{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T21:03:45Z","timestamp":1789506225892,"version":"build-2803163510"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Epidemics"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.epidem.2026.100933","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:33:24Z","timestamp":1783035204000},"page":"100933","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["When and where: Spatiotemporal machine learning forecasts of respiratory infection-related primary care visits"],"prefix":"10.1016","volume":"56","author":[{"given":"Binay","family":"Adhikari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5545-2851","authenticated-orcid":false,"given":"Afraz","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer","family":"Vines","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armin","family":"Shahriari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2390-4884","authenticated-orcid":false,"given":"Reza","family":"Hosseini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naveed Z.","family":"Janjua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael A.","family":"Irvine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4288-7258","authenticated-orcid":false,"given":"Hind","family":"Sbihi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.epidem.2026.100933_bib1","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1038\/s41467-021-21708-0","article-title":"Wildfire Smoke Impacts Respiratory Health More Than Fine Particles from Other Sources: Observational Evidence from Southern California","volume":"12","author":"Aguilera","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.epidem.2026.100933_bib2","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2024.1279392","article-title":"Syndromic Surveillance of Population-Level COVID-19 Burden with Cough Monitoring in a Hospital Emergency Waiting Room","volume":"12","author":"Al Hossain","year":"2024","journal-title":"Front. Public. Health"},{"key":"10.1016\/j.epidem.2026.100933_bib3","series-title":"A companion to theoretical econometrics","first-page":"310","article-title":"Spatial econometrics","author":"Anselin","year":"2001"},{"issue":"5","key":"10.1016\/j.epidem.2026.100933_bib4","first-page":"24","article-title":"Predicting COVID-19 and Respiratory Illness: Results of the 2022-2023 Armed Forces Health Surveillance Division Forecasting Challenge","volume":"31","author":"Bova","year":"2024","journal-title":"MSMR"},{"issue":"3","key":"10.1016\/j.epidem.2026.100933_bib5","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"\u201cRandom Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.epidem.2026.100933_bib6","unstructured":"British Columbia Ministry of Citizens Services. 2022. \u201cPopulation Estimates - Province of British Columbia.\u201d \u3008https:\/\/www2.gov.bc.ca\/gov\/content\/data\/statistics\/people-population-community\/population\/population-estimates\u3009."},{"key":"10.1016\/j.epidem.2026.100933_bib7","series-title":"Client Roster (Client Registry System\/Enterprise Master Patient Index)","author":"British Columbia Ministry of Health","year":"2021"},{"key":"10.1016\/j.epidem.2026.100933_bib8","series-title":"Edited by British Columbia Ministry of Health [publisher]. British Columbia Ministry of Health; British Columbia Ministry of Health [publisher]. Data Extract","article-title":"Medical Services Plan (MSP) Payment Information File","author":"British Columbia Ministry of Health [Creator]","year":"2011"},{"key":"10.1016\/j.epidem.2026.100933_bib9","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: A Scalable Machine Learning System","author":"Chen","year":"2016","journal-title":"Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min."},{"issue":"5","key":"10.1016\/j.epidem.2026.100933_bib10","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1177\/00333549231186574","article-title":"Syndromic Surveillance for COVID-19, Massachusetts, February 2020\u2013November 2022: The Impact of Fever and Severity on Algorithm Performance","volume":"138","author":"Cocoros","year":"2023","journal-title":"Public. Health Rep."},{"issue":"2","key":"10.1016\/j.epidem.2026.100933_bib11","doi-asserted-by":"crossref","first-page":"185","DOI":"10.3390\/pathogens11020185","article-title":"A Comparison of Infectious Disease Forecasting Methods Across Locations, Diseases, and Time","volume":"11","author":"Dixon","year":"2022","journal-title":"Pathogens"},{"issue":"10","key":"10.1016\/j.epidem.2026.100933_bib12","first-page":"e741","article-title":"Data Capture and Sharing in the COVID-19 Pandemic: A Cause for Concern","volume":"4","author":"Dron","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"10.1016\/j.epidem.2026.100933_bib13","series-title":"Diagnostic Codes for Respiratory Conditions","author":"Government of British Columbia","year":"2017"},{"issue":"1","key":"10.1016\/j.epidem.2026.100933_bib14","article-title":"A Syndromic Surveillance Tool to Detect Anomalous Clusters of COVID-19 Symptoms in the United States","volume":"11","author":"G\u00fcemes","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.epidem.2026.100933_bib15","series-title":"Artificial Neural Networks","author":"Haykin","year":"1999"},{"key":"10.1016\/j.epidem.2026.100933_bib16","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.5518","article-title":"Random Forest as a Generic Framework for Predictive Modeling of Spatial and Spatio-Temporal Variables","volume":"6","author":"Hengl","year":"2018","journal-title":"PeerJ"},{"issue":"4","key":"10.1016\/j.epidem.2026.100933_bib17","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another Look at Measures of Forecast Accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecast."},{"issue":"September","key":"10.1016\/j.epidem.2026.100933_bib18","doi-asserted-by":"crossref","first-page":"164155","DOI":"10.1109\/ACCESS.2020.3022366","article-title":"Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy","volume":"8","author":"Karadayi","year":"2020","journal-title":"IEEE Access."},{"key":"10.1016\/j.epidem.2026.100933_bib19","first-page":"3146","article-title":"LightGBM: A Highly Efficient Gradient Boosting Decision Tree","volume":"30","author":"Ke","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.epidem.2026.100933_bib20","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1186\/s12889-021-11136-6","article-title":"A cross-sectional study measuring contact patterns using diaries in an urban and a rural community in South Africa, 2018","volume":"21","author":"Kleynhans","year":"2021","journal-title":"BMC Public. Health"},{"issue":"7","key":"10.1016\/j.epidem.2026.100933_bib21","doi-asserted-by":"crossref","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":"3","key":"10.1016\/j.epidem.2026.100933_bib22","doi-asserted-by":"crossref","DOI":"10.2196\/19446","article-title":"Early Stage Machine Learning\u2013Based Prediction of US County Vulnerability to the COVID-19 Pandemic: Machine Learning Approach","volume":"6","author":"Mehta","year":"2020","journal-title":"JMIR Public. Health Surveill."},{"issue":"2","key":"10.1016\/j.epidem.2026.100933_bib23","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1111\/j.1467-8306.2004.09402005.x","article-title":"Tobler\u2019s First Law and Spatial Analysis","volume":"94","author":"Miller","year":"2004","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"10.1016\/j.epidem.2026.100933_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.healthplace.2023.102988","article-title":"Modelling the Spatiotemporal Spread of COVID-19 Outbreaks and Prioritization of the Risk Areas in Toronto, Canada","volume":"80","author":"Nazia","year":"2023","journal-title":"Health & Place."},{"key":"10.1016\/j.epidem.2026.100933_bib25","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-01119-3","article-title":"Spatio-Temporal Prediction of the COVID-19 Pandemic in US Counties: Modeling with a Deep LSTM Neural Network","volume":"11","author":"Nikparvar","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.epidem.2026.100933_bib26","article-title":"CatBoost: Unbiased Boosting with Categorical Features","volume":"31","author":"Prokhorenkova","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.epidem.2026.100933_bib27","doi-asserted-by":"crossref","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":"1785","key":"10.1016\/j.epidem.2026.100933_bib28","article-title":"Social mixing patterns in rural and urban areas of southern China","volume":"281","author":"Read","year":"2014","journal-title":"Proc. R. Soc. B Biol. Sci."},{"issue":"10","key":"10.1016\/j.epidem.2026.100933_bib29","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1004513","article-title":"Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance","volume":"11","author":"Santillana","year":"2015","journal-title":"PLOS Comput. Biol."},{"issue":"3","key":"10.1016\/j.epidem.2026.100933_bib30","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1016\/j.ipm.2018.04.011","article-title":"Real-Time Processing of Social Media with Sentinel: A Syndromic Surveillance System Incorporating Deep Learning for Health Classification","volume":"56","author":"\u0218erban","year":"2019","journal-title":"Inf. Process. & Manag."},{"key":"10.1016\/j.epidem.2026.100933_bib31","unstructured":"Shi, Xingjian, and Dit-Yan Yeung. 2018. \u201cMachine Learning for Spatiotemporal Sequence Forecasting: A Survey.\u201d arXiv Preprint arXiv:1808.06865."},{"issue":"1","key":"10.1016\/j.epidem.2026.100933_bib32","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol."},{"issue":"10","key":"10.1016\/j.epidem.2026.100933_bib33","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1038\/s42256-022-00538-9","article-title":"Forecasting SARS-CoV-2 Transmission and Clinical Risk at Small Spatial Scales by the Application of Machine Learning Architectures to Syndromic Surveillance Data","volume":"4","author":"Ward","year":"2022","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.epidem.2026.100933_bib34","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2024.1248905","article-title":"Cohort profile: the British Columbia COVID-19 Cohort (BCC19C)-a dynamic, linked population-based cohort","volume":"12","author":"Wilton","year":"2024","journal-title":"Front. Public. Health"},{"key":"10.1016\/j.epidem.2026.100933_bib35","first-page":"72","article-title":"\u201cHigh Influenza Incidence and Disease Severity Among Children and Adolescents Aged 18 Years\u2015united States, 2022\u201323 Season.\u201d","author":"White","year":"2023","journal-title":"Mmwr. Morb. Mortal. Wkly. Report."},{"issue":"11","key":"10.1016\/j.epidem.2026.100933_bib36","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0280617","article-title":"Geographical Characteristics and Influencing Factors of the Influenza Epidemic in Hubei, China, from 2009 to 2019","volume":"18","author":"Yang","year":"2023","journal-title":"Plos. One"},{"issue":"October","key":"10.1016\/j.epidem.2026.100933_bib37","article-title":"Machine Learning Takes a Village: Assessing Neighbourhood-Level Vulnerability for an Overdose and Infectious Disease Outbreak","volume":"96","author":"Yedinak","year":"2021","journal-title":"Int. J. Drug. Policy"},{"issue":"August","key":"10.1016\/j.epidem.2026.100933_bib38","article-title":"Urban Spatial Risk Prediction and Optimization Analysis of POI Based on Deep Learning from the Perspective of an Epidemic","volume":"112","author":"Zhang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Epidemics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1755436526000496?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1755436526000496?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T20:08:28Z","timestamp":1789502908000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1755436526000496"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":38,"alternative-id":["S1755436526000496"],"URL":"https:\/\/doi.org\/10.1016\/j.epidem.2026.100933","relation":{},"ISSN":["1755-4365"],"issn-type":[{"value":"1755-4365","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"When and where: Spatiotemporal machine learning forecasts of respiratory infection-related primary care visits","name":"articletitle","label":"Article Title"},{"value":"Epidemics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.epidem.2026.100933","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"100933"}}