Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda
BackgroundInfectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data scie...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-03-01
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| Series: | Interactive Journal of Medical Research |
| Online Access: | https://www.i-jmr.org/2023/1/e42292 |
| _version_ | 1797734237745446912 |
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| author | Lateef Babatunde Amusa Hossana Twinomurinzi Edith Phalane Refilwe Nancy Phaswana-Mafuya |
| author_facet | Lateef Babatunde Amusa Hossana Twinomurinzi Edith Phalane Refilwe Nancy Phaswana-Mafuya |
| author_sort | Lateef Babatunde Amusa |
| collection | DOAJ |
| description |
BackgroundInfectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling.
ObjectiveThe aim of this study was to synthesize research and identify hotspots of big data in infectious disease epidemiology.
MethodsBibliometric data from 3054 documents that satisfied the inclusion criteria retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents.
ResultsThe bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. The analysis also placed US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes.
ConclusionsProposals for future studies are made based on these findings. This study will provide health care informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology. |
| first_indexed | 2024-03-12T12:41:23Z |
| format | Article |
| id | doaj.art-6a229274844a40fcbe4a3ced615213ba |
| institution | Directory Open Access Journal |
| issn | 1929-073X |
| language | English |
| last_indexed | 2024-03-12T12:41:23Z |
| publishDate | 2023-03-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Interactive Journal of Medical Research |
| spelling | doaj.art-6a229274844a40fcbe4a3ced615213ba2023-08-28T23:49:53ZengJMIR PublicationsInteractive Journal of Medical Research1929-073X2023-03-0112e4229210.2196/42292Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research AgendaLateef Babatunde Amusahttps://orcid.org/0000-0002-8848-1149Hossana Twinomurinzihttps://orcid.org/0000-0002-9811-3358Edith Phalanehttps://orcid.org/0000-0001-6128-2337Refilwe Nancy Phaswana-Mafuyahttps://orcid.org/0000-0001-9387-0432 BackgroundInfectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling. ObjectiveThe aim of this study was to synthesize research and identify hotspots of big data in infectious disease epidemiology. MethodsBibliometric data from 3054 documents that satisfied the inclusion criteria retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents. ResultsThe bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. The analysis also placed US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes. ConclusionsProposals for future studies are made based on these findings. This study will provide health care informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.https://www.i-jmr.org/2023/1/e42292 |
| spellingShingle | Lateef Babatunde Amusa Hossana Twinomurinzi Edith Phalane Refilwe Nancy Phaswana-Mafuya Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda Interactive Journal of Medical Research |
| title | Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda |
| title_full | Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda |
| title_fullStr | Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda |
| title_full_unstemmed | Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda |
| title_short | Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda |
| title_sort | big data and infectious disease epidemiology bibliometric analysis and research agenda |
| url | https://www.i-jmr.org/2023/1/e42292 |
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