Respiratory Viral Infections: When and Where? A scoping review of spatiotemporal methods

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 synthesize the current state of evidence on quantitative methodologies for investigating the spatiotemporal characteristics of respiratory viral infections.
Methods: We conducted a scoping review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review (PRISMA-SCR) guidelines. We searched three biomedical bibliographic databases, EMBASE, MEDLINE, and Web of Science, identifying studies that analyzed spatiotemporal transmission of viral respiratory infectious diseases (published before March 1, 2023).
Results: We identified 8466 articles from database searches, of which 152 met our inclusion criteria and were qualitatively synthesized. Most included articles (n = 140) were published during the COVID-19 pandemic, with 131 articles specifically analyzing COVID-19. Exploratory research (n = 77) investigated the spatiotemporal transmission characteristics of respiratory infectious diseases, focusing 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 utilizing 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 modeling techniques are widely applied, future studies should consider capturing the complex spatiotemporal interactions in disease trajectory modeling.

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DOI

https://doi.org/10.7189/jogh.15.04213

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https://www.research.ed.ac.uk/en/publications/3292718e-ba5f-4022-83c2-6e88b3512085