{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T16:48:46Z","timestamp":1781974126030,"version":"3.54.5"},"reference-count":89,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T00:00:00Z","timestamp":1761177600000},"content-version":"vor","delay-in-days":295,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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 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. Long-standing 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.<\/jats:p>","DOI":"10.1093\/epirev\/mxaf017","type":"journal-article","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T11:55:39Z","timestamp":1761134139000},"source":"Crossref","is-referenced-by-count":3,"title":["Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices"],"prefix":"10.1093","volume":"47","author":[{"given":"Hassan K","family":"Ajulo","sequence":"first","affiliation":[{"name":"Public Health and Tropical Medicine, James Cook University , Townsville, QLD ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faith O","family":"Alele","sequence":"additional","affiliation":[{"name":"School of Health, University of the Sunshine Coast , Sippy Downs, QLD ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3282-1861","authenticated-orcid":false,"given":"Theophilus I","family":"Emeto","sequence":"additional","affiliation":[{"name":"Public Health and Tropical Medicine, James Cook University , Townsville, QLD ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9793-8024","authenticated-orcid":false,"given":"Oyelola A","family":"Adegboye","sequence":"additional","affiliation":[{"name":"Public Health and Tropical Medicine, James Cook University , Townsville, QLD ,","place":["Australia"]},{"name":"Menzies School of Health Research, Charles Darwin University , Darwin, NT ,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"issue":"1","key":"2025121203563514500_ref1","doi-asserted-by":"publisher","DOI":"10.1111\/cbdd.14383","article-title":"Discovery and development of COVID-19 vaccine from laboratory to clinic","volume":"103","author":"Saravanan","year":"2024","journal-title":"Chem Biol Drug Des."},{"issue":"18","key":"2025121203563514500_ref2","doi-asserted-by":"publisher","first-page":"15313","DOI":"10.1007\/s00521-022-07424-w","article-title":"Machine learning applications for COVID-19 outbreak management","volume":"34","author":"Heidari","year":"2022","journal-title":"Neural Comput Appl."},{"key":"2025121203563514500_ref3","author":"Worldometer"},{"issue":"2","key":"2025121203563514500_ref4","doi-asserted-by":"publisher","DOI":"10.1016\/S2665-9913(24)00005-5","article-title":"optimising COVID-19 immunity","volume":"6","author":"Pandemic to endemic","year":"2024","journal-title":"Lancet Rheumatol."},{"key":"2025121203563514500_ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.sste.2023.100623","article-title":"Examining associations between social vulnerability indices and COVID-19 incidence and mortality with spatial-temporal Bayesian modeling","volume":"48","author":"Johnson","year":"2024","journal-title":"Spatial Spatiotemporal Epidemiol."},{"issue":"1","key":"2025121203563514500_ref6","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2022-067134","article-title":"Spatial and spatio-temporal epidemiological approaches to inform COVID-19 surveillance and control: a systematic review of statistical and modelling methods in Africa","volume":"13","author":"Odhiambo","year":"2023","journal-title":"BMJ Open."},{"issue":"14","key":"2025121203563514500_ref7","doi-asserted-by":"publisher","first-page":"8267","DOI":"10.3390\/ijerph19148267","article-title":"Methods used in the spatial and spatiotemporal analysis of COVID-19 epidemiology: a systematic review","volume":"19","author":"Nazia","year":"2022","journal-title":"Int J Environ Res Public Health."},{"key":"2025121203563514500_ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2020.140033","article-title":"Spatial analysis and GIS in the study of COVID-19","volume":"739","author":"Franch-Pardo","year":"2020","journal-title":"A review. Sci Total Environ."},{"key":"2025121203563514500_ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.mex.2024.102639","article-title":"Analyzing COVID-19 outbreaks: a methodological approach with Gaussian pulse models","volume":"12","author":"Setianto","year":"2024","journal-title":"MethodsX."},{"issue":"5","key":"2025121203563514500_ref10","doi-asserted-by":"publisher","first-page":"2191","DOI":"10.1111\/tgis.12792","article-title":"A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020","volume":"25","author":"Franch-Pardo","year":"2021","journal-title":"Transactions GIS."},{"key":"2025121203563514500_ref11","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.annepidem.2020.07.014","article-title":"A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?","volume":"52","author":"Sun","year":"2020","journal-title":"Ann Epidemiol."},{"issue":"4","key":"2025121203563514500_ref12","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1111\/jrh.12486","article-title":"Progression of COVID-19 from urban to rural areas in the United States: a spatiotemporal analysis of prevalence rates","volume":"36","author":"Paul","year":"2020","journal-title":"J Rural Health."},{"key":"2025121203563514500_ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2021.145992","article-title":"The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level","volume":"777","author":"Liu","year":"2021","journal-title":"Sci Total Environ."},{"issue":"5","key":"2025121203563514500_ref14","doi-asserted-by":"publisher","DOI":"10.1029\/2021GH000402","article-title":"Modeling the spatiotemporal association between COVID-19 transmission and population mobility using geographically and temporally weighted regression","volume":"5","author":"Chen","year":"2021","journal-title":"GeoHealth."},{"issue":"7","key":"2025121203563514500_ref15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0010594","article-title":"Linear and machine learning modelling for spatiotemporal disease predictions: force-of-infection of Chagas disease","volume":"16","author":"Ledien","year":"2022","journal-title":"PLoS Negl Trop Dis."},{"issue":"9","key":"2025121203563514500_ref16","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1017\/S0033291719000151","article-title":"Machine learning in mental health: a scoping review of methods and applications","volume":"49","author":"Shatte","year":"2019","journal-title":"Psychol Med."},{"issue":"4","key":"2025121203563514500_ref17","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/nmeth.4642","article-title":"Statistics versus machine learning","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nat Methods."},{"key":"2025121203563514500_ref18","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-59216-4","volume-title":"International Conference on Biomedical and Health Informatics 2022","author":"Pino","year":"2024"},{"key":"2025121203563514500_ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICBK50248.2020.00030","volume-title":"2020 IEEE International Conference on Knowledge Graph (ICKG)","author":"Das Gupta","year":"2020"},{"issue":"6245","key":"2025121203563514500_ref20","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science."},{"issue":"4","key":"2025121203563514500_ref21","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s10109-024-00452-1","article-title":"Introduction to the special issue on spatial machine learning","volume":"26","author":"Credit","year":"2024","journal-title":"J Geographical Syst."},{"key":"2025121203563514500_ref22","doi-asserted-by":"publisher","first-page":"7196492","DOI":"10.1155\/2021\/7196492","article-title":"Spatial prediction of COVID-19 in China based on machine learning algorithms and geographically weighted regression","volume":"2021","author":"Shao","year":"2021","journal-title":"Comput Math Methods Med."},{"key":"2025121203563514500_ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.sste.2021.100471","article-title":"Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms","volume":"40","author":"Kianfar","year":"2022","journal-title":"Spat Spatiotemporal Epidemiol."},{"key":"2025121203563514500_ref24","doi-asserted-by":"publisher","first-page":"1277052","DOI":"10.3389\/fphy.2023.1277052","article-title":"Forecasting infections with spatio-temporal graph neural networks: a case study of the Dutch SARS-CoV-2 spread","volume":"11","author":"Croft","year":"2023","journal-title":"Front Phys."},{"key":"2025121203563514500_ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.healthplace.2022.102744","article-title":"Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: a geographical random forest approach","volume":"74","author":"Grekousis","year":"2022","journal-title":"Health Place."},{"key":"2025121203563514500_ref26","doi-asserted-by":"publisher","first-page":"article 465","DOI":"10.1007\/s42979-021-00864-6","article-title":"Explaining causal influence of external factors on incidence rate of Covid-19","volume":"2","author":"Paul","year":"2021","journal-title":"SN Computer Science"},{"key":"2025121203563514500_ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2020.109917","article-title":"Analysis of spatial spread relationships of coronavirus (COVID-19) pandemic in the world using self organizing maps","volume":"138","author":"Melin","year":"2020","journal-title":"Chaos Solitons Fractals."},{"key":"2025121203563514500_ref28","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.911336","article-title":"A graph convolutional network for predicting COVID-19 dynamics in 190 regions\/countries","volume":"10","author":"Anno","year":"2022","journal-title":"Front Public Health."},{"issue":"2","key":"2025121203563514500_ref29","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1007\/s41060-024-00525-w","article-title":"A probabilistic spatio-temporal neural network to forecast COVID-19 counts","volume":"20","author":"Ravenda","year":"2024","journal-title":"Int J Data Sci Analytics."},{"issue":"6","key":"2025121203563514500_ref30","doi-asserted-by":"publisher","first-page":"6587","DOI":"10.1007\/s11356-020-10962-2","article-title":"Distribution of the environmental and socioeconomic risk factors on COVID-19 death rate across continental USA: a spatial nonlinear analysis","volume":"28","author":"Luo","year":"2021","journal-title":"Environ Sci Pollut Res Int."},{"key":"2025121203563514500_ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3550272","article-title":"Modeling the geospatial evolution of COVID-19 using spatio-temporal convolutional sequence-to-sequence neural networks","volume":"8","author":"Cardoso","year":"2022","journal-title":"ACM Trans Spatial Algorithms Syst."},{"key":"2025121203563514500_ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107469","article-title":"Learning where to look for COVID-19 growth: multivariate analysis of COVID-19 cases over time using explainable convolution-LSTM","volume":"109","author":"Yudistira","year":"2021","journal-title":"Appl Soft Comput."},{"key":"2025121203563514500_ref33","doi-asserted-by":"publisher","first-page":"670","DOI":"10.1109\/TSIPN.2022.3193252","article-title":"Analysis of the spatio-temporal dynamics of COVID-19 in Massachusetts via spectral graph wavelet theory","volume":"8","author":"Geng","year":"2022","journal-title":"IEEE Trans Signal Inform Process Over Netws."},{"issue":"5","key":"2025121203563514500_ref34","doi-asserted-by":"publisher","first-page":"2336","DOI":"10.3390\/ijerph18052336","article-title":"Geospatial analysis of COVID-19: a scoping review","volume":"18","author":"Fatima","year":"2021","journal-title":"Int J Environ Res Public Health."},{"issue":"12","key":"2025121203563514500_ref35","doi-asserted-by":"publisher","first-page":"15","DOI":"10.4081\/jphia.2023.2767","article-title":"Mapping the pandemic: a review of geographical information systems-based spatial modeling of Covid-19","volume":"14","author":"Aboalyem","year":"2023","journal-title":"J Public Health Afr."},{"key":"2025121203563514500_ref36","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.12688\/f1000research.27544.2","article-title":"Applications of GIS and geospatial analyses in COVID-19 research: a systematic review","volume":"9","author":"Ahasan","year":"2020","journal-title":"F1000Res."},{"key":"2025121203563514500_ref37","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.b2700","article-title":"The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration","volume":"339","author":"Liberati","year":"2009","journal-title":"BMJ."},{"issue":"7","key":"2025121203563514500_ref38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1000097","article-title":"Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement","volume":"6","author":"Moher","year":"2009","journal-title":"PLoS Med."},{"issue":"1","key":"2025121203563514500_ref39","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1186\/s13643-016-0384-4","article-title":"Rayyan\u2014a web and mobile app for systematic reviews","volume":"5","author":"Ouzzani","year":"2016","journal-title":"Syst Rev."},{"issue":"9","key":"2025121203563514500_ref40","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1109\/TKDE.2018.2866809","article-title":"A survey on spatial prediction methods","volume":"31","author":"Jiang","year":"2019","journal-title":"IEEE Trans Knowl Data Eng."},{"issue":"10","key":"2025121203563514500_ref41","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001744","article-title":"Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS Checklist","volume":"11","author":"Moons","year":"2014","journal-title":"PLoS Med."},{"key":"2025121203563514500_ref42","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1109\/ojemb.2021.3063890","article-title":"Interaction-temporal GCN: a hybrid deep framework for Covid-19 pandemic analysis","volume":"2","author":"Yu","year":"2021","journal-title":"IEEE Open J Eng Med Biol."},{"issue":"9","key":"2025121203563514500_ref43","doi-asserted-by":"publisher","first-page":"5437","DOI":"10.1007\/s00500-022-07778-2","article-title":"Predicting COVID-19 using lioness optimization algorithm and graph convolution network","volume":"27","author":"Li","year":"2023","journal-title":"Soft Comput."},{"issue":"3","key":"2025121203563514500_ref44","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1049\/cit2.12238","article-title":"Dynamic adaptive spatio temporal graph network for COVID-19 forecasting","volume":"9","author":"Pu","year":"2023","journal-title":"CAAI Trans Intell Technol."},{"issue":"6","key":"2025121203563514500_ref45","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1007\/s40031-020-00517-x","article-title":"A multivariate spatiotemporal model of COVID-19 epidemic using ensemble of ConvLSTM networks","volume":"102","author":"Paul","year":"2020","journal-title":"J Inst Eng India Series B."},{"issue":"15","key":"2025121203563514500_ref46","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3390\/su13158295","article-title":"Spatial and temporal spread of the COVID-19 pandemic using self organizing neural networks and a fuzzy fractal approach","volume":"13","author":"Melin","year":"2021","journal-title":"Sustainability."},{"key":"2025121203563514500_ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.smhl.2022.100348","article-title":"Spatial temporal synchronous graph transformer network (STSGT) for COVID-19 forecasting","volume":"26","author":"Banerjee","year":"2022","journal-title":"Smart Health."},{"issue":"1","key":"2025121203563514500_ref48","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1109\/TBDATA.2020.3032755","article-title":"An epidemiological neural network exploiting dynamic graph structured data applied to the COVID-19 outbreak","volume":"7","author":"La Gatta","year":"2020","journal-title":"IEEE Trans Big Data."},{"key":"2025121203563514500_ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.rinp.2022.105374","article-title":"Borough-level COVID-19 forecasting in London using deep learning techniques and a novel MSE-Moran's I loss function","volume":"35","author":"Olsen","year":"2022","journal-title":"Results Phys."},{"issue":"1","key":"2025121203563514500_ref50","doi-asserted-by":"publisher","first-page":"3930","DOI":"10.1038\/s41598-022-07757-5","article-title":"Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany","volume":"12","author":"Fritz","year":"2022","journal-title":"Sci Rep."},{"key":"2025121203563514500_ref51","doi-asserted-by":"publisher","first-page":"85064","DOI":"10.1109\/ACCESS.2022.3195535","article-title":"Forecasting the COVID-19 space-time dynamics in Brazil with convolutional graph neural networks and transport modals","volume":"10","author":"Oliveira","year":"2022","journal-title":"IEEE Access."},{"key":"2025121203563514500_ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2022.112996","article-title":"A new method for spatio-temporal transmission prediction of COVID-19","volume":"167","author":"Wang","year":"2023","journal-title":"Chaos Solitons Fractals."},{"key":"2025121203563514500_ref53","doi-asserted-by":"publisher","first-page":"159915","DOI":"10.1109\/access.2020.3019989","article-title":"DeepCOVIDNet: an interpretable deep learning model for predictive surveillance of COVID-19 using heterogeneous features and their interactions","volume":"8","author":"Ramchandani","year":"2020","journal-title":"IEEE Access."},{"issue":"4","key":"2025121203563514500_ref54","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1093\/jamia\/ocaa322","article-title":"STAN: spatio-temporal attention network for pandemic prediction using real-world evidence","volume":"28","author":"Gao","year":"2021","journal-title":"J Am Med Inform Assoc."},{"issue":"1","key":"2025121203563514500_ref55","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1080\/01969722.2022.2103614","article-title":"Fine-grained population mobility data-based community-level COVID-19 prediction model","volume":"55","author":"Jia","year":"2022","journal-title":"Cybernet Syst."},{"issue":"1","key":"2025121203563514500_ref56","doi-asserted-by":"publisher","first-page":"5235","DOI":"10.1038\/s41598-023-31222-6","article-title":"Predicting COVID-19 positivity and hospitalization with multi-scale graph neural networks","volume":"13","author":"Skianis","year":"2023","journal-title":"Sci Rep."},{"issue":"8","key":"2025121203563514500_ref57","doi-asserted-by":"publisher","first-page":"2265","DOI":"10.1007\/s00477-021-02168-w","article-title":"A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases","volume":"36","author":"Niraula","year":"2022","journal-title":"Stoch Environ Res Risk Assess."},{"issue":"6","key":"2025121203563514500_ref58","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.1111\/tgis.12803","article-title":"Understanding and predicting the spatio-temporal spread of COVID-19 via integrating diffusive graph embedding and compartmental models","volume":"25","author":"Zhang","year":"2021","journal-title":"Trans GIS."},{"key":"2025121203563514500_ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104132","article-title":"A deep learning approach for spatio-temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia","volume":"132","author":"Sciannameo","year":"2022","journal-title":"Northern Italy. J Biomed Inform."},{"issue":"1","key":"2025121203563514500_ref60","doi-asserted-by":"publisher","first-page":"2055989","DOI":"10.1080\/08839514.2022.2055989","article-title":"Using a layered ensemble of physics-guided graph attention networks to predict COVID-19 trends","volume":"36","author":"Sun","year":"2022","journal-title":"Appl Artif Intell."},{"key":"2025121203563514500_ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.seps.2020.100976","article-title":"Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019","volume":"80","author":"Huang","year":"2022","journal-title":"Socioecon Plann Sci."},{"issue":"6","key":"2025121203563514500_ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e17625","article-title":"A new RNN based machine learning model to forecast COVID-19 incidence, enhanced by the use of mobility data from the bike-sharing service in Madrid","volume":"9","author":"Mu\u00f1oz-Organero","year":"2023","journal-title":"Heliyon."},{"issue":"9","key":"2025121203563514500_ref63","doi-asserted-by":"publisher","first-page":"3519","DOI":"10.3390\/s22093519","article-title":"Deep spatiotemporal model for COVID-19 forecasting","volume":"22","author":"Mu\u00f1oz-Organero","year":"2022","journal-title":"Sensors (Basel)."},{"issue":"3","key":"2025121203563514500_ref64","doi-asserted-by":"publisher","first-page":"235","DOI":"10.3390\/medicina57030235","article-title":"The spread of the COVID-19 outbreak in Brazil: an overview by Kohonen self-organizing map networks","volume":"57","author":"Galvan","year":"2021","journal-title":"Medicina (Kaunas)."},{"issue":"1","key":"2025121203563514500_ref65","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s11004-023-10096-4","article-title":"A stochastic model of an early warning system for detecting anomalous incidence values of COVID-19","volume":"56","author":"Duarte","year":"2023","journal-title":"Math Geosci."},{"issue":"1","key":"2025121203563514500_ref66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/4307708","article-title":"Space-distributed traffic-enhanced LSTM-based machine learning model for COVID-19 incidence forecasting","volume":"2022","author":"Mu\u00f1oz-Organero","year":"2022","journal-title":"Comput Intell Neurosci."},{"issue":"3","key":"2025121203563514500_ref67","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac0314","article-title":"Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk","volume":"2","author":"Wang","year":"2021","journal-title":"Mach Learn Sci Technol."},{"issue":"21","key":"2025121203563514500_ref68","doi-asserted-by":"publisher","first-page":"15953","DOI":"10.1109\/jiot.2021.3070042","article-title":"Toward combatting COVID-19: a risk assessment system","volume":"8","author":"Wang","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"2025121203563514500_ref69","doi-asserted-by":"publisher","DOI":"10.1162\/99608f92.79e1f45e","article-title":"A spatiotemporal epidemiological prediction model to inform county-level COVID-19 risk in the United States","author":"Zhou","year":"2020","journal-title":"Harvard Data Sci Rev."},{"issue":"5","key":"2025121203563514500_ref70","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1080\/00330124.2023.2194363","article-title":"Spatiotemporal modeling of the association between neighborhood factors and COVID-19 incidence rates in Scotland","volume":"75","author":"Wang","year":"2023","journal-title":"Prof Geogr."},{"issue":"3","key":"2025121203563514500_ref71","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s41060-021-00295-9","article-title":"A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA","volume":"15","author":"Lucas","year":"2023","journal-title":"Int J Data Sci Anal."},{"key":"2025121203563514500_ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2023.103559","article-title":"Predicting the transmission trend of respiratory viruses in new regions via geospatial similarity learning","volume":"125","author":"Zhao","year":"2023","journal-title":"Int J Appl Earth Observ Geoinform."},{"issue":"1","key":"2025121203563514500_ref73","doi-asserted-by":"publisher","first-page":"4720","DOI":"10.1038\/s41467-021-24732-2","article-title":"Deep learning of contagion dynamics on complex networks","volume":"12","author":"Murphy","year":"2021","journal-title":"Nat Commun."},{"issue":"2","key":"2025121203563514500_ref74","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s11831-023-09997-8","article-title":"Infectious disease modeling: from traditional to evolutionary algorithms","volume":"31","author":"Malhotra","year":"2024","journal-title":"Arch Comput Methods Eng."},{"key":"2025121203563514500_ref75","doi-asserted-by":"publisher","first-page":"241","DOI":"10.3389\/fpubh.2020.00241","article-title":"Economic consequences of the COVID-19 outbreak: the need for epidemic preparedness","volume":"8","author":"Pak","year":"2020","journal-title":"Front Public Health."},{"key":"2025121203563514500_ref76","first-page":"465","article-title":"Exploring the relationship between micro-enterprises and regional development: Evidence from Tunisia","volume-title":"International Review of Entrepreneurship","author":"Bonnet","year":"2017"},{"issue":"8","key":"2025121203563514500_ref77","doi-asserted-by":"publisher","first-page":"164","DOI":"10.3390\/tropicalmed7080164","article-title":"How spatial epidemiology helps understand infectious human disease transmission","volume":"7","author":"Lin","year":"2022","journal-title":"Trop Med Infect Dis."},{"key":"2025121203563514500_ref78","doi-asserted-by":"publisher","first-page":"579190","DOI":"10.3389\/fpubh.2020.579190","article-title":"Analysis of COVID-19 cases' spatial dependence in US counties reveals health inequalities","volume":"8","author":"Saffary","year":"2020","journal-title":"Front Public Health."},{"key":"2025121203563514500_ref79","doi-asserted-by":"publisher","DOI":"10.1017\/S0950268820001983","article-title":"The spatio-temporal epidemic dynamics of COVID-19 outbreak in Africa","volume":"148","author":"Gayawan","year":"2020","journal-title":"Epidemiol Infect."},{"issue":"1","key":"2025121203563514500_ref80","doi-asserted-by":"publisher","first-page":"29349","DOI":"10.3402\/gha.v8.29349","article-title":"Socio-demographic and environmental determinants of infectious disease morbidity in children under 5 years in Ghana","volume":"8","author":"Amugsi","year":"2015","journal-title":"Glob Health Action."},{"issue":"2","key":"2025121203563514500_ref81","doi-asserted-by":"publisher","first-page":"e88","DOI":"10.1016\/S2468-2667(23)00005-1","article-title":"Incorporating social environment data in infectious disease research","volume":"8","author":"Noppert","year":"2023","journal-title":"Lancet Public Health."},{"key":"2025121203563514500_ref82","doi-asserted-by":"publisher","DOI":"10.1016\/j.onehlt.2020.100206","article-title":"Environmental and sociodemographic risk factors associated with environmentally transmitted zoonoses hospitalisations in Queensland, Australia","volume":"12","author":"Cortes-Ramirez","year":"2021","journal-title":"One Health."},{"issue":"1","key":"2025121203563514500_ref83","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3390\/ijerph21010078","article-title":"Socioeconomic, demographic, and environmental factors may inform malaria intervention prioritization in urban Nigeria","volume":"21","author":"Chiziba","year":"2024","journal-title":"Int J Environ Res Public Health."},{"issue":"11","key":"2025121203563514500_ref84","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1016\/s1473-3099(20)30553-3","article-title":"Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study","volume":"20","author":"Badr","year":"2020","journal-title":"Lancet Infect Dis."},{"issue":"5","key":"2025121203563514500_ref85","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30725-8","article-title":"Associations between phone mobility data and COVID-19 cases","volume":"21","author":"Gatalo","year":"2021","journal-title":"Lancet Infect Dis."},{"issue":"1","key":"2025121203563514500_ref86","doi-asserted-by":"publisher","first-page":"16987","DOI":"10.1038\/s41598-022-21126-2","article-title":"Impact of government policies on the COVID-19 pandemic unraveled by mathematical modelling","volume":"12","author":"Wilk","year":"2022","journal-title":"Sci Rep."},{"issue":"1","key":"2025121203563514500_ref87","doi-asserted-by":"publisher","first-page":"6955","DOI":"10.1038\/s41598-021-85381-5","article-title":"Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA","volume":"11","author":"Qui\u00f1ones","year":"2021","journal-title":"Sci Rep."},{"issue":"5","key":"2025121203563514500_ref88","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0268320","article-title":"Composite measures of quality of health care: evidence mapping of methodology and reporting","volume":"17","author":"Kara","year":"2022","journal-title":"PLoS One."},{"issue":"1","key":"2025121203563514500_ref89","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1186\/s12874-019-0769-x","article-title":"Ranking hospital performance based on individual indicators: can we increase reliability by creating composite indicators?","volume":"19","author":"Austin","year":"2019","journal-title":"BMC Med Res Methodol."}],"container-title":["Epidemiologic Reviews"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/epirev\/advance-article-pdf\/doi\/10.1093\/epirev\/mxaf017\/64880903\/mxaf017.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/epirev\/article-pdf\/47\/1\/mxaf017\/64880903\/mxaf017.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/epirev\/article-pdf\/47\/1\/mxaf017\/64880903\/mxaf017.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T08:56:46Z","timestamp":1765529806000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/epirev\/article\/doi\/10.1093\/epirev\/mxaf017\/8300103"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":89,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1,10]]}},"URL":"https:\/\/doi.org\/10.1093\/epirev\/mxaf017","relation":{},"ISSN":["1478-6729"],"issn-type":[{"value":"1478-6729","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025]]},"published":{"date-parts":[[2025]]},"article-number":"mxaf017"}}