Liang Jingyi, Horvath Daniel, Luz Saturnino, Li You, Nair Harish
Centre for Global Health, Usher Institute, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK.
School of Biomedical Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK.
J Glob Health. 2025 Aug 4;15:04213. doi: 10.7189/jogh.15.04213.
Respiratory viral infections pose a substantial disease burden worldwide. Spatiotemporal techniques help identify transmission patterns of these infections, thereby supporting timely control and prevention efforts. We aimed to synthesise the current state of evidence on quantitative methodologies for investigating the spatiotemporal characteristics of respiratory viral infections.
We conducted a scoping review using the PRISMA-ScR guidelines. We searched three biomedical bibliographic databases, EMBASE, MEDLINE, and Web of Science, identifying studies that analysed spatiotemporal transmission of viral respiratory infectious diseases (published before 1 March 2023).
We identified 8466 articles from database searches, of which 152 met our inclusion criteria and were qualitatively synthesised. Most included articles (n = 140) were published during the COVID-19 pandemic, with 131 articles specifically analysing COVID-19. Exploratory research (n = 77) investigated the spatiotemporal transmission characteristics of respiratory infectious diseases, focussing on transmission patterns (n = 16), and influencing factors (n = 61). Forecasting research (n = 75) aimed to predict the disease trends using either univariate (n = 57) or multivariate models (n = 18), predominantly using machine learning methods (n = 41). The application of advanced deep learning models (n = 20) in disease forecasting analysis was often constrained by the quality of the available disease data.
There is a growing body of research on spatiotemporal analyses of respiratory viral infections, particularly during the COVID-19 pandemic. The acquisition of high-quality data remains important for effectively leveraging sophisticated models in disease forecasting research. Concurrently, although advanced modelling techniques are widely applied, future studies should consider capturing the complex spatiotemporal interactions in disease trajectory modelling.
呼吸道病毒感染在全球范围内造成了巨大的疾病负担。时空技术有助于识别这些感染的传播模式，从而支持及时的控制和预防措施。我们旨在综合目前关于调查呼吸道病毒感染时空特征的定量方法的证据状况。
我们使用PRISMA-ScR指南进行了一项范围综述。我们检索了三个生物医学文献数据库，即EMBASE、MEDLINE和Web of Science，识别分析病毒性呼吸道传染病时空传播的研究（发表于2023年3月1日前）。
我们从数据库检索中识别出8466篇文章，其中152篇符合我们的纳入标准并进行了定性综合分析。大多数纳入文章（n = 140）发表于新冠疫情期间，其中131篇专门分析了新冠病毒。探索性研究（n = 77）调查了呼吸道传染病的时空传播特征，重点关注传播模式（n = 16）和影响因素（n = 61）。预测性研究（n = 75）旨在使用单变量模型（n = 57）或多变量模型（n = 18）预测疾病趋势，主要使用机器学习方法（n = 41）。先进深度学习模型（n = 20）在疾病预测分析中的应用常常受到可用疾病数据质量的限制。
关于呼吸道病毒感染时空分析的研究越来越多，尤其是在新冠疫情期间。获取高质量数据对于在疾病预测研究中有效利用复杂模型仍然很重要。同时，尽管先进的建模技术被广泛应用，但未来的研究应考虑在疾病轨迹建模中捕捉复杂的时空相互作用。
