{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T17:14:43Z","timestamp":1773335683341,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T00:00:00Z","timestamp":1749168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2112562"],"award-info":[{"award-number":["2112562"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2229996"],"award-info":[{"award-number":["2229996"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office","doi-asserted-by":"publisher","award":["W911NF-23-2-0224"],"award-info":[{"award-number":["W911NF-23-2-0224"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000030","name":"U.S. Department of Health & Human Services | Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["CDC-RFA-FT-23-0069"],"award-info":[{"award-number":["CDC-RFA-FT-23-0069"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000030","name":"U.S. Department of Health & Human Services | Centers for Disease Control and Prevention","doi-asserted-by":"publisher","award":["NOA: 6 NU38FT000012-01"],"award-info":[{"award-number":["NOA: 6 NU38FT000012-01"]}],"id":[{"id":"10.13039\/100000030","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009945","name":"Merck KGaA","doi-asserted-by":"publisher","award":["Future Insight Prize"],"award-info":[{"award-number":["Future Insight Prize"]}],"id":[{"id":"10.13039\/100009945","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Comput Sci"],"DOI":"10.1038\/s43588-025-00798-6","type":"journal-article","created":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T09:03:15Z","timestamp":1749200595000},"page":"467-480","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Advancing real-time infectious disease forecasting using large language models"],"prefix":"10.1038","volume":"5","author":[{"given":"Hongru","family":"Du","sequence":"first","affiliation":[]},{"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Jianan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Shaochong","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7067-7752","authenticated-orcid":false,"given":"Xihong","family":"Lin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1486-8412","authenticated-orcid":false,"given":"Yiran","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Lauren M.","family":"Gardner","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6431-8956","authenticated-orcid":false,"given":"Hao \u2018Frank\u2019","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,6]]},"reference":[{"key":"798_CR1","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1038\/s41591-020-0883-7","volume":"26","author":"G Giordano","year":"2020","unstructured":"Giordano, G. et al. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy. Nat. Med. 26, 855\u2013860 (2020).","journal-title":"Nat. Med."},{"key":"798_CR2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-40305-x","volume":"14","author":"X Li","year":"2023","unstructured":"Li, X. et al. Wastewater-based epidemiology predicts COVID-19-induced weekly new hospital admissions in over 150 USA counties. Nat. Commun. 14, 4548 (2023).","journal-title":"Nat. Commun."},{"key":"798_CR3","doi-asserted-by":"publisher","first-page":"104482","DOI":"10.1016\/j.ebiom.2023.104482","volume":"89","author":"H Du","year":"2023","unstructured":"Du, H. et al. Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach. EBioMedicine 89, 104482 (2023).","journal-title":"EBioMedicine"},{"key":"798_CR4","doi-asserted-by":"publisher","first-page":"839","DOI":"10.2105\/AJPH.2022.306831","volume":"112","author":"NG Reich","year":"2022","unstructured":"Reich, N. G. et al. Collaborative hubs: making the most of predictive epidemic modeling. Am. J. Public Health 112, 839\u2013842 (2022).","journal-title":"Am. J. Public Health"},{"key":"798_CR5","doi-asserted-by":"publisher","first-page":"e2111456118","DOI":"10.1073\/pnas.2111456118","volume":"118","author":"R Rosenfeld","year":"2021","unstructured":"Rosenfeld, R. & Tibshirani, R. J. Epidemic tracking and forecasting: lessons learned from a tumultuous year. Proc. Natl Acad. Sci. USA 118, e2111456118 (2021).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"798_CR6","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1038\/s41586-020-2404-8","volume":"584","author":"S Hsiang","year":"2020","unstructured":"Hsiang, S. et al. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature 584, 262\u2013267 (2020).","journal-title":"Nature"},{"key":"798_CR7","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1038\/s41586-021-04188-6","volume":"600","author":"J Li","year":"2021","unstructured":"Li, J., Lai, S., Gao, G. F. & Shi, W. The emergence, genomic diversity and global spread of SARS-CoV-2. Nature 600, 408\u2013418 (2021).","journal-title":"Nature"},{"key":"798_CR8","doi-asserted-by":"publisher","first-page":"e738","DOI":"10.1016\/S2589-7500(22)00148-0","volume":"4","author":"K Nixon","year":"2022","unstructured":"Nixon, K. et al. An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation. Lancet Digit. Health 4, e738\u2013e747 (2022).","journal-title":"Lancet Digit. Health"},{"key":"798_CR9","doi-asserted-by":"publisher","first-page":"26190","DOI":"10.1073\/pnas.2007868117","volume":"117","author":"M Castro","year":"2020","unstructured":"Castro, M., Ares, S., Cuesta, J. A. & Manrubia, S. The turning point and end of an expanding epidemic cannot be precisely forecast. Proc. Natl Acad. Sci. USA 117, 26190\u201326196 (2020).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"798_CR10","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.ijforecast.2020.08.004","volume":"38","author":"JP Ioannidis","year":"2022","unstructured":"Ioannidis, J. P., Cripps, S. & Tanner, M. A. Forecasting for COVID-19 has failed. Int. J. Forecast. 38, 423\u2013438 (2022).","journal-title":"Int. J. Forecast."},{"key":"798_CR11","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1038\/s41586-021-03792-w","volume":"596","author":"A Telenti","year":"2021","unstructured":"Telenti, A. et al. After the pandemic: perspectives on the future trajectory of COVID-19. Nature 596, 495\u2013504 (2021).","journal-title":"Nature"},{"key":"798_CR12","doi-asserted-by":"publisher","first-page":"13881","DOI":"10.1073\/pnas.2008760117","volume":"117","author":"MR Nepomuceno","year":"2020","unstructured":"Nepomuceno, M. R. et al. Besides population age structure, health and other demographic factors can contribute to understanding the COVID-19 burden. Proc. Natl Acad. Sci. USA 117, 13881\u201313883 (2020).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"798_CR13","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1038\/s41586-023-06840-9","volume":"625","author":"K Ruggeri","year":"2023","unstructured":"Ruggeri, K. et al. A synthesis of evidence for policy from behavioural science during COVID-19. Nature 625, 134\u2013147 (2023).","journal-title":"Nature"},{"key":"798_CR14","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1038\/nature01255","volume":"420","author":"DB Searls","year":"2002","unstructured":"Searls, D. B. The language of genes. Nature 420, 211\u2013217 (2002).","journal-title":"Nature"},{"key":"798_CR15","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","volume":"620","author":"K Singhal","year":"2023","unstructured":"Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172\u2013180 (2023).","journal-title":"Nature"},{"key":"798_CR16","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-023-06160-y","volume":"619","author":"LY Jiang","year":"2023","unstructured":"Jiang, L. Y. et al. Health system-scale language models are all-purpose prediction engines. Nature 619, 357\u2013362 (2023).","journal-title":"Nature"},{"key":"798_CR17","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","volume":"29","author":"AJ Thirunavukarasu","year":"2023","unstructured":"Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930\u20131940 (2023).","journal-title":"Nat. Med."},{"key":"798_CR18","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1016\/j.neuron.2024.01.016","volume":"112","author":"D Bzdok","year":"2024","unstructured":"Bzdok, D. et al. Data science opportunities of large language models for neuroscience and biomedicine. Neuron 112, 698\u2013717 (2024).","journal-title":"Neuron"},{"key":"798_CR19","unstructured":"Williams, R., Hosseinichimeh, N., Majumdar, A. & Ghaffarzadegan, N. Epidemic modeling with generative agents. Preprint at https:\/\/arxiv.org\/abs\/2307.04986 (2023)."},{"key":"798_CR20","first-page":"19622","volume":"36","author":"N Gruver","year":"2023","unstructured":"Gruver, N., Finzi, M., Qiu, S. & Wilson, A. G. Large language models are zero-shot time series forecasters. Adv. Neural Inf. Process. Syst. 36, 19622\u201319635 (2023).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"798_CR21","unstructured":"Covid Data Tracker. CDC https:\/\/covid.cdc.gov\/covid-data-tracker\/#maps_new-admissions-percent-change-state (2024)."},{"key":"798_CR22","doi-asserted-by":"publisher","first-page":"e699","DOI":"10.1016\/S2589-7500(22)00167-4","volume":"4","author":"K Nixon","year":"2022","unstructured":"Nixon, K. et al. Real-time COVID-19 forecasting: challenges and opportunities of model performance and translation. Lancet Digit. Health 4, e699\u2013e701 (2022).","journal-title":"Lancet Digit. Health"},{"key":"798_CR23","unstructured":"Touvron, H. et al. LLaMA 2: open foundation and fine-tuned chat models. Preprint at https:\/\/arxiv.org\/abs\/2307.09288 (2023)."},{"key":"798_CR24","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/j.jclinepi.2009.11.009","volume":"63","author":"K Rufibach","year":"2010","unstructured":"Rufibach, K. Use of Brier score to assess binary predictions. J. Clin. Epidemiol. 63, 938\u2013939 (2010).","journal-title":"J. Clin. Epidemiol."},{"key":"798_CR25","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-21776-2","volume":"12","author":"K Leung","year":"2021","unstructured":"Leung, K., Wu, J. T. & Leung, G. M. Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing. Nat. Commun. 12, 1501 (2021).","journal-title":"Nat. Commun."},{"key":"798_CR26","doi-asserted-by":"publisher","first-page":"e2113561119","DOI":"10.1073\/pnas.2113561119","volume":"119","author":"EY Cramer","year":"2022","unstructured":"Cramer, E. Y. et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States. Proc. Natl Acad. Sci. USA 119, e2113561119 (2022).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"798_CR27","doi-asserted-by":"publisher","first-page":"e1011200","DOI":"10.1371\/journal.pcbi.1011200","volume":"20","author":"VK Lopez","year":"2024","unstructured":"Lopez, V. K. et al. Challenges of COVID-19 case forecasting in the US, 2020\u20132021. PLoS Comput. Biol. 20, e1011200 (2024).","journal-title":"PLoS Comput. Biol."},{"key":"798_CR28","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1038\/s41587-021-01040-0","volume":"39","author":"K Kalia","year":"2021","unstructured":"Kalia, K., Saberwal, G. & Sharma, G. The lag in SARS-CoV-2 genome submissions to GISAID. Nat. Biotechnol. 39, 1058\u20131060 (2021).","journal-title":"Nat. Biotechnol."},{"key":"798_CR29","unstructured":"TAG-VE statement on Omicron sublineages BQ.1 and XBB. WHO https:\/\/www.who.int\/news\/item\/27-10-2022-tag-ve-statement-on-omicron-sublineages-bq.1-and-xbb (2024)."},{"key":"798_CR30","doi-asserted-by":"publisher","first-page":"651","DOI":"10.15585\/mmwr.mm7224a2","volume":"72","author":"KC Ma","year":"2023","unstructured":"Ma, K. C. Genomic surveillance for SARS-CoV-2 variants: circulation of Omicron lineages\u2014United States, January 2022\u2013May 2023. MMWR Morb. Mortal. Wkly Rep. 72, 651\u2013656 (2023).","journal-title":"MMWR Morb. Mortal. Wkly Rep."},{"key":"798_CR31","doi-asserted-by":"publisher","first-page":"16732","DOI":"10.1073\/pnas.2006520117","volume":"117","author":"AL Bertozzi","year":"2020","unstructured":"Bertozzi, A. L., Franco, E., Mohler, G., Short, M. B. & Sledge, D. The challenges of modeling and forecasting the spread of COVID-19. Proc. Natl Acad. Sci. USA 117, 16732\u201316738 (2020).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"798_CR32","doi-asserted-by":"publisher","first-page":"e370","DOI":"10.1016\/S1473-3099(22)00434-0","volume":"22","author":"E Dong","year":"2022","unstructured":"Dong, E. et al. The Johns Hopkins University Center for Systems Science and Engineering COVID-19 dashboard: data collection process, challenges faced, and lessons learned. Lancet Infect. Dis. 22, e370\u2013e376 (2022).","journal-title":"Lancet Infect. Dis."},{"key":"798_CR33","unstructured":"COVID-19 reported patient impact and hospital capacity by facility. Department of Health and Human Services https:\/\/healthdata.gov\/Hospital\/COVID-19-Reported-Patient-Impact-and-Hospital-Capa\/anag-cw7u\/about_data (2024)."},{"key":"798_CR34","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","volume":"20","author":"E Dong","year":"2020","unstructured":"Dong, E., Du, H. & Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 20, 533\u2013534 (2020).","journal-title":"Lancet Infect. Dis."},{"key":"798_CR35","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1186\/s12889-024-17790-w","volume":"24","author":"H Du","year":"2024","unstructured":"Du, H., Saiyed, S. & Gardner, L. M. Association between vaccination rates and COVID-19 health outcomes in the United States: a population-level statistical analysis. BMC Public Health 24, 220 (2024).","journal-title":"BMC Public Health"},{"key":"798_CR36","unstructured":"COVID Data Tracker. CDC https:\/\/covid.cdc.gov\/covid-data-tracker\/#vaccine-delivery-coverage (2024)."},{"key":"798_CR37","unstructured":"State population totals and components of change: 2020\u20132023. US Census Bureau https:\/\/www.census.gov\/data\/tables\/time-series\/demo\/popest\/2020s-state-total.html#v2022 (2024)."},{"key":"798_CR38","doi-asserted-by":"publisher","first-page":"9696","DOI":"10.1073\/pnas.2004911117","volume":"117","author":"JB Dowd","year":"2020","unstructured":"Dowd, J. B. et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proc. Natl Acad. Sci. USA 117, 9696\u20139698 (2020).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"798_CR39","unstructured":"Radley, D., Collins, S. & Hayes, S. The Commonwealth Fund 2019 Scorecard on State Health System Performance (The Commonwealth Fund, 2022)."},{"key":"798_CR40","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1016\/S0140-6736(23)00461-0","volume":"401","author":"TJ Bollyky","year":"2023","unstructured":"Bollyky, T. J. et al. Assessing COVID-19 pandemic policies and behaviours and their economic and educational trade-offs across US states from Jan 1, 2020, to July 31, 2022: an observational analysis. Lancet 401, 1341\u20131360 (2023).","journal-title":"Lancet"},{"key":"798_CR41","unstructured":"Federal Elections 2020. FEC https:\/\/www.fec.gov\/introduction-campaign-finance\/election-results-and-voting-information\/federal-elections-2020\/ (2024)."},{"key":"798_CR42","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1038\/s41562-020-01009-0","volume":"4","author":"N Haug","year":"2020","unstructured":"Haug, N. et al. Ranking the effectiveness of worldwide COVID-19 government interventions. Nat. Hum. Behav. 4, 1303\u20131312 (2020).","journal-title":"Nat. Hum. Behav."},{"key":"798_CR43","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/s41562-021-01079-8","volume":"5","author":"T Hale","year":"2021","unstructured":"Hale, T. et al. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat. Hum. Behav. 5, 529\u2013538 (2021).","journal-title":"Nat. Hum. Behav."},{"key":"798_CR44","doi-asserted-by":"publisher","first-page":"1736","DOI":"10.1038\/s41564-022-01233-6","volume":"7","author":"JE Stockdale","year":"2022","unstructured":"Stockdale, J. E., Liu, P. & Colijn, C. The potential of genomics for infectious disease forecasting. Nat. Microbiol. 7, 1736\u20131743 (2022).","journal-title":"Nat. Microbiol."},{"key":"798_CR45","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998\u20136008 (2017).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"798_CR46","unstructured":"OpenAI et al. GPT-4 Technical Report. Preprint at https:\/\/arxiv.org\/abs\/2303.08774 (2024)."},{"key":"798_CR47","unstructured":"Fu, Y., Peng, H., Sabharwal, A., Clark, P. & Khot, T. Complexity-based prompting for multi-step reasoning. In The Eleventh International Conference on Learning Representations (2023)."},{"key":"798_CR48","doi-asserted-by":"crossref","unstructured":"Liang, J. et al. Code as policies: language model programs for embodied control. In 2023 IEEE International Conference on Robotics and Automation (ICRA) 9493\u20139500 (IEEE, 2023).","DOI":"10.1109\/ICRA48891.2023.10160591"},{"key":"798_CR49","unstructured":"Touvron, H. et al. LLaMA: open and efficient foundation language models. Preprint at https:\/\/arxiv.org\/abs\/2302.13971 (2023)."},{"key":"798_CR50","first-page":"23","volume":"12","author":"P Gage","year":"1994","unstructured":"Gage, P. A new algorithm for data compression. C Users J. 12, 23\u201338 (1994).","journal-title":"C Users J."},{"key":"798_CR51","first-page":"34892","volume":"36","author":"H Liu","year":"2023","unstructured":"Liu, H., Li, C., Wu, Q. & Lee, Y. J. Visual instruction tuning. Adv. Neural Inf. Process. Syst. 36, 34892\u201334916 (2023).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"798_CR52","doi-asserted-by":"publisher","unstructured":"Du, H. & Zhao, Y. Advancing real-time infectious disease forecasting using large language models. Zenodo https:\/\/doi.org\/10.5281\/zenodo.14788491 (2025).","DOI":"10.5281\/zenodo.14788491"},{"key":"798_CR53","doi-asserted-by":"publisher","first-page":"4121","DOI":"10.1093\/bioinformatics\/bty407","volume":"34","author":"J Hadfield","year":"2018","unstructured":"Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121\u20134123 (2018).","journal-title":"Bioinformatics"}],"container-title":["Nature Computational Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00798-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00798-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00798-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T22:02:59Z","timestamp":1750802579000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s43588-025-00798-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,6]]},"references-count":53,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["798"],"URL":"https:\/\/doi.org\/10.1038\/s43588-025-00798-6","relation":{},"ISSN":["2662-8457"],"issn-type":[{"value":"2662-8457","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,6]]},"assertion":[{"value":"15 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}