{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T05:08:56Z","timestamp":1775797736638,"version":"3.50.1"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>Aiming at the problems of delayed early warning for severe viral exanthems and insufficient prediction accuracy based on a single data source, this study proposes a dual-branch cross-modal fusion prediction model based on TCN-TransDAF. The model adopts a Dilated-TCN branch to capture the long-term evolution law of clinical temporal indicators within 72 h after diagnosis, and a local-global attention Transformer branch to mine the variation characteristics of virulence loci in viral genomes. With the aid of a cross-modal fusion mechanism, it realizes the deep coupling of dual-dimensional information of host and pathogen, and simultaneously completes the tasks of severe risk determination, subtype identification and onset time window prediction. The experimental results show that the model achieves an AUC-ROC of 0.942 for severe case prediction and a MAE of 5.8 h for severe case onset time window prediction on the test set. Compared with the optimal baseline model, the AUC-ROC is increased by 5.79%, the MAE is reduced by 26.34%, and the recall is increased by 10.09%, which enables accurate early warning 12\u201318 h before the onset of severe symptoms. This study provides an efficient technical solution for the early warning of severe viral exanthems, and its cross-modal modeling idea also offers a reference for the prediction research of other infectious diseases.<\/jats:p>","DOI":"10.3389\/fpubh.2026.1796307","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T04:27:11Z","timestamp":1775795231000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Severe viral rash progression prediction and surveillance early warning based on local-global attention and temporal convolutional network"],"prefix":"10.3389","volume":"14","author":[{"given":"Sai","family":"Li","sequence":"first","affiliation":[{"name":"Department of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengqiu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bibo","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeshu","family":"Ning","sequence":"additional","affiliation":[{"name":"Department of Neurology, The Affiliated Children's Hospital of Xiangya School of Medicine, Central South University, Hunan Children's Hospital","place":["Changsha, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junkai","family":"Ren","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoqi","family":"Wang","sequence":"additional","affiliation":[{"name":"Cardlytics, Inc.","place":["Milpitas, CA, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaozhou","family":"He","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"ski2","DOI":"10.1002\/ski2.120","article-title":"Rash morphology as a predictor of COVID-19 severity: a systematic review of the cutaneous manifestations of COVID-19","volume":"2","author":"Holmes","year":"2022","journal-title":"Skin Health Dis"},{"key":"B2","first-page":"339","article-title":"A deep convolutional neural network for skin rashes classification","volume-title":"Novel and Intelligent Digital Systems Conferences","author":"Ora no","year":"2022"},{"key":"B3","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/ICCCNT54827.2022.9984237","article-title":"Human monkeypox 2022 virus: machine learning prediction model, outbreak forecasting, visualization with time-series exploratory data analysis","volume-title":"2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)","author":"Bhosale","year":"2022"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1007\/s11030-024-10935-4","article-title":"Functional characterization and structural prediction of hypothetical proteins in monkeypox virus and identification of potential inhibitors","volume":"29","author":"Raen","year":"2025","journal-title":"Mol Divers"},{"key":"B5","doi-asserted-by":"publisher","first-page":"6156","DOI":"10.3390\/ijms24076156","article-title":"Epstein-Barr virus reactivation as a new predictor of achieving remission or lupus low disease activity state in patients with systemic lupus erythematosus with cutaneous involvement","volume":"24","author":"Miskovic","year":"2023","journal-title":"Int J Mol Sci"},{"key":"B6","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1007\/s40122-024-00612-7","article-title":"Development of a prediction model and corresponding scoring table for postherpetic neuralgia using six machine learning algorithms: a retrospective study","volume":"13","author":"Lin","year":"2024","journal-title":"Pain Ther"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1038\/s41598-024-83966-4","article-title":"Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence","volume":"15","author":"Abbas","year":"2025","journal-title":"Sci Rep"},{"key":"B8","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1504\/IJCAST.2025.147069","article-title":"Combining CNNs and symptom data for monkeypox virus detection","volume":"1","author":"Oncu","year":"2025","journal-title":"Int J Complex Appl Sci Technol"},{"key":"B9","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.jiph.2022.11.033","article-title":"Designing multi-epitope monkeypox virus-specific vaccine using immunoinformatics approach","volume":"16","author":"Zaib","year":"2023","journal-title":"J Infect Public Health"},{"key":"B10","doi-asserted-by":"publisher","first-page":"124","DOI":"10.22452\/mjcs.vol37no2.2","article-title":"Usage of particle swarm optimization in digital images selection for monkeypox virus prediction and diagnosis","volume":"37","author":"Mandal","year":"2024","journal-title":"Malays J Comput Sci"},{"key":"B11","doi-asserted-by":"publisher","first-page":"e52412","DOI":"10.2196\/52412","article-title":"Novel approach for detecting respiratory syncytial virus in pediatric patients using machine learning models based on patient-reported symptoms: model development and validation study","volume":"8","author":"Kawamoto","year":"2024","journal-title":"JMIR Form Res"},{"key":"B12","doi-asserted-by":"publisher","first-page":"104246","DOI":"10.1016\/j.inffus.2026.104246","article-title":"HSBNet: fusing semantics and anisotropic thermal diffusion fields for boundary-aware point cloud segmentation","volume":"2026","author":"Ning","year":"2026","journal-title":"Inf Fusion"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1454057","DOI":"10.3389\/fmed.2024.1454057","article-title":"Construction of a disease risk prediction model for postherpetic pruritus by machine learning","volume":"11","author":"Lin","year":"2024","journal-title":"Front Med"},{"key":"B14","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1038\/s41591-023-02225-7","article-title":"A deep-learning algorithm to classify skin lesions from mpox virus infection","volume":"29","author":"Thieme","year":"2023","journal-title":"Nat Med"},{"key":"B15","doi-asserted-by":"publisher","first-page":"2009093","DOI":"10.1080\/23311916.2021.2009093","article-title":"Machine learning approach for classification of maculopapular and vesicular rashes using the textural features of the skin images","volume":"9","author":"Upadya P","year":"2022","journal-title":"Cogent Eng"},{"key":"B16","first-page":"4","article-title":"A supervised machine learning approach for diagnosing Lassa fever and viral hemorrhagic fever types reliant on observed signs","volume":"3","author":"Alile","year":"2022","journal-title":"Life"},{"key":"B17","doi-asserted-by":"publisher","first-page":"113674","DOI":"10.1016\/j.engappai.2025.113674","article-title":"Occluded person re-identification in multi-scenarios: a synergistic interaction framework with perception-aware optimization","volume":"166","author":"Ning","year":"2026","journal-title":"Eng Appl Artif Intell"},{"key":"B18","doi-asserted-by":"publisher","first-page":"e15552","DOI":"10.7717\/peerj.15552","article-title":"Comparative analysis of machine learning approaches for predicting respiratory virus infection and symptom severity","volume":"11","author":"I\u015f\u0131k","year":"2023","journal-title":"PeerJ"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2507.00304","article-title":"MamNet: a novel hybrid model for time-series forecasting and frequency pattern analysis in network traffic","author":"Zhang","year":"2025","journal-title":"arXiv [preprint]"},{"key":"B20","doi-asserted-by":"publisher","first-page":"e395","DOI":"10.1016\/S2666-5247(23)00133-7","article-title":"First shared SARS-CoV-2 genome: GISAID vs virological. org","volume":"4","author":"Burki","year":"2023","journal-title":"Lancet Microbe"},{"key":"B21","doi-asserted-by":"publisher","first-page":"117860","DOI":"10.1016\/j.jep.2024.117860","article-title":"TIRPnet: Risk prediction of traditional Chinese medicine ingredients based on a deep neural network","volume":"325","author":"Wei","year":"2024","journal-title":"J Ethnopharmacol"},{"key":"B22","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/s10916-025-02185-0","article-title":"Prediction of patient visits for skin diseases through enhanced evolutionary computation and ensemble learning","volume":"49","author":"Leng","year":"2025","journal-title":"J Med Syst"},{"key":"B23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/MysuruCon55714.2022.9972746","article-title":"A deep learning-based methodology for predicting monkey pox from skin sores","volume-title":"2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon)","author":"Ahamed","year":"2022"},{"key":"B24","author":"Miskat","year":"2025","journal-title":"A Vision Transformer-Based Pipeline for the Automated Classification of Monkeypox and Other Vesicular Skin Lesions: A Computationally-Efficient Approach for Global Health"},{"key":"B25","doi-asserted-by":"publisher","first-page":"e0327564","DOI":"10.1371\/journal.pone.0327564","article-title":"Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable transformer model","volume":"20","author":"Chen","year":"2025","journal-title":"PLoS ONE"},{"key":"B26","doi-asserted-by":"publisher","first-page":"108039","DOI":"10.1016\/j.bspc.2025.108039","article-title":"A dual-channel multi-source cross-modal fusion network with contrastive learning for microbe-disease prediction","volume":"110","author":"Wu","year":"2025","journal-title":"Biomed Signal Process Control"},{"key":"B27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-025-03868-8","article-title":"A dual-stage attention based multimodal fusion framework for medical visual question answering","volume":"6","author":"Mukesh","year":"2025","journal-title":"SN Comput Sci."},{"key":"B28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data."},{"key":"B29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci Data"},{"key":"B30","doi-asserted-by":"publisher","first-page":"30494","DOI":"10.2807\/1560-7917.ES.2017.22.13.30494","article-title":"Global initiative on sharing all influenza data-from vision to reality","volume":"22","author":"Shu","year":"2017","journal-title":"Eurosurveillance"},{"key":"B31","doi-asserted-by":"publisher","first-page":"105289","DOI":"10.1016\/j.ebiom.2024.105289","article-title":"Clinical and laboratory predictors of mpox severity and duration: an Italian multicentre cohort study (mpox-Icona)","volume":"107","author":"Mazzotta","year":"2024","journal-title":"EBioMedicine"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1916","DOI":"10.3390\/s25061916","article-title":"MAL-net: a multi-label deep learning framework integrating LSTM and multi-head attention for enhanced classification of IgA nephropathy subtypes using clinical sensor data","volume":"25","author":"Wang","year":"2025","journal-title":"Sensors"},{"key":"B33","doi-asserted-by":"publisher","first-page":"102548","DOI":"10.1016\/j.ecoinf.2024.102548","article-title":"MobileNet-GRU fusion for optimizing diagnosis of yellow vein mosaic virus","volume":"81","author":"Chawla","year":"2024","journal-title":"Ecol Inform"},{"key":"B34","doi-asserted-by":"publisher","first-page":"5959","DOI":"10.3390\/ijerph19105959","article-title":"A deep learning approach to estimate the incidence of infectious disease cases for routinely collected ambulatory records: the example of Varicella-Zoster","volume":"19","author":"Lanera","year":"2022","journal-title":"Int J Environ Res Public Health"},{"key":"B35","doi-asserted-by":"publisher","first-page":"9121","DOI":"10.1038\/s41598-025-92464-0","article-title":"COVID-19 health data prediction: a critical evaluation of CNN-based approaches","volume":"15","author":"Kim","year":"2025","journal-title":"Sci Rep"},{"key":"B36","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/ICARCE63054.2024.00035","article-title":"A New Efficient Classification Method for Monkeypox Virus Skin Lesions Based on MPFormer","volume-title":"2024 3rd International Conference on Automation, Robotics and Computer Engineering (ICARCE)","author":"Huan","year":"2024"},{"key":"B37","doi-asserted-by":"publisher","first-page":"32924","DOI":"10.1038\/s41598-025-12216-y","article-title":"Attention-Enhanced CNNs and transformers for accurate monkeypox and skin disease detection","volume":"15","author":"Mousa","year":"2025","journal-title":"Sci Rep"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1796307\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T04:27:13Z","timestamp":1775795233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1796307\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,10]]},"references-count":37,"alternative-id":["10.3389\/fpubh.2026.1796307"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2026.1796307","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,10]]},"article-number":"1796307"}}