{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T13:00:17Z","timestamp":1771506017531,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010892","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000}}],"reference-count":28,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T00:00:00Z","timestamp":1675728000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011738","name":"Lee Kong Chian School of Medicine","doi-asserted-by":"crossref","award":["Ministry of Education Start-Up Grant"],"award-info":[{"award-number":["Ministry of Education Start-Up Grant"]}],"id":[{"id":"10.13039\/501100011738","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Upper respiratory tract infections (URTIs) represent a large strain on primary health resources. To mitigate URTI transmission and public health burdens, it is important to pre-empt and provide forward guidance on URTI burden, while taking into account various facets which influence URTI transmission. This is so that appropriate public health measures can be taken to mitigate strain on primary care resources. This study describes a new approach to forecasting URTIs which can be used for national public health resource planning. Specifically, using environmental and disease data comprising more than 1000 dimensions, we developed sub-models which optimizes model explainability, in-sample model fit, predictive accuracy and combines many weaker predictors over a 2-month time horizon to generate direct, point forecasts over a 1\u20138 week ahead forecast horizon. Predictive performance was evaluated using rolling out-of-sample forecast assessment within both periods with\/without structural breaks in transmission over the period of 2012\u20132022. We showed that forecast combinations of 5 other forecasting models had better and more consistent predictive performance than other modelling approaches, over periods with and without structural breaks in transmission dynamics. Furthermore, epidemiological analysis on high dimensional data was enabled using post-selection inference, to show the dynamic association between lower temperature, increases in past relative humidity and absolute humidity and increased URTIs attendance. The methods proposed can be used for outbreak preparedness and guide healthcare resource planning, in both stable periods of transmission and periods where structural breaks in data occur.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010892","type":"journal-article","created":{"date-parts":[[2023,2,7]],"date-time":"2023-02-07T18:36:38Z","timestamp":1675794998000},"page":"e1010892","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":11,"title":["Forecasting upper respiratory tract infection burden using high-dimensional time series data and forecast combinations"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2245-0331","authenticated-orcid":true,"given":"Jue Tao","family":"Lim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelvin Bryan","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Abisheganaden","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Borame L.","family":"Dickens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"key":"pcbi.1010892.ref001","volume-title":"Upper Respiratory Tract Infection.","author":"M Thomas","year":"2022"},{"key":"pcbi.1010892.ref002","article-title":"Global burden of upper respiratory infections in 204 countries and territories, from 1990 to 2019.","volume":"37","author":"X Jin","year":"2021","journal-title":"eClinicalMedicine."},{"key":"pcbi.1010892.ref003","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1001\/archinte.163.4.487","article-title":"The Economic Burden of Non\u2013Influenza-Related Viral Respiratory Tract Infection in the United States","volume":"163","author":"AM Fendrick","year":"2003","journal-title":"Arch Intern Med"},{"key":"pcbi.1010892.ref004","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1093\/bmb\/61.1.215","article-title":"Acute upper airway infections: Childhood respiratory infections","volume":"61","author":"JV West","year":"2002","journal-title":"Br Med Bull"},{"key":"pcbi.1010892.ref005","doi-asserted-by":"crossref","first-page":"99","DOI":"10.4193\/Rhino14.239","article-title":"Exposure to cold and acute upper respiratory tract infection.","volume":"53","author":"R Eccles","year":"2015","journal-title":"Rhinol J."},{"key":"pcbi.1010892.ref006","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1093\/pubmed\/21.3.331","article-title":"The impact of climate on the prevalence of respiratory tract infections in early childhood in Lahore, Pakistan.","volume":"21","author":"V Erling","year":"1999","journal-title":"J Public Health"},{"key":"pcbi.1010892.ref007","doi-asserted-by":"crossref","first-page":"1884","DOI":"10.1017\/S0950268810002955","article-title":"Correlations between clinical illness, respiratory virus infections and climate factors in a tropical paediatric population","volume":"139","author":"TP Loh","year":"2011","journal-title":"Epidemiol Infect"},{"key":"pcbi.1010892.ref008","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.rmed.2008.09.011","article-title":"Cold temperature and low humidity are associated with increased occurrence of respiratory tract infections.","volume":"103","author":"TM M\u00e4kinen","year":"2009","journal-title":"Respir Med"},{"key":"pcbi.1010892.ref009","article-title":"Impact of Temperature Relative Humidity and Absolute Humidity on the Incidence of Hospitalizations for Lower Respiratory Tract Infections Due to Influenza, Rhinovirus, and Respiratory Syncytial Virus: Results from Community-Acquired Pneumonia Organization (CAPO) International Cohort Study.","volume":"1","author":"T Wiemken","year":"2017","journal-title":"Univ Louisville J Respir Infect."},{"key":"pcbi.1010892.ref010","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1017\/S0950268812001379","article-title":"Sunshine, rainfall, humidity and child pneumonia in the tropics: time-series analyses","volume":"141","author":"S PAYNTER","year":"2013","journal-title":"Epidemiol Infect"},{"key":"pcbi.1010892.ref011","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4899-7612-3","volume-title":"An Introduction to Mathematical Epidemiology","author":"M. Martcheva","year":"2015"},{"key":"pcbi.1010892.ref012","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s41060-020-00235-z","article-title":"Forecasting respiratory tract infection episodes from prescription data for healthcare service planning.","volume":"11","author":"AR Khan","year":"2021","journal-title":"Int J Data Sci Anal"},{"key":"pcbi.1010892.ref013","doi-asserted-by":"crossref","first-page":"200","DOI":"10.12998\/wjcc.v6.i8.200","article-title":"Machine learning to relate PM2.5 and PM10 concentrations to outpatient visits for upper respiratory tract infections in Taiwan: A nationwide analysis.","volume":"6","author":"M-J Chen","year":"2018","journal-title":"World J Clin Cases"},{"key":"pcbi.1010892.ref014","doi-asserted-by":"crossref","first-page":"17928","DOI":"10.1038\/s41598-019-54495-2","article-title":"A method for hand-foot-mouth disease prediction using GeoDetector and LSTM model in Guangxi, China.","volume":"9","author":"J Gu","year":"2019","journal-title":"Sci Rep"},{"key":"pcbi.1010892.ref015","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1017\/S0950268806007084","article-title":"Real-time epidemic forecasting for pandemic influenza","volume":"135","author":"IM Hall","year":"2007","journal-title":"Epidemiol Infect"},{"key":"pcbi.1010892.ref016","doi-asserted-by":"crossref","first-page":"e1005801","DOI":"10.1371\/journal.pcbi.1005801","article-title":"Individual versus superensemble forecasts of seasonal influenza outbreaks in the United States.","volume":"13","author":"TK Yamana","year":"2017","journal-title":"PLOS Comput Biol"},{"key":"pcbi.1010892.ref017","unstructured":"MOH | Weekly Infectious Diseases Bulletin. 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