ArticleJournal of global health2025
Respiratory viral infections: when and where? A scoping review of spatiotemporal methods.
Article in Journal of global health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Spatial, temporal, and spatiotemporal analysis of pertussis in Shapingba District, China, 2014-2024.BMC infectious diseases · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: 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. Methods: 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). Results: 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. Conclusions: 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.
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Registered trials
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