Spatial and spatiotemporal machine learning models for studying 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. This study aims to review the application of spatial and spatiotemporal machine learning (ML) models in understanding the dynamics of COVID-19, as well as some contextual local-level drivers in the demographic, socio-economic, environmental, epidemiological, healthcare, housing conditions, behavioural, vaccination, governmental policy, and mobility domains. A systematic search was conducted across Scopus, Web of Science, PubMed, Emcare (via Ovid), the WHO COVID-19 database, and grey literature, adhering to PRISMA guidelines. A total of 42 studies met the inclusion criteria. The review findings indicate that global-scale spatial and spatiotemporal ML models dominate the field. Long standing standalone factors in the demographic, environmental, and socio-economic domains are frequently used as local-level drivers. In addition, government policy and mobility data have recently gained prominence through advances in data integration, enabling more dynamic modelling of disease spread. However, the integration of composite indicators, aggregating multiple standalone factors into a single score, is notably lacking. Such composite indicators have the potential to reduce model complexity, improve interpretability, and enhance performance by capturing multidimensional aspects of vulnerability or risk in a more simplified form. This review highlights critical gaps in the current use of spatial and spatiotemporal ML models to understand the dynamics of COVID-19. Addressing these gaps could significantly enhance the understanding of COVID-19 dynamics and inform the development of effective public health strategies to mitigate future threats.

Keywords: Spatial, spatiotemporal, machine learning, coronavirus, COVID-19, infectious disease, epidemiology, public health