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

Hassan K Ajulo; Faith O Alele; Theophilus I Emeto; Oyelola A Adegboye

doi:10.1093/epirev/mxaf017

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

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

Hassan K Ajulo, Faith O Alele, Theophilus I Emeto and Oyelola A Adegboye

Epidemiologic Reviews, Vol.47(1), pp.1-20

2025

DOI: https://doi.org/10.1093/epirev/mxaf017

PMID: 41128286

Appears in COVID-19 Research

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Abstract

COVID-19

epidemiology

Spatial

infectious disease

spatiotemporal

coronavirus

machine learning

public health

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 systematically review the application of spatial and spatiotemporal machine learning (ML) models in understanding the dynamics of COVID-19, as well as contextual local-level drivers in demographic, socioeconomic, environmental, epidemiological, healthcare, housing, 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. Data extraction was conducted according to the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist, and study quality was assessed using a validated scoring system. A total of 42 studies met the inclusion criteria. The review findings indicate that global-scale spatial and spatiotemporal ML models dominate the field. Longstanding standalone factors in the demographic, environmental, and socioeconomic, domains are frequently used as local-level drivers. 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 spatial epidemiology 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.

Details

Title: Spatial and spatiotemporal machine learning models for COVID-19 dynamics: A review of methodology and reporting practices
Authors: Hassan K Ajulo - James Cook University
Faith O Alele - University of the Sunshine Coast, Queensland, School of Health - Public Health
Theophilus I Emeto - James Cook University
Oyelola A Adegboye (Corresponding Author) - James Cook University
Publication details: Epidemiologic Reviews, Vol.47(1), pp.1-20
Publisher: Oxford University Press
Date published: 2025
DOI: 10.1093/epirev/mxaf017
ISSN: 1478-6729; 0193-963X
PMID: 41128286
Copyright note: © The Author(s) 2025. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Organisation Unit: School of Health - Public Health
Language: English
Record Identifier: 991179076902621
Output Type: Journal article

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