{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T14:42:17Z","timestamp":1792420937118,"version":"4.5.0"},"reference-count":22,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","license":[{"start":{"date-parts":[[2023,8,20]],"date-time":"2023-08-20T00:00:00Z","timestamp":1692489600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["Infect Dis Immun"],"published-print":{"date-parts":[[2024,1]]},"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n \u201cChickenpox\u201d is a highly infectious disease caused by the varicella-zoster virus, influenced by seasonal and spatial factors. Dealing with varicella-zoster epidemics can be a substantial drain on health-authority resources. Methods that improve the ability to locally predict case numbers from time-series data sets every week are therefore worth developing.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Simple-to-extract trend attributes from published univariate weekly case-number univariate data sets were used to generate multivariate data for Hungary covering 10 years. That attribute-enhanced data set was assessed by machine learning (ML) and deep learning (DL) models to generate weekly case forecasts from next week (t0) to 12 weeks forward (t+12). The ML and DL predictions were compared with those generated by multilinear regression and univariate prediction methods.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Support vector regression generates the best predictions for weeks t0 and t+1, whereas extreme gradient boosting generates the best predictions for weeks t+3 to t+12. Long-short-term memory only provides comparable prediction accuracy to the ML models for week t+12. Multi\u2013K-fold cross validation reveals that overall the lowest prediction uncertainty is associated with the tree-ensemble ML models.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The novel trend-attribute method offers the potential to reduce prediction errors and improve transparency for chickenpox time series.<\/jats:p>\n <\/jats:sec>","DOI":"10.1097\/id9.0000000000000096","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T01:00:40Z","timestamp":1691542840000},"page":"25-34","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":4,"title":["Weeks-Ahead Epidemiological Predictions of Varicella Cases From Univariate Time Series Data Applying Artificial Intelligence"],"prefix":"10.1097","volume":"4","author":[{"given":"David A.","family":"Wood","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2023,8,20]]},"reference":[{"issue":"2","key":"bib1-20260710","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1017\/S0950268801005921","article-title":"Epidemiology of varicella zoster virus infection in Canada and the United Kingdom","volume":"127","year":"2001","journal-title":"Epidemiol Infect"},{"key":"bib2-20260710","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1186\/1471-2334-7-59","article-title":"Varicella and herpes zoster in Madrid, based on the sentinel general practitioner network: 1997\u20132004","volume":"7","year":"2007","journal-title":"BMC Infect Dis"},{"issue":"3","key":"bib3-20260710","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1093\/infdis\/172.3.706","article-title":"The epidemiology of varicella and its complications","volume":"172","year":"1995","journal-title":"J Infect Dis"},{"issue":"46\u201347","key":"bib4-20260710","first-page":"5398","article-title":"The changing epidemiology of chickenpox in Alberta","volume":"23","year":"2005","journal-title":"Vaccine"},{"issue":"1","key":"bib5-20260710","doi-asserted-by":"crossref","first-page":"236","DOI":"10.4161\/hv.36157","article-title":"The impact of ten years of infant universal varicella vaccination in Sicily, Italy (2003\u20132012)","volume":"11","year":"2015","journal-title":"Hum Vaccin Immunother"},{"issue":"e189","key":"bib6-20260710","first-page":"1","article-title":"Linear and non-linear combination forecasting model of varicella incidence in Chongqing","volume":"149","year":"2021","journal-title":"Epidemiol Infect"},{"issue":"1","key":"bib7-20260710","doi-asserted-by":"crossref","first-page":"678","DOI":"10.1186\/s12889-022-12898-3","article-title":"Epidemiological analysis of varicella in Dalian from 2009 to 2019 and application of three kinds of model in prediction prevalence of varicella","volume":"22","year":"2022","journal-title":"BMC Public Health"},{"issue":"1","key":"bib8-20260710","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1186\/s12879-019-3759-z","article-title":"The impact of childhood varicella vaccination on the incidence of herpes zoster in the general population: modelling the effect of exogenous and endogenous varicella-zoster virus immunity boosting","volume":"19","year":"2019","journal-title":"BMC Infect Dis"},{"key":"bib9-20260710","doi-asserted-by":"crossref","first-page":"e5012","DOI":"10.7717\/peerj.5012","article-title":"Evaluation of the effect of chickenpox vaccination on shingles epidemiology using agent-based modeling","volume":"6","year":"2018","journal-title":"PeerJ"},{"key":"bib10-20260710","doi-asserted-by":"crossref","first-page":"e63116","DOI":"10.1371\/journal.pone.0063116","article-title":"Comparative study of four time series methods in forecasting typhoid fever incidence in China","volume":"8","year":"2013","journal-title":"PloS One"},{"key":"bib11-20260710","first-page":"415","article-title":"Use of artificial intelligence in infectious diseases","year":"2020","journal-title":"Artif Intell Precision Health"},{"key":"bib12-20260710","doi-asserted-by":"crossref","first-page":"652669","DOI":"10.3389\/frai.2021.652669","article-title":"The promise of AI in detection, diagnosis, and epidemiology for combating COVID-19: beyond the hype","volume":"4","year":"2021","journal-title":"Front Artif Intell"},{"key":"bib13-20260710","doi-asserted-by":"crossref","first-page":"5099","DOI":"10.3390\/ijerph19095099","article-title":"Machine learning, deep learning, and mathematical models to analyze forecasting and epidemiology of COVID-19: a systematic literature review","volume":"19","year":"2022","journal-title":"Int J Environ Res Public Health"},{"issue":"12","key":"bib14-20260710","doi-asserted-by":"crossref","first-page":"477","DOI":"10.3390\/biology9120477","article-title":"Epidemic dynamics via wavelet theory and machine learning with applications to COVID-19","volume":"9","year":"2020","journal-title":"Biology"},{"issue":"12","key":"bib15-20260710","doi-asserted-by":"crossref","first-page":"e0208404","DOI":"10.1371\/journal.pone.0208404","article-title":"Time series modeling of pertussis incidence in China from 2004 to 2018 with a novel wavelet based SARIMA-NAR hybrid model","volume":"13","year":"2018","journal-title":"PloS One"},{"issue":"2","key":"bib16-20260710","doi-asserted-by":"crossref","first-page":"e0247854","DOI":"10.1371\/journal.pone.0247854","article-title":"COVID-19, learning from the past: a wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and twitter trends in Italian Lombardy region","volume":"16","year":"2021","journal-title":"PloS One"},{"key":"bib17-20260710","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1111\/2041-210X.13480","article-title":"Tensor decomposition for infectious disease incidence data","volume":"11","year":"2020","journal-title":"Methods Ecol Evol"},{"key":"bib18-20260710","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1007\/s40095-022-00488-3","article-title":"Near-term historical trend attribute extraction improves hours-ahead forecasts of solar capacity factor at the country level coupled with artificial intelligence","volume":"13","year":"2022","journal-title":"Int J Energy Envir E"},{"key":"bib21-20260710","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s13362-020-00079-z","article-title":"Modeling the transmission dynamics of varicella in Hungary","volume":"10","year":"2020","journal-title":"J Math Industry"},{"key":"bib23-20260710","first-page":"1","article-title":"The association between varicella (chickenpox) and group a streptococcus infections in historical perspective","volume":"4","year":"2016","journal-title":"Sage Open Med"},{"issue":"2","key":"bib25-20260710","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","article-title":"Cross-validatory choice and assessment of statistical predictions","volume":"36","year":"1974","journal-title":"J R Stat Soc Ser B (Methodological)"},{"key":"bib27-20260710","first-page":"769","article-title":"Concentration and dependency ratios","volume":"87","year":"1997","journal-title":"Riv Polit Econ"}],"container-title":["Infectious Diseases & Immunity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.lww.com\/10.1097\/ID9.0000000000000096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T14:00:37Z","timestamp":1783692037000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.lww.com\/10.1097\/ID9.0000000000000096"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,20]]},"references-count":22,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1097\/id9.0000000000000096","relation":{},"ISSN":["2096-9511","2693-8839"],"issn-type":[{"value":"2096-9511","type":"print"},{"value":"2693-8839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,20]]}}}