Using geospatial social media data for infectious disease studies: a systematic review
Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 20...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2022.2161652 |
| _version_ | 1797678478119665664 |
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| author | Fengrui Jing Zhenlong Li Shan Qiao Jiajia Zhang Banky Olatosi Xiaoming Li |
| author_facet | Fengrui Jing Zhenlong Li Shan Qiao Jiajia Zhang Banky Olatosi Xiaoming Li |
| author_sort | Fengrui Jing |
| collection | DOAJ |
| description | Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced computing, statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research. |
| first_indexed | 2024-03-11T23:00:20Z |
| format | Article |
| id | doaj.art-3b7cb13e51a2445890f9aad85e190bdb |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:20Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-3b7cb13e51a2445890f9aad85e190bdb2023-09-21T14:57:12ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552023-12-0116113015710.1080/17538947.2022.21616522161652Using geospatial social media data for infectious disease studies: a systematic reviewFengrui Jing0Zhenlong Li1Shan Qiao2Jiajia Zhang3Banky Olatosi4Xiaoming Li5Geoinformation and Big Data Research Laboratory, Department of Geography, University of South CarolinaGeoinformation and Big Data Research Laboratory, Department of Geography, University of South CarolinaBig Data Health Science Center, University of South Carolina, ColumbiaBig Data Health Science Center, University of South Carolina, ColumbiaBig Data Health Science Center, University of South Carolina, ColumbiaBig Data Health Science Center, University of South Carolina, ColumbiaGeospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced computing, statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.http://dx.doi.org/10.1080/17538947.2022.2161652public healthsocial mediainfectious diseasesgeographyspatial analysis |
| spellingShingle | Fengrui Jing Zhenlong Li Shan Qiao Jiajia Zhang Banky Olatosi Xiaoming Li Using geospatial social media data for infectious disease studies: a systematic review International Journal of Digital Earth public health social media infectious diseases geography spatial analysis |
| title | Using geospatial social media data for infectious disease studies: a systematic review |
| title_full | Using geospatial social media data for infectious disease studies: a systematic review |
| title_fullStr | Using geospatial social media data for infectious disease studies: a systematic review |
| title_full_unstemmed | Using geospatial social media data for infectious disease studies: a systematic review |
| title_short | Using geospatial social media data for infectious disease studies: a systematic review |
| title_sort | using geospatial social media data for infectious disease studies a systematic review |
| topic | public health social media infectious diseases geography spatial analysis |
| url | http://dx.doi.org/10.1080/17538947.2022.2161652 |
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