{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:20Z","timestamp":1785369620330,"version":"3.55.0"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010414","name":"Health Research Board","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010414","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Public Health in Practice"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.puhip.2026.100748","type":"journal-article","created":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T16:35:53Z","timestamp":1770482153000},"page":"100748","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Utilising unsupervised machine learning to predict outbreaks of respiratory tract infections in acute Irish hospitals (2016-2021)"],"prefix":"10.1016","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7114-1693","authenticated-orcid":false,"given":"Doaa","family":"Amin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akke","family":"Vellinga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.puhip.2026.100748_bib1","series-title":"Infection Prevention and Control of Epidemic and Pandemic Prone Acute Respiratory Infections in Health Care","year":"2014"},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib2","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1186\/s12889-019-6824-z","article-title":"Factors associated with fatal cases of acute respiratory infection (Ari) among hospitalized patients in Guatemala","volume":"19","author":"Tomczyk","year":"2019","journal-title":"BMC Public Health"},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib3","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1186\/s12889-015-1685-6","article-title":"The cost of acute respiratory infections in northern India: a multi-site study","volume":"15","author":"Peasah","year":"2015","journal-title":"BMC Public Health"},{"issue":"2","key":"10.1016\/j.puhip.2026.100748_bib4","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/S2352-4642(21)00311-4","article-title":"Global, regional, and national causes of under-5 mortality in 2000-19: an updated systematic analysis with implications for the sustainable development goals","volume":"6","author":"Perin","year":"2022","journal-title":"Lancet Child Adolesc. Health"},{"key":"10.1016\/j.puhip.2026.100748_bib6","series-title":"Key Trends 2022","article-title":"Health in Ireland","year":"2022"},{"issue":"2","key":"10.1016\/j.puhip.2026.100748_bib9","doi-asserted-by":"crossref","DOI":"10.7189\/jogh.08.020303","article-title":"Artificial intelligence, machine learning and health systems","volume":"8","author":"Panch","year":"2018","journal-title":"J. Glob. Health"},{"key":"10.1016\/j.puhip.2026.100748_bib10","article-title":"Machine learning advances in microbiology: a review of methods and applications","volume":"13","author":"Jiang","year":"2022","journal-title":"Front. Microbiol."},{"issue":"7","key":"10.1016\/j.puhip.2026.100748_bib11","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/j.amjmed.2019.01.017","article-title":"Artificial intelligence transforms the future of health care","volume":"132","author":"Noorbakhsh-Sabet","year":"2019","journal-title":"Am. J. Med."},{"key":"10.1016\/j.puhip.2026.100748_bib12","article-title":"Using machine learning for healthcare challenges and opportunities","volume":"30","author":"Alanazi","year":"2022","journal-title":"Inform. Med. Unlocked"},{"issue":"20","key":"10.1016\/j.puhip.2026.100748_bib13","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"issue":"11","key":"10.1016\/j.puhip.2026.100748_bib14","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1007\/s40520-023-02552-2","article-title":"A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future","volume":"35","author":"Woodman","year":"2023","journal-title":"Aging Clin. Exp. Res."},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/02713683.2016.1175019","article-title":"Machine learning techniques in clinical vision sciences","volume":"42","author":"Caixinha","year":"2017","journal-title":"Curr. Eye Res."},{"issue":"2","key":"10.1016\/j.puhip.2026.100748_bib16","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s40745-015-0040-1","article-title":"A comprehensive survey of clustering algorithms","volume":"2","author":"Xu","year":"2015","journal-title":"Ann. Data Sci."},{"key":"10.1016\/j.puhip.2026.100748_bib17","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1016\/j.neucom.2017.06.053","article-title":"A review of clustering techniques and developments","volume":"267","author":"Saxena","year":"2017","journal-title":"Neurocomputing"},{"issue":"12","key":"10.1016\/j.puhip.2026.100748_bib18","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2020.29068","article-title":"Use of latent class analysis and k-means clustering to identify complex patient profiles","volume":"3","author":"Grant","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"10.1016\/j.puhip.2026.100748_bib19","doi-asserted-by":"crossref","DOI":"10.1002\/alz.12986","article-title":"Plasma biomarkers identify older adults at risk of Alzheimer'S disease and related dementias in a real\u2010world population\u2010based cohort","author":"Ferreira","year":"2023","journal-title":"Alzheimer's Dement."},{"issue":"5","key":"10.1016\/j.puhip.2026.100748_bib20","first-page":"242","article-title":"Using unsupervised machine learning to identify subgroups among home health patients with heart failure using telehealth","volume":"36","author":"Bose","year":"2018","journal-title":"Comput. Inf. Nurs."},{"key":"10.1016\/j.puhip.2026.100748_bib21","doi-asserted-by":"crossref","DOI":"10.7717\/peerj.5416","article-title":"Clustering of fmri data: the elusive optimal number of clusters","volume":"6","author":"Seghier","year":"2018","journal-title":"PeerJ"},{"issue":"5 Pt 2","key":"10.1016\/j.puhip.2026.100748_bib24","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1111\/j.1475-6773.2005.00444.x","article-title":"Measuring diagnoses: icd code accuracy","volume":"40","author":"O'Malley","year":"2005","journal-title":"Health Serv. Res."},{"issue":"3","key":"10.1016\/j.puhip.2026.100748_bib25","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1023\/A:1009769707641","article-title":"Extensions to the k-means algorithm for clustering large data sets with categorical values","volume":"2","author":"Huang","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"issue":"7","key":"10.1016\/j.puhip.2026.100748_bib26","doi-asserted-by":"crossref","first-page":"10223","DOI":"10.1016\/j.eswa.2009.01.060","article-title":"A new initialization method for categorical data clustering","volume":"36","author":"Cao","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.puhip.2026.100748_bib27","doi-asserted-by":"crossref","first-page":"201925","DOI":"10.1109\/ACCESS.2020.3036347","article-title":"Surveillance routing of covid-19 infection spread using an intelligent infectious diseases algorithm","volume":"8","author":"Guevara","year":"2020","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib28","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13638-021-01910-w","article-title":"A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm","volume":"2021","author":"Shi","year":"2021","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib30","doi-asserted-by":"crossref","first-page":"5661","DOI":"10.1038\/s41598-023-32790-3","article-title":"Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance","volume":"13","author":"Shahid","year":"2023","journal-title":"Sci. Rep."},{"issue":"3","key":"10.1016\/j.puhip.2026.100748_bib31","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s00357-014-9161-z","article-title":"Ward's hierarchical agglomerative clustering method: which algorithms implement ward's criterion?","volume":"31","author":"Murtagh","year":"2014","journal-title":"J. Classif."},{"issue":"8","key":"10.1016\/j.puhip.2026.100748_bib32","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1002\/int.21990","article-title":"New similarity measures of intuitionistic fuzzy sets based on the jaccard index with its application to clustering","volume":"33","author":"Hwang","year":"2018","journal-title":"Int. J. Intell. Syst."},{"key":"10.1016\/j.puhip.2026.100748_bib33","doi-asserted-by":"crossref","unstructured":"T. Cali\u0143ski. Dendrogram. Wiley statsref: Statistics reference online. https:\/\/doi.org\/https:\/\/doi.org\/10.1002\/9781118445112.stat05624.","DOI":"10.1002\/9781118445112.stat05624"},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib34","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s11135-023-01669-w","article-title":"Charting fields and spaces quantitatively: from multiple correspondence analysis to categorical principal components analysis","volume":"58","author":"Atkinson","year":"2024","journal-title":"Qual. Quantity"},{"key":"10.1016\/j.puhip.2026.100748_bib35","author":"Halford"},{"key":"10.1016\/j.puhip.2026.100748_bib36","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2017.00345","article-title":"The multiple correspondence analysis method and brain functional connectivity: its application to the study of the non-linear relationships of motor cortex and basal ganglia","volume":"11","author":"Rodriguez-Sabate","year":"2017","journal-title":"Front. Neurosci."},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib37","article-title":"Using research to prepare for outbreaks of severe acute respiratory infection","volume":"4","year":"2019","journal-title":"BMJ Glob. Health"},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib38","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/1472-6947-7-28","article-title":"Modeling and detection of respiratory-related outbreak signatures","volume":"7","author":"Craigmile","year":"2007","journal-title":"BMC Med. Inf. Decis. Making"},{"issue":"5","key":"10.1016\/j.puhip.2026.100748_bib39","doi-asserted-by":"crossref","first-page":"e262","DOI":"10.1016\/S1470-2045(19)30149-4","article-title":"Big data and machine learning algorithms for health-care delivery","volume":"20","author":"Ngiam","year":"2019","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.puhip.2026.100748_bib40","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.jcv.2017.07.011","article-title":"Prevalence and seasonality of six respiratory viruses during five consecutive epidemic seasons in Belgium","volume":"94","author":"Ramaekers","year":"2017","journal-title":"J. Clin. Virol."},{"issue":"3","key":"10.1016\/j.puhip.2026.100748_bib41","doi-asserted-by":"crossref","first-page":"e154","DOI":"10.1016\/S2542-5196(20)30297-7","article-title":"Seasonal association between viral causes of hospitalised acute lower respiratory infections and meteorological factors in China: a retrospective study","volume":"5","author":"Xu","year":"2021","journal-title":"Lancet Planet. Health"},{"issue":"9","key":"10.1016\/j.puhip.2026.100748_bib42","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0203751","article-title":"Seasonal association between ambient ozone and hospital admission for respiratory diseases in hanoi, Vietnam","volume":"13","author":"Luong","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.puhip.2026.100748_bib43","article-title":"Ambient temperature and seasonal variation in inpatient mortality from respiratory diseases: a retrospective observational study","volume":"35","author":"Achebak","year":"2023","journal-title":"TLancet Regional Health \u2013 Europe"},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib44","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1186\/s12913-019-4412-7","article-title":"Hospital and economic burden of influenza-like illness and lower respiratory tract infection in adults \u226550 years-old","volume":"19","author":"Trucchi","year":"2019","journal-title":"BMC Health Serv. Res."},{"issue":"1","key":"10.1016\/j.puhip.2026.100748_bib45","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jinf.2016.09.003","article-title":"Respiratory viruses and influenza-like illness: epidemiology and outcomes in children aged 6 months to 10 years in a multi-country population sample","volume":"74","author":"Taylor","year":"2017","journal-title":"J. Infect."},{"issue":"8","key":"10.1016\/j.puhip.2026.100748_bib46","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1093\/infdis\/jit316","article-title":"Patients hospitalized with laboratory-confirmed influenza during the 2010\u20132011 influenza season: exploring disease severity by virus type and subtype","volume":"208","author":"Chaves","year":"2013","journal-title":"J. Infect. Dis."},{"key":"10.1016\/j.puhip.2026.100748_bib47","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.3390\/sym14061237","article-title":"Using k-means clustering in python with periodic boundary conditions","volume":"14","author":"Miniak-G\u00f3recka","year":"2022","journal-title":"Symmetry"}],"container-title":["Public Health in Practice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666535226000273?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666535226000273?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T23:34:02Z","timestamp":1785368042000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666535226000273"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":41,"alternative-id":["S2666535226000273"],"URL":"https:\/\/doi.org\/10.1016\/j.puhip.2026.100748","relation":{},"ISSN":["2666-5352"],"issn-type":[{"value":"2666-5352","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Utilising unsupervised machine learning to predict outbreaks of respiratory tract infections in acute Irish hospitals (2016-2021)","name":"articletitle","label":"Article Title"},{"value":"Public Health in Practice","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.puhip.2026.100748","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 Ltd on behalf of The Royal Society for Public Health.","name":"copyright","label":"Copyright"}],"article-number":"100748"}}