tetano
Editor, Senior Moderator
Epidemiol Rev
. 2025 Oct 23:mxaf017.
doi: 10.1093/epirev/mxaf017. Online ahead of print. Spatial and spatiotemporal machine learning models for COVID-19 dynamics: A review of methodology and reporting practices
Hassan K Ajulo[SUP] 1 [/SUP], Faith O Alele[SUP] 2 [/SUP], Theophilus I Emeto[SUP] 1 [/SUP], Oyelola A Adegboye[SUP] 1 3 [/SUP]
Affiliations
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.
Keywords: COVID-19; Spatial; coronavirus; epidemiology; infectious disease; machine learning; public health; spatiotemporal.
. 2025 Oct 23:mxaf017.
doi: 10.1093/epirev/mxaf017. Online ahead of print. Spatial and spatiotemporal machine learning models for COVID-19 dynamics: A review of methodology and reporting practices
Hassan K Ajulo[SUP] 1 [/SUP], Faith O Alele[SUP] 2 [/SUP], Theophilus I Emeto[SUP] 1 [/SUP], Oyelola A Adegboye[SUP] 1 3 [/SUP]
Affiliations
- PMID: 41128286
- DOI: 10.1093/epirev/mxaf017
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.
Keywords: COVID-19; Spatial; coronavirus; epidemiology; infectious disease; machine learning; public health; spatiotemporal.