Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.

ABSTRACT: COVID-19 has transitioned from a pandemic to an endemic state, but the emergence of novel variants continues to pose significant public health challenges. In this study, the application of spatial and spatiotemporal machine learning (ML) models in understanding the dynamics of COVID-19 was systematically reviewed, as were contextual local-level comprehensive socio-environmental drivers. A systematic search was conducted across the Scopus, Web of Science, PubMed, Emcare (via Ovid), and the World Health Organization COVID-19 databases, and gray literature, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Data extraction was conducted according to the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies checklis

SUBMITTER: Ajulo HK 

PROVIDER: S-EPMC12699224 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature