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Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study.

ABSTRACT: BACKGROUND:The COVID-19 pandemic has spread at an alarming speed, and effective treatment for the disease is still lacking. The body of evidence on COVID-19 has been increasing at an impressive pace, creating the need for a method to rapidly assess the current knowledge and identify key information. Gold standard methods such as systematic reviews and meta-analyses are regarded unsuitable because they have a narrow scope and are very time consuming. OBJECTIVE:This study aimed to explore the published scientific literature on COVID-19 and map the research evolution during the early phase of the COVID-19 pandemic. METHODS:We performed a PubMed search to analyze the titles, keywords, and abstracts of published papers on COVID-19. We used latent Dirichlet allocation modeling to extract topics and conducted a trend analysis to understand the temporal changes in research for each topic, journal impact factor (JIF), and geographic origin. RESULTS:Based on our search, we identified 16,670 relevant articles dated between February 14, 2020, and June 1, 2020. Of these, 6 articles were reports from peer-reviewed randomized trials on patients with COVID-19. We identified 14 main research topics, of which the most common topics were health care responses (2812/16,670, 16.86%) and clinical manifestations (1828/16,670, 10.91%). We found an increasing trend for research on clinical manifestations and protective measures and a decreasing trend for research on disease transmission, epidemiology, health care response, and radiology. Publications on protective measures, immunology, and clinical manifestations were associated with the highest JIF. The overall median JIF was 3.7 (IQR 2.6-5.9), and we found that the JIF for these publications declined over time. The top countries producing research were the United States, China, Italy, and the United Kingdom. CONCLUSIONS:In less than 6 months since the novel coronavirus was first detected, a remarkably high number of research articles on COVID-19 have been published. Here, we discuss and present the temporal changes in the available COVID-19 research during the early phase of the pandemic. Our findings may aid researchers and policy makers to form a structured view of the current COVID-19 evidence base and provide further research directions.

SUBMITTER: Alga A

PROVIDER: S-EPMC7674137 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study.

Älgå Andreas A Eriksson Oskar O Nordberg Martin M

Journal of medical Internet research 20201110 11

<h4>Background</h4>The COVID-19 pandemic has spread at an alarming speed, and effective treatment for the disease is still lacking. The body of evidence on COVID-19 has been increasing at an impressive pace, creating the need for a method to rapidly assess the current knowledge and identify key information. Gold standard methods such as systematic reviews and meta-analyses are regarded unsuitable because they have a narrow scope and are very time consuming.<h4>Objective</h4>This study aimed to e ...[more]

PMID: 33031049

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Project description:BackgroundA debate about the scientific quality of COVID-19 themed research has emerged. We explored whether the quality of evidence of COVID-19 publications is lower when compared to nonCOVID-19 publications in the three highest ranked scientific medical journals.MethodsWe searched the PubMed Database from March 12 to April 12, 2020 and identified 559 publications in the New England Journal of Medicine, the Journal of the American Medical Association, and The Lancet which were divided into COVID-19 (cases, n = 204) and nonCOVID-19 (controls, n = 355) associated content. After exclusion of secondary, unauthored, response letters and non-matching article types, 155 COVID-19 publications (including 13 original articles) and 130 nonCOVID-19 publications (including 52 original articles) were included in the comparative analysis. The hierarchical level of evidence was determined for each publication included and compared between cases and controls as the main outcome. A quantitative scoring of quality was carried out for the subgroup of original articles. The numbers of authors and citation rates were also compared between groups.ResultsThe 130 nonCOVID-19 publications were associated with higher levels of evidence on the level of evidence pyramid, with a strong association measure (Cramer's V: 0.452, P <0.001). The 155 COVID-19 publications were 186-fold more likely to be of lower evidence (95% confidence interval [CI] for odds ratio, 7.0-47; P <0.001). The quantitative quality score (maximum possible score, 28) was significantly different in favor of nonCOVID-19 (mean difference, 11.1; 95% CI, 8.5-13.7; P <0.001). There was a significant difference in the early citation rate of the original articles that favored the COVID-19 original articles (median [interquartile range], 45 [30-244] vs. 2 [1-4] citations; P <0.001).ConclusionsWe conclude that the quality of COVID-19 publications in the three highest ranked scientific medical journals is below the quality average of these journals. These findings need to be verified at a later stage of the pandemic.

Project description:BACKGROUND:In December 2019, a few coronavirus disease (COVID-19) cases were first reported in Wuhan, Hubei, China. Soon after, increasing numbers of cases were detected in other parts of China, eventually leading to a disease outbreak in China. As this dreadful disease spreads rapidly, the mass media has been active in community education on COVID-19 by delivering health information about this novel coronavirus, such as its pathogenesis, spread, prevention, and containment. OBJECTIVE:The aim of this study was to collect media reports on COVID-19 and investigate the patterns of media-directed health communications as well as the role of the media in this ongoing COVID-19 crisis in China. METHODS:We adopted the WiseSearch database to extract related news articles about the coronavirus from major press media between January 1, 2020, and February 20, 2020. We then sorted and analyzed the data using Python software and Python package Jieba. We sought a suitable topic number with evidence of the coherence number. We operated latent Dirichlet allocation topic modeling with a suitable topic number and generated corresponding keywords and topic names. We then divided these topics into different themes by plotting them into a 2D plane via multidimensional scaling. RESULTS:After removing duplications and irrelevant reports, our search identified 7791 relevant news reports. We listed the number of articles published per day. According to the coherence value, we chose 20 as the number of topics and generated the topics' themes and keywords. These topics were categorized into nine main primary themes based on the topic visualization figure. The top three most popular themes were prevention and control procedures, medical treatment and research, and global or local social and economic influences, accounting for 32.57% (n=2538), 16.08% (n=1258), and 11.79% (n=919) of the collected reports, respectively. CONCLUSIONS:Topic modeling of news articles can produce useful information about the significance of mass media for early health communication. Comparing the number of articles for each day and the outbreak development, we noted that mass media news reports in China lagged behind the development of COVID-19. The major themes accounted for around half the content and tended to focus on the larger society rather than on individuals. The COVID-19 crisis has become a worldwide issue, and society has become concerned about donations and support as well as mental health among others. We recommend that future work addresses the mass media's actual impact on readers during the COVID-19 crisis through sentiment analysis of news data.

Project description:BackgroundThe COVID-19 pandemic is favoring digital transitions in many industries and in society as a whole. Health care organizations have responded to the first phase of the pandemic by rapidly adopting digital solutions and advanced technology tools.ObjectiveThe aim of this review is to describe the digital solutions that have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems.MethodsWe conducted a systematic review of early COVID-19-related literature (from January 1 to April 30, 2020) by searching MEDLINE and medRxiv with appropriate terms to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as the paper title, journal, and publication date, and we categorized the retrieved papers by the type of technology and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to health care system target, grade of innovation, and scalability to other geographical areas.ResultsThe search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Most of the selected articles addressed the use of digital technologies for diagnosis, surveillance, and prevention. We report that most of these digital solutions and innovative technologies have been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles, we identified numerous suggestions on the use of artificial intelligence (AI)-powered tools for the diagnosis and screening of COVID-19. Digital technologies are also useful for prevention and surveillance measures, such as contact-tracing apps and monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement.ConclusionsIn the field of diagnosis, digital solutions that integrate with traditional methods, such as AI-based diagnostic algorithms based both on imaging and clinical data, appear to be promising. For surveillance, digital apps have already proven their effectiveness; however, problems related to privacy and usability remain. For other patient needs, several solutions have been proposed, such as telemedicine or telehealth tools. These tools have long been available, but this historical moment may actually be favoring their definitive large-scale adoption. It is worth taking advantage of the impetus provided by the crisis; it is also important to keep track of the digital solutions currently being proposed to implement best practices and models of care in future and to adopt at least some of the solutions proposed in the scientific literature, especially in national health systems, which have proved to be particularly resistant to the digital transition in recent years.

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Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study.

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Analysis of Scientific Publications During the Early Phase of the COVID-19 Pandemic: Topic Modeling Study.

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