Jing Fengrui, Li Zhenlong, Qiao Shan, Zhang Jiajia, Olatosi Banky, Li Xiaoming
Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA.
Big Data Health Science Center, University of South Carolina, Columbia, SC, USA.
Int J Digit Earth. 2023;16(1):130-157. doi: 10.1080/17538947.2022.2161652. Epub 2023 Jan 3.
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 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.
地理空间社交媒体（GSM）数据因其丰富、及时且可获取的空间信息，在公共卫生领域，尤其是传染病研究中得到了越来越广泛的应用。本综述综合了2013年12月至2022年3月期间发表的86篇使用GSM数据进行传染病研究的文章。这些文章涵盖了12种传染病类型，从呼吸道传染病到性传播疾病，空间层面包括社区、县、州和国家。我们将这些研究分为三个主要的传染病研究领域：监测、解释和预测。借助先进的统计和空间方法，GSM数据已广泛且深入地应用于这些领域，尤其是在监测和解释领域。我们进一步从背景信息使用、应用范围、时空维度和数据局限性方面识别出四个知识空白，并为未来研究提出了创新机会。我们的研究结果将有助于更好地理解在传染病研究中使用GSM数据，并为未来研究更有效地使用GSM数据提供策略见解。
