Predicting infectious diseases: a bibliometric review on Africa
Africa has a long history of novel and re-emerging infectious disease outbreaks. This reality has attracted the attention of researchers interested in the general research theme of predicting infectious diseases. However, a knowledge mapping analysis of literature to reveal the research trends, gaps...
| Autores principales: | , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2022
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| Acceso en línea: | https://hdl.handle.net/10568/118281 |
| _version_ | 1855514496812974080 |
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| author | Phoobane, P. Masinde, M. Mabhaudhi, Tafadzwanashe |
| author_browse | Mabhaudhi, Tafadzwanashe Masinde, M. Phoobane, P. |
| author_facet | Phoobane, P. Masinde, M. Mabhaudhi, Tafadzwanashe |
| author_sort | Phoobane, P. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Africa has a long history of novel and re-emerging infectious disease outbreaks. This reality has attracted the attention of researchers interested in the general research theme of predicting infectious diseases. However, a knowledge mapping analysis of literature to reveal the research trends, gaps, and hotspots in predicting Africa’s infectious diseases using bibliometric tools has not been conducted. A bibliometric analysis of 247 published papers on predicting infectious diseases in Africa, published in the Web of Science core collection databases, is presented in this study. The results indicate that the severe outbreaks of infectious diseases in Africa have increased scientific publications during the past decade. The results also reveal that African researchers are highly underrepresented in these publications and that the United States of America (USA) is the most productive and collaborative country. The relevant hotspots in this research field include malaria, models, classification, associations, COVID-19, and cost-effectiveness. Furthermore, weather-based prediction using meteorological factors is an emerging theme, and very few studies have used the fourth industrial revolution (4IR) technologies. Therefore, there is a need to explore 4IR predicting tools such as machine learning and consider integrated approaches that are pivotal to developing robust prediction systems for infectious diseases, especially in Africa. This review paper provides a useful resource for researchers, practitioners, and research funding agencies interested in the research theme—the prediction of infectious diseases in Africa—by capturing the current research hotspots and trends. |
| format | Journal Article |
| id | CGSpace118281 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1182812025-12-08T10:29:22Z Predicting infectious diseases: a bibliometric review on Africa Phoobane, P. Masinde, M. Mabhaudhi, Tafadzwanashe infectious diseases prediction bibliometric analysis machine learning artificial intelligence malaria covid-19 ebola virus disease collaboration models Africa has a long history of novel and re-emerging infectious disease outbreaks. This reality has attracted the attention of researchers interested in the general research theme of predicting infectious diseases. However, a knowledge mapping analysis of literature to reveal the research trends, gaps, and hotspots in predicting Africa’s infectious diseases using bibliometric tools has not been conducted. A bibliometric analysis of 247 published papers on predicting infectious diseases in Africa, published in the Web of Science core collection databases, is presented in this study. The results indicate that the severe outbreaks of infectious diseases in Africa have increased scientific publications during the past decade. The results also reveal that African researchers are highly underrepresented in these publications and that the United States of America (USA) is the most productive and collaborative country. The relevant hotspots in this research field include malaria, models, classification, associations, COVID-19, and cost-effectiveness. Furthermore, weather-based prediction using meteorological factors is an emerging theme, and very few studies have used the fourth industrial revolution (4IR) technologies. Therefore, there is a need to explore 4IR predicting tools such as machine learning and consider integrated approaches that are pivotal to developing robust prediction systems for infectious diseases, especially in Africa. This review paper provides a useful resource for researchers, practitioners, and research funding agencies interested in the research theme—the prediction of infectious diseases in Africa—by capturing the current research hotspots and trends. 2022-02-08 2022-02-28T21:14:23Z 2022-02-28T21:14:23Z Journal Article https://hdl.handle.net/10568/118281 en Open Access MDPI Phoobane, P.; Masinde, M.; Mabhaudhi, Tafadzwanashe. 2022. Predicting infectious diseases: a bibliometric review on Africa. International Journal of Environmental Research and Public Health, 19(3):1893. [doi: https://doi.org/10.3390/ijerph19031893] |
| spellingShingle | infectious diseases prediction bibliometric analysis machine learning artificial intelligence malaria covid-19 ebola virus disease collaboration models Phoobane, P. Masinde, M. Mabhaudhi, Tafadzwanashe Predicting infectious diseases: a bibliometric review on Africa |
| title | Predicting infectious diseases: a bibliometric review on Africa |
| title_full | Predicting infectious diseases: a bibliometric review on Africa |
| title_fullStr | Predicting infectious diseases: a bibliometric review on Africa |
| title_full_unstemmed | Predicting infectious diseases: a bibliometric review on Africa |
| title_short | Predicting infectious diseases: a bibliometric review on Africa |
| title_sort | predicting infectious diseases a bibliometric review on africa |
| topic | infectious diseases prediction bibliometric analysis machine learning artificial intelligence malaria covid-19 ebola virus disease collaboration models |
| url | https://hdl.handle.net/10568/118281 |
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