e shows the terms reported \u0026gt; 1% among those who reported adverse reactions through text. The most frequently reported term was ‘body aches’ (21.9%), and terms for cold-related symptoms were also frequently reported. Sleep-related symptoms such as ‘drowsiness’ (2.5%) and ‘sleep disturbance’ (1.7%) were noted. In addition, terms such as ‘chest tightness’ (1.4%), ‘shortness of breath’ (1.4%), ‘indigestion’ (1.3%), ‘heart palpitations’ (1.3%), and ‘loss of appetite’ (1.1%) were also described.\u003c/p\u003e\n\u003cp\u003eAlthough the frequency is classified as uncommon (\u0026lt;1%), eye-related adverse reactions such as ‘vision abnormality’ (0.8%), ‘eye pain’ (0.4%), \u0026nbsp;and ‘dry eyes’ (\u0026lt;0.1%) were reported. Ear-related adverse reactions, including ‘tinnitus’ (0.4%), ‘earache’ (0.3%), and ‘ear fullness’ (0.2%), were also documented. From the 5th day after vaccination, four individuals reported ‘stomatitis’ (0.2%) as an adverse reaction. Other oral cavity-related adverse reactions included ‘gum pain’ (0.2%), ‘dry mouth’ (0.2%), ‘gum swelling’ (0.1%), and ‘tooth pain’ (0.1%). In addition, ‘hoarseness’ (0.7%), ‘blushing’ (0.6%), ‘increased blood pressure’ (0.4%), and ‘brain fog’ (0.4%) were reported. ‘Memory impairments’ were reported by 3 people (0.2%), all of whom had only received the COVID-19 vaccine (\u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTerms related to frequently reported adverse reactions showed differences in ranking between groups, but the reported percentages were generally similar across groups. Some terms, however, showed differences between groups; ‘sore throat’ and ‘palpitations’ were reported more often when only the COVID-19 vaccine was administered than when both the COVID-19 and influenza vaccines were administered together. The terms ‘headache,’ ‘cold sweat,’ ‘shortness of breath,’ ‘vomiting,’ and ‘indigestion’ were reported more frequently by women than by men (\u003cstrong\u003eFigure 1\u003c/strong\u003e). The frequency of reporting ‘joint pain’ differed depending on age groups and was reported more frequently in younger age groups. However, other terms appeared similar (\u003cstrong\u003eSupplemental Figure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eReporting percentages of specific terms that were frequently reported during the survey period remained constant, decreased, or increased over the first 7 days following vaccination. The term ‘body aches’ was most frequently mentioned the day after vaccination and tended to decrease thereafter. Conversely, the terms ‘sputum,’ ‘cough,’ ‘runny nose,’ ‘sore throat,’ and ‘cold’ tended to increase as time passed following vaccination (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3\u003c/strong\u003e shows the results of the analysis of cooccurring terms. ‘injection’ and ‘headache’ were frequently reported along with other terms. ‘injection’ was mainly reported along with ‘pain,’ ‘myalgia,’ and ‘swelling,’ and ‘headache’ was mainly reported along with ‘fatigue,’ ‘body aches,’ and ‘dizziness.’ It was confirmed that various systemic adverse reactions were reported together. The results of the phi-coefficient analysis for the pairs of two terms are shown in \u003cstrong\u003eSupplemental Figure 3\u003c/strong\u003e. The terms ‘injection’ and ‘pain’ showed the highest correlation coefficients, followed by the pair ‘abdominal pain’ and ‘diarrhea,’ and the pair ‘cough’ and ‘sputum’ ranking third.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUsing a text mining approach, we identified various common (\u0026ge;\u0026thinsp;1%) adverse reactions among individuals vaccinated with the COVID-19 vaccine from October 19, 2023, to November 6, 2023, which were not commonly reported in the literature. Moreover, the reporting percentages for most identified adverse reactions remained consistent, irrespective of concurrent influenza vaccination.\u003c/p\u003e \u003cp\u003eIn early 2021, COVID-19 vaccination campaigns were initiated across various countries, including the United States [14]. The efficacy of these vaccines has been validated through both randomized controlled trials and observational studies [15]. However, given the unprecedented speed of vaccine approval, our understanding of potential adverse events remains incomplete. Although considerable time has elapsed, we continue to implement our COVID-19 vaccination program and monitor vaccine safety to respond to new variants and prevent outbreaks. Most studies have focused primarily on identifying a narrow spectrum of severe adverse reactions associated with the vaccines. Text analytics provide an opportunity to uncover a broader range of actual and potential concerns by analyzing keywords within data [16]. Therefore, we performed text analysis to gain a more comprehensive understanding of vaccine safety.\u003c/p\u003e \u003cp\u003eAnalyzing data from social media, such as Twitter, on topics related to COVID-19 has frequently involved studying the evolution of emotions and sentiments during the pandemic [17\u0026ndash;19], and analyzing shared information topics [20\u0026ndash;22]. Regarding the COVID-19 vaccine, text analysis has been conducted on reasons for hesitancy about vaccination [14], and sentiment analysis regarding the vaccine has been performed [23]. Methods such as sentiment analysis and topic modeling have been used to examine public perceptions. However, few studies have conducted text analysis specifically to identify potential adverse reactions.\u003c/p\u003e \u003cp\u003eSome studies have reported temporary adverse events that were not previously documented in the literature. Similar to previous studies [24,25], symptoms related to colds were identified, and adverse reactions such as sleep disturbance, indigestion, lumbago, and loss of appetite were also observed. In addition, our text analysis identified uncommon adverse reactions (\u0026ge;\u0026thinsp;0.1% to \u0026lt;\u0026thinsp;1%) related to the eyes, ears, and oral cavity. Although previous vaccine safety surveillance results prompted investigations into symptoms such as chest pain, dizziness, and whole-body redness, our analysis detected various additional adverse reactions.\u003c/p\u003e \u003cp\u003eAlthough there were no reports of widespread vision-related side effects related to the COVID-19 vaccine, dry eyes and transient vision loss/amaurosis fugax were reported in one patient at each dose among adverse reactions after the first and second doses of BNT162b2 mRNA (Pfizer-BioNTech, Comirnaty) among healthcare workers in Italy [26]. Another study conducted in China to monitor inactivated COVID-19 vaccines (WIV04 and HB02 vaccines) reported blurred vision in two patients with the second dose, and bloodshot congested eyes in two and three patients with each dose, respectively. It also reported uncommon adverse reactions such as palpitation, oral infection, and irregular menstruation [27]. A study conducted in the UK among people vaccinated between December 8, 2020, and May 17, 2021, also reported adverse reactions related to the eyes and ears [28].\u003c/p\u003e \u003cp\u003eFor practical reasons, the coadministration of the COVID-19 vaccine and the 2023\u0026ndash;2024 seasonal influenza vaccine was recommended [6,29]. Regarding the safety of simultaneous vaccination, compared to those who received only the COVID-19 vaccine, those who received both the seasonal influenza vaccine and the COVID-19 mRNA booster at the same time reported that any systemic reactions were frequent (8% for Pfizer-BioNTech booster and 11% for Moderna booster), but mild and passed quickly [28,30]. The timing and sequence of vaccination against COVID-19 and influenza (TACTIC) study, a randomized clinical trial, found that coadministration of influenza and COVID-19 vaccines showed a lower response of quantitative and functional antibodies than the COVID-19 vaccine alone [25]. However, no differences in adverse reactions were observed [25], aligning with previous findings [31,32].\u003c/p\u003e \u003cp\u003eIn this study, from day 0\u0026ndash;7 after vaccination, sore throat and palpitation reactions were reported more frequently in those who received only the COVID-19 vaccination compared to those who received the simultaneous vaccination, but other adverse reactions were similar. However, it should be noted that spontaneous reporting of adverse events allows for a cross-sectional assessment of actual response, which can often result in underreporting or missing data [33,34]. Therefore, discussing causality is not appropriate. If we investigate the list of various adverse reactions by improving the investigation system, differences can be verified later.\u003c/p\u003e \u003cp\u003eThe advantage of text analysis lies in its ability to uncover the main topics actively and potentially discussed within a text, while also clarifying the semantic relationships and meanings. We observed that reporting patterns of adverse reactions after vaccination varied over time, with reports of cold-related adverse reactions increasing as time progressed. Our findings align with studies indicating that the occurrence patterns of symptoms differ [28], mirroring our results. Reporting patterns showed that pain-related adverse reactions and cold-related adverse reactions were more often reported together, while \u0026lsquo;diarrhea\u0026rsquo; and \u0026lsquo;abdominal pain\u0026rsquo; were less often reported together with other adverse reactions. Interestingly, the frequently reported \u0026lsquo;sleep disturbance\u0026rsquo; and \u0026lsquo;cold sweat\u0026rsquo; were not observed in the cooccurrence network and correlation analysis. One study reported cooccurring symptoms of COVID-19 using social media data [35], but there were no studies on adverse events following COVID-19 vaccination. Through data collected through text, self-reported health problems were segmented, and various health problems were identified for each body organ. It also provides insight into understanding potential adverse reactions.\u003c/p\u003e \u003cp\u003eWhen interpreting the results of this study, in addition to the limitations reported in a previous study [3], the following points need to be kept in mind. Because similar responses were mixed, local, and systemic adverse reactions were not analyzed separately. This may be because the meaning of the question was not conveyed well or due to differences in understanding. Because symptoms are not confirmed according to clinical diagnostic criteria, there may be problems with the accuracy of reported adverse reactions. In addition, with data collected through self-reporting, there may be issues with under-reporting and missing data of adverse reactions. As only text responses are considered, adverse reactions investigated through closed-ended questions are likely to be underestimated. However, most respondents answered closed-ended questions and also responded in text, which would have resulted in redundant reporting. In addition, despite the extensive time devoted to preprocessing for text analysis, the accuracy of the results may have been impacted by factors such as inaccurate answers, responses not related to adverse reactions, and reports of underlying disease history.\u003c/p\u003e \u003cp\u003eNevertheless, we were able to explore in detail potential adverse reactions in addition to those previously reported through text mining analysis. We also explored whether there were differences in adverse reactions reported in text depending on the coadministration of influenza at the same time. This provides a broader understanding of concurrent adverse reactions by visualizing them through continuously reported phi-coefficient analysis and cooccurrence analysis.\u003c/p\u003e \u003cp\u003eCarrying out timely public health responses to protect public health is even more important during a pandemic. The active surveillance system for vaccine safety communicates directly with vaccine recipients and can quickly detect safety issues. However, this presents a realistic barrier to identifying potentially hidden adverse reactions in text-collected data. Therefore, it is necessary to develop a system that can automatically classify data collected as text, and the results of this study can be used to lay the foundation for this.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study explored hidden adverse reactions among the data collected through the active surveillance system for COVID-19 vaccines using a text mining approach. Our results contribute to a better understanding of vaccine adverse reactions and provide the information needed to improve future adverse reaction data management systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Park Hyesook, Lee HA, Park B; Data curation: Kim CH, Kim Y, Park Hyunjin, Jun S, Lee\u0026nbsp;Hyelim; Formal analysis: Lee HA, Park B, Kim CH, Kim Y; Methodology: Lee HA, Park B; Project administration: Park Hyunjin, Jun S, Lee\u0026nbsp;Hyelim, Kwon SL, Heo Y, Lee Hyungmin; Visualization: Lee HA, Park B, Kim CH, Kim Y; Writing - original draft: Lee HA, Park B; Writing - review \u0026amp; editing: all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received financial support from the COVID-19 Vaccination Program (6400-6434-300), which is administered by the Korea Disease Control and Prevention Agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all the participants who took part in the survey for this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDurand J, Dogn\u0026eacute; JM, Cohet C, Browne K, Gordillo-Mara\u0026ntilde;\u0026oacute;n M, Piccolo L, Zaccaria C, Genov G. 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PMID: 36812402; PMCID: PMC10131634.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of study subjects who reported to adverse reactions via text after receiving mRNA COVID-19 vaccine (October 19, 2023 to November 6, 2023, n\u0026thinsp;=\u0026thinsp;1,864)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1864)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOVID-19 vaccine only\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1152, 61.80%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoadministration COVID-19 and influenza vaccines\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;712, 38.20%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1224 (65.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e743 (64.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e481 (67.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e640 (34.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e409 (35.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e231 (32.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68.11 (12.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.17 (12.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.63 (11.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;65 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e360 (19.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e260 (22.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100 (14.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;74 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1019 (54.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e617 (53.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e402 (56.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;75 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e485 (26.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e275 (23.87%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e210 (29.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaccine manufacturer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerna\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1022 (54.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e580 (50.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e442 (62.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePfizer-BioNTech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e842 (45.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e572 (49.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e270 (37.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequency of reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41 (0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39 (0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0 (1.0\u0026ndash;2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0 (1.0\u0026ndash;2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0 (1.0\u0026ndash;1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eSD; standard deviation, IQR; interquartile range\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTerms frequently reported as adverse reactions during the first 7 days following mRNA COVID-19 vaccination (October 19, 2023 to November 6, 2023)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdverse reactions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eVery common\u003c/p\u003e\n \u003cp\u003e(\u0026ge;\u0026thinsp;10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody aches\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFatigue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeadache\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"26\"\u003e\n \u003cp\u003eCommon\u003c/p\u003e\n \u003cp\u003e(\u0026ge;\u0026thinsp;1.0% to \u0026lt;\u0026thinsp;10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDizziness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMyalgia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRunny nose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSore throat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSputum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNausea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrowsiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiarrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArm pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCold sweat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSwelling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShoulder pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChest tightness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChest pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLumbago\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShortness of breath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndigestion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePalpitations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJoint pain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVomiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLoss of appetite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eIf an individual reported the same adverse reaction repeatedly over several days, it was treated as a single event. Percentage (%) was calculated for 1,864 individuals. Gray background indicates newly identified adverse reactions via text analysis.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Adverse reactions, Data mining, Pharmacovigilance, Safety, Vaccination","lastPublishedDoi":"10.21203/rs.3.rs-5315120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5315120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUnstructured text data collected through a surveillance system for vaccine safety monitoring can identify previously unreported adverse reactions and provide the information necessary to improve the surveillance system. Therefore, this study explored adverse reactions using text data gathered through an active surveillance system following monovalent XBB.1.5 COVID-19 vaccination.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA text mining analysis was conducted on 2,608 records from 1,864 individuals who reported any health conditions experienced within 7 days after vaccination in text format. Frequency analysis of key terms was performed, with subsequent analysis by sex, age, and concurrent influenza vaccination. Furthermore, semantic network analysis was conducted on terms reported simultaneously.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe analysis identified various common (\u0026ge;\u0026thinsp;1%) adverse events, such as sleep disturbances, lumbago, and indigestion, which had not been frequently reported in prior literature. Moreover, although not common (\u0026ge;\u0026thinsp;0.1% to \u0026lt;\u0026thinsp;1%), adverse reactions affecting the eyes, ears, and oral cavity were also noted. These adverse reactions showed no significant differences in occurrence with or without simultaneous influenza vaccination. Through cooccurrence analysis and correlation coefficient assessments, associations were found between diarrhea and abdominal pain, as well as between musculoskeletal symptoms and cold-related symptoms.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study used text mining to reveal previously unrecognized adverse reactions related to COVID-19 vaccination, thus expanding our understanding of the vaccine\u0026rsquo;s safety profile. The insights gained could further the scope of future investigations into adverse reactions to vaccines and improve the processing of text data in surveillance systems.\u003c/p\u003e","manuscriptTitle":"Uncovering Adverse reactions following COVID-19 Monovalent XBB.1.5 Vaccination from Active Surveillance: A Text Mining Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-28 16:54:51","doi":"10.21203/rs.3.rs-5315120/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1fc74705-72d3-4e99-82f8-26fa18ce7600","owner":[],"postedDate":"October 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39385803,"name":"Health sciences/Diseases"},{"id":39385804,"name":"Health sciences/Risk factors"},{"id":39385805,"name":"Biological sciences/Drug discovery/Drug safety"}],"tags":[],"updatedAt":"2025-02-06T04:38:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-28 16:54:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5315120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5315120","identity":"rs-5315120","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}