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Factors such as the high incidence of both emerging and existing infectious diseases, delays in vaccinations, and routine exposure in communal settings heighten children's susceptibility to infections. Despite this pressing need, a comprehensive exploration of research trends in this domain remains lacking. This study aims to address this gap by employing text mining and modeling techniques to conduct a comprehensive analysis of the existing literature, thereby identifying emerging research trends in infectious disease prevention among children.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A cross\u2010sectional text mining approach was adopted, focusing on journal articles published between January 1, 2003, and August 31, 2022. These articles, related to infectious disease prevention in children, were sourced from databases such as PubMed, CINAHL, MEDLINE (Ovid), Scopus, and Korean RISS. The data underwent preprocessing using the Natural Language Toolkit (NLTK) in Python, with a semantic network analysis and topic modeling conducted using R software.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The final dataset comprised 509 journal articles extracted from multiple databases. The study began with a word frequency analysis to pinpoint relevant themes, subsequently visualized through a word cloud. Dominant terms encompassed \u201cvaccination,\u201d \u201cadolescent,\u201d \u201cinfant,\u201d \u201cparent,\u201d \u201cfamily,\u201d \u201cschool,\u201d \u201ccountry,\u201d \u201chousehold,\u201d \u201ccommunity,\u201d \u201cHIV,\u201d \u201cHPV,\u201d \u201cCOVID\u201019,\u201d \u201cinfluenza,\u201d and \u201cdiarrhea.\u201d The semantic analysis identified \u201cage\u201d as a key term across infection, control, and intervention discussions. Notably, the relationship between \u201chand\u201d and \u201chandwashing\u201d was prominent, especially in educational contexts linked with \u201cschool\u201d and \u201cabsence.\u201d Latent Dirichlet Allocation (LDA) topic modeling further delineated seven topics related to infectious disease prevention for children, encompassing (1) educational programs, (2) vaccination efforts, (3) family\u2010level responses, (4) care for immunocompromised individuals, (5) country\u2010specific responses, (6) school\u2010based strategies, and (7) persistent threats from established infectious diseases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The study emphasizes the indispensable role of personalized interventions tailored for various child demographics, highlighting the pivotal contributions of both parental guidance and school participation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Clinical Relevance<\/jats:title><jats:p>The study provides insights into the complex public health challenges associated with preventing and managing infectious diseases in children. The insights derived could inform the formulation of evidence\u2010based public health policies, steering practical interventions and fostering interdisciplinary synergy for holistic prevention strategies.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jnu.12963","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T10:35:28Z","timestamp":1708511728000},"page":"606-620","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Understanding global research trends in the control and prevention of infectious diseases for children: Insights from text mining and topic modeling"],"prefix":"10.1111","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0156-3422","authenticated-orcid":false,"given":"Won\u2010Oak","family":"Oh","sequence":"first","affiliation":[{"name":"College of Nursing, Korea University  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9121-3498","authenticated-orcid":false,"given":"Eunji","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Nursing, Korea University  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7312-0802","authenticated-orcid":false,"given":"Yoo\u2010jin","family":"Heo","sequence":"additional","affiliation":[{"name":"College of Nursing, Korea University  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5599-0705","authenticated-orcid":false,"given":"Myung\u2010Jin","family":"Jung","sequence":"additional","affiliation":[{"name":"College of Nursing, Korea University  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4975-5864","authenticated-orcid":false,"given":"Jihee","family":"Han","sequence":"additional","affiliation":[{"name":"College of Nursing, Korea University  Seoul South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3232939"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.NAJ.0000444496.24228.2c"},{"key":"e_1_2_11_4_1","first-page":"548","article-title":"Survey of Text Mining","volume":"45","author":"Berry M. 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