{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T18:20:56Z","timestamp":1787854856066,"version":"build-2784847793"},"reference-count":69,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008902","name":"Los Alamos National Laboratory","doi-asserted-by":"publisher","award":["20240066DR"],"award-info":[{"award-number":["20240066DR"]}],"id":[{"id":"10.13039\/100008902","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Forecasting infectious disease outbreaks is hard. Forecasting emerging infectious diseases with limited historical data is even harder. In this paper, we investigate ways to improve emerging infectious disease forecasting when little pathogen-specific training data are available. Specifically, we explore two sources of information that may be available near the start of an emerging disease outbreak: synthetic data and genetic information. For this investigation, we conducted an experiment where we trained deep learning models on different combinations of real and synthetic data, both with and without genetic information, to explore how these models compare when forecasting COVID-19 cases for US states. All models are developed with an eye towards forecasting the next pandemic. We find that models trained with synthetic data have better forecast accuracy than models trained on real data alone, and models that use genetic variants have better forecast accuracy compared to those that do not. All models outperformed a baseline persistence model, a benchmark that proved challenging for many real-time COVID-19 case forecasting models, and multiple models outperformed the COVIDHub-4_week_ensemble. This paper demonstrates the value of these underutilized sources of information and provides a blueprint for forecasting future pandemics.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1014630","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:39:28Z","timestamp":1787852368000},"page":"e1014630","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Leveraging synthetic and genetic data to improve epidemic forecasting"],"prefix":"10.1371","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4681-091X","authenticated-orcid":true,"given":"Dave","family":"Osthus","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7170-867X","authenticated-orcid":true,"given":"Alexander C.","family":"Murph","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emma E.","family":"Goldberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren J.","family":"Beesley","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William M.","family":"Fischer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nidhi","family":"Parikh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren A.","family":"Castro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"issue":"5","key":"pcbi.1014630.ref001","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1011200","article-title":"Challenges of COVID-19 Case Forecasting in the US, 2020\u20132021","volume":"20","author":"VK Lopez","year":"2024","journal-title":"PLoS Comput Biol"},{"issue":"8","key":"pcbi.1014630.ref002","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1004382","article-title":"Flexible Modeling of Epidemics with an Empirical Bayes Framework","volume":"11","author":"LC Brooks","year":"2015","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"pcbi.1014630.ref003","doi-asserted-by":"crossref","first-page":"2991","DOI":"10.1038\/s41467-021-23234-5","article-title":"Multiscale influenza forecasting","volume":"12","author":"D Osthus","year":"2021","journal-title":"Nat Commun"},{"key":"pcbi.1014630.ref004","doi-asserted-by":"crossref","first-page":"100810","DOI":"10.1016\/j.epidem.2024.100810","article-title":"Flusion: Integrating multiple data sources for accurate influenza predictions","volume":"50","author":"EL Ray","year":"2025","journal-title":"Epidemics"},{"issue":"5","key":"pcbi.1014630.ref005","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","article-title":"An interactive web-based dashboard to track COVID-19 in real time","volume":"20","author":"E Dong","year":"2020","journal-title":"Lancet Infect Dis"},{"key":"pcbi.1014630.ref006","unstructured":"Kolman, Shannon. Disease Forecasting Tools Can Support Policymaking During Epidemics. 2023. Accessed: 2025-07-13. Available from: https:\/\/www.ncsl.org\/health\/disease-forecasting-tools-can-support-policymaking-during-epidemics"},{"key":"pcbi.1014630.ref007","doi-asserted-by":"crossref","first-page":"100612","DOI":"10.1016\/j.epidem.2022.100612","article-title":"The challenges of data in future pandemics","volume":"40","author":"N Shadbolt","year":"2022","journal-title":"Epidemics"},{"issue":"6","key":"pcbi.1014630.ref008","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1010115","article-title":"Addressing delayed case reporting in infectious disease forecast modeling","volume":"18","author":"LJ Beesley","year":"2022","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1014630.ref009","doi-asserted-by":"crossref","first-page":"1099182","DOI":"10.3389\/fdata.2023.1099182","article-title":"Human behavior in the time of COVID-19: Learning from big data","volume":"6","author":"H Lyu","year":"2023","journal-title":"Front Big Data"},{"issue":"6","key":"pcbi.1014630.ref010","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1111\/rsp3.12340","article-title":"The underlying factors of the COVID\u201019 spatially uneven spread. Initial evidence from regions in nine EU countries","volume":"12","author":"N Kapitsinis","year":"2020","journal-title":"Reg Sci Policy Pract"},{"issue":"4","key":"pcbi.1014630.ref011","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2020.06.043","article-title":"Tracking Changes in SARS-CoV-2 Spike: Evidence that D614G Increases Infectivity of the COVID-19 Virus","volume":"182","author":"B Korber","year":"2020","journal-title":"Cell"},{"key":"pcbi.1014630.ref012","doi-asserted-by":"crossref","first-page":"104534","DOI":"10.1016\/j.ebiom.2023.104534","article-title":"SARS-CoV-2 variant transition dynamics are associated with vaccination rates, number of co-circulating variants, and convalescent immunity","volume":"91","author":"LJ Beesley","year":"2023","journal-title":"EBioMedicine"},{"issue":"2","key":"pcbi.1014630.ref013","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.ijforecast.2020.08.004","article-title":"Forecasting for COVID-19 has failed","volume":"38","author":"JPA Ioannidis","year":"2022","journal-title":"Int J Forecast"},{"issue":"1","key":"pcbi.1014630.ref014","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1038\/s41597-022-01517-w","article-title":"The United States COVID-19 Forecast Hub dataset","volume":"9","author":"EY Cramer","year":"2022","journal-title":"Sci Data"},{"key":"pcbi.1014630.ref015","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.81916","article-title":"Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations","volume":"12","author":"K Sherratt","year":"2023","journal-title":"Elife"},{"issue":"11","key":"pcbi.1014630.ref016","doi-asserted-by":"crossref","first-page":"2532","DOI":"10.3390\/v14112532","article-title":"Challenges and Opportunities for Global Genomic Surveillance Strategies in the COVID-19 Era","volume":"14","author":"T Ling-Hu","year":"2022","journal-title":"Viruses"},{"issue":"13","key":"pcbi.1014630.ref017","first-page":"30494","article-title":"GISAID: Global initiative on sharing all influenza data - from vision to reality","volume":"22","author":"Y Shu","year":"2017","journal-title":"Euro Surveill"},{"issue":"11","key":"pcbi.1014630.ref018","doi-asserted-by":"crossref","first-page":"1802","DOI":"10.1016\/j.chom.2025.10.013","article-title":"Real-time monitoring of SARS-CoV-2 evolution during the COVID-19 pandemic","volume":"33","author":"B Korber","year":"2025","journal-title":"Cell Host Microbe"},{"key":"pcbi.1014630.ref019","doi-asserted-by":"crossref","first-page":"104482","DOI":"10.1016\/j.ebiom.2023.104482","article-title":"Incorporating variant frequencies data into short-term forecasting for COVID-19 cases and deaths in the USA: a deep learning approach","volume":"89","author":"H Du","year":"2023","journal-title":"EBioMedicine"},{"key":"pcbi.1014630.ref020","volume-title":"Forecasting: Principles and Practice","author":"RJ Hyndman","year":"2018"},{"key":"pcbi.1014630.ref021","doi-asserted-by":"crossref","unstructured":"Brauer F. Compartmental models in epidemiology. Mathematical Epidemiology. 2008. p. 19\u201379.","DOI":"10.1007\/978-3-540-78911-6_2"},{"key":"pcbi.1014630.ref022","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1146\/annurev-publhealth-040617-014317","article-title":"Agent-Based Modeling in Public Health: Current Applications and Future Directions","volume":"39","author":"M Tracy","year":"2018","journal-title":"Annu Rev Public Health"},{"issue":"1","key":"pcbi.1014630.ref023","doi-asserted-by":"crossref","DOI":"10.1093\/ve\/veac045","article-title":"Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications","volume":"8","author":"LA Featherstone","year":"2022","journal-title":"Virus Evol"},{"issue":"1","key":"pcbi.1014630.ref024","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1038\/s41598-018-36361-9","article-title":"Collaborative efforts to forecast seasonal influenza in the United States, 2015\u20132016","volume":"9","author":"CJ McGowan","year":"2019","journal-title":"Sci Rep"},{"key":"pcbi.1014630.ref025","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.neucom.2022.09.005","article-title":"Deep learning for Covid-19 forecasting: State-of-the-art review","volume":"511","author":"F Kamalov","year":"2022","journal-title":"Neurocomputing (Amst)"},{"key":"pcbi.1014630.ref026","doi-asserted-by":"crossref","unstructured":"Adhikari B, Xu X, Ramakrishnan N, Prakash BA. EpiDeep: Exploiting Embeddings for Epidemic Forecasting. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD \u201919. New York, NY, USA: Association for Computing Machinery. 2019. p. 577\u201386.","DOI":"10.1145\/3292500.3330917"},{"key":"pcbi.1014630.ref027","doi-asserted-by":"crossref","first-page":"109864","DOI":"10.1016\/j.chaos.2020.109864","article-title":"Time series forecasting of COVID-19 transmission in Canada using LSTM networks","volume":"135","author":"VKR Chimmula","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"pcbi.1014630.ref028","doi-asserted-by":"crossref","unstructured":"Xie F, Zhang Z, Li L, Zhou B, Tan Y. EpiGNN: Exploring Spatial Transmission with Graph Neural Network for Regional Epidemic Forecasting. In: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Grenoble, France, September 19\u201323, 2022, Proceedings, Part VI. Berlin, Heidelberg: Springer-Verlag; 2022. p. 469\u201385.","DOI":"10.1007\/978-3-031-26422-1_29"},{"issue":"11","key":"pcbi.1014630.ref029","doi-asserted-by":"crossref","first-page":"12191","DOI":"10.1609\/aaai.v36i11.21479","article-title":"CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting","volume":"36","author":"L Wang","year":"2022","journal-title":"AAAI"},{"issue":"1","key":"pcbi.1014630.ref030","doi-asserted-by":"crossref","first-page":"3896","DOI":"10.1038\/s41467-022-31511-0","article-title":"Deep learning from phylogenies to uncover the epidemiological dynamics of outbreaks","volume":"13","author":"J Voznica","year":"2022","journal-title":"Nat Commun"},{"issue":"4","key":"pcbi.1014630.ref031","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1093\/sysbio\/syaf082","article-title":"PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies","volume":"75","author":"MF Perez","year":"2025","journal-title":"Syst Biol"},{"key":"pcbi.1014630.ref032","doi-asserted-by":"crossref","first-page":"112306","DOI":"10.1016\/j.chaos.2022.112306","article-title":"Forecasting new diseases in low-data settings using transfer learning","volume":"161","author":"K Roster","year":"2022","journal-title":"Chaos Solitons Fractals"},{"key":"pcbi.1014630.ref033","unstructured":"University of Pittsburgh. Project Tycho. 2026. Accessed 2026-02-03. https:\/\/www.tycho.pitt.edu\/data\/"},{"key":"pcbi.1014630.ref034","article-title":"How transferable are features in deep neural networks?","volume":"27","author":"J Yosinski","year":"2014","journal-title":"Adv Neural Inform Process Syst"},{"issue":"10","key":"pcbi.1014630.ref035","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"SJ Pan","year":"2009","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"pcbi.1014630.ref036","unstructured":"Dudley C, Magdaleno R, Harding C, Sharma A, Martin E, Eisenberg M. Mantis: A simulation-grounded foundation model for disease forecasting. arXiv preprint arXiv:250812260. 2025."},{"issue":"6","key":"pcbi.1014630.ref037","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1013203","article-title":"Synthetic method of analogues for emerging infectious disease forecasting","volume":"21","author":"AC Murph","year":"2025","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"pcbi.1014630.ref038","doi-asserted-by":"crossref","first-page":"4255","DOI":"10.1038\/s41467-026-70937-8","article-title":"A disease-agnostic approach to ensemble learning for infectious disease forecasting","volume":"17","author":"AC Murph","year":"2026","journal-title":"Nat Commun"},{"key":"pcbi.1014630.ref039","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.07361","article-title":"The effects of a deleterious mutation load on patterns of influenza A\/H3N2\u2019s antigenic evolution in humans","volume":"4","author":"K Koelle","year":"2015","journal-title":"eLife"},{"key":"pcbi.1014630.ref040","doi-asserted-by":"crossref","unstructured":"Nair NG, Satpathy P, Christopher J. Covariate Shift: A Review and Analysis on Classifiers. In: 2019 Global Conference for Advancement in Technology (GCAT). IEEE; 2019. p. 1\u20136.","DOI":"10.1109\/GCAT47503.2019.8978471"},{"key":"pcbi.1014630.ref041","doi-asserted-by":"crossref","unstructured":"McInnes L, Healy J, Melville J. UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:180203426. 2018.","DOI":"10.21105\/joss.00861"},{"key":"pcbi.1014630.ref042","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/ACCESS.2018.2886026","article-title":"An Overview on Concept Drift Learning","volume":"7","author":"AS Iwashita","year":"2018","journal-title":"IEEE Access"},{"issue":"5","key":"pcbi.1014630.ref043","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1093\/aje\/kww211","article-title":"Type- and Subtype-Specific Influenza Forecast","volume":"185","author":"S Kandula","year":"2017","journal-title":"Am J Epidemiol"},{"issue":"7","key":"pcbi.1014630.ref044","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1009230","article-title":"Accurate influenza forecasts using type-specific incidence data for small geographic units","volume":"17","author":"J Turtle","year":"2021","journal-title":"PLoS Comput Biol"},{"issue":"2","key":"pcbi.1014630.ref045","first-page":"3","article-title":"LoRA: Low-rank adaptation of large language models","volume":"1","author":"EJ Hu","year":"2022","journal-title":"ICLR"},{"key":"pcbi.1014630.ref046","doi-asserted-by":"crossref","unstructured":"Case B, Salcedo MV, Fox SJ. An accurate hierarchical model to forecast diverse seasonal infectious diseases. medRxiv. 2025;2025\u20133.","DOI":"10.1101\/2025.03.03.25323259"},{"key":"pcbi.1014630.ref047","unstructured":"Johns Hopkins University. Johns Hopkins COVID-19 data hub ends after three years. 2023. Accessed 2026-02-04. https:\/\/hub.jhu.edu\/2023\/03\/10\/coronavirus-resource-center-data-hub-ends\/"},{"issue":"1","key":"pcbi.1014630.ref048","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1038\/s41597-023-02276-y","article-title":"Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic","volume":"10","author":"HS Badr","year":"2023","journal-title":"Sci Data"},{"key":"pcbi.1014630.ref049","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gks1195","volume":"41","author":"DA Benson","year":"2012","journal-title":"Nucleic Acids Research"},{"key":"pcbi.1014630.ref050","volume":"39","author":"R Leinonen","year":"2010","journal-title":"Nucleic Acids Res"},{"issue":"12","key":"pcbi.1014630.ref051","doi-asserted-by":"crossref","first-page":"100995","DOI":"10.1016\/j.lanmic.2024.100995","article-title":"Pathoplexus: towards fair and transparent sequence sharing","volume":"5","author":"E Dalla Vecchia","year":"2024","journal-title":"Lancet Microbe"},{"issue":"2","key":"pcbi.1014630.ref052","doi-asserted-by":"crossref","DOI":"10.1093\/ve\/veab064","article-title":"Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool","volume":"7","author":"\u00c1 O\u2019Toole","year":"2021","journal-title":"Virus Evol"},{"issue":"1","key":"pcbi.1014630.ref053","doi-asserted-by":"crossref","first-page":"7003","DOI":"10.1038\/s41467-022-33713-y","article-title":"Global disparities in SARS-CoV-2 genomic surveillance","volume":"13","author":"AF Brito","year":"2022","journal-title":"Nat Commun"},{"key":"pcbi.1014630.ref054","unstructured":"World Health Organization. Pneumonia of unknown cause \u2013 China; 2020. Accessed: 2026-03-04. https:\/\/www.who.int\/emergencies\/disease-outbreak-news\/item\/2020-DON229"},{"key":"pcbi.1014630.ref055","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/1741-7007-10-38","article-title":"Canalization of the evolutionary trajectory of the human influenza virus","volume":"10","author":"T Bedford","year":"2012","journal-title":"BMC Biol"},{"issue":"2","key":"pcbi.1014630.ref056","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1007683","article-title":"Early prediction of antigenic transitions for influenza A\/H3N2","volume":"16","author":"LA Castro","year":"2020","journal-title":"PLOS Computat Biol"},{"key":"pcbi.1014630.ref057","doi-asserted-by":"crossref","first-page":"7280","DOI":"10.1073\/pnas.082080899","article-title":"Agent-based modeling: methods and techniques for simulating human systems","volume":"99","author":"E Bonabeau","year":"2002","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1014630.ref058","volume-title":"Agent-based models","author":"N Gilbert","year":"2019"},{"issue":"2","key":"pcbi.1014630.ref059","first-page":"239","article-title":"A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code","volume":"21","author":"MD McKay","year":"1979","journal-title":"Technometrics"},{"issue":"24","key":"pcbi.1014630.ref060","doi-asserted-by":"crossref","first-page":"13910","DOI":"10.1073\/pnas.96.24.13910","article-title":"Mutation rates among RNA viruses","volume":"96","author":"JW Drake","year":"1999","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1014630.ref061","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1146\/annurev.ecolsys.110308.120248","article-title":"The Evolutionary Genetics of Emerging Viruses [Journal Article]","volume":"40","author":"EC Holmes","year":"2009","journal-title":"Annu Rev Ecol Evol Syst"},{"key":"pcbi.1014630.ref062","unstructured":"Beesley LJ, Murph AC, Osthus D, Castro LA. Transfer learning using 66 diseases for disease forecasting applications. 2026. https:\/\/arxiv.org\/abs\/2605.27269"},{"issue":"7553","key":"pcbi.1014630.ref063","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Y LeCun","year":"2015","journal-title":"Nature"},{"issue":"5","key":"pcbi.1014630.ref064","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"K Hornik","year":"1989","journal-title":"Neural Netw"},{"key":"pcbi.1014630.ref065","article-title":"Attention is all you need","volume":"30","author":"A Vaswani","year":"2017","journal-title":"Adv Neural Inform Process Syst"},{"key":"pcbi.1014630.ref066","first-page":"83","article-title":"Chapter 4 Nonuniform Random Variate Generation","volume-title":"Handbooks in Operations Research and Management Science","author":"L Devroye","year":"2006"},{"issue":"2","key":"pcbi.1014630.ref067","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1111\/j.1467-9868.2007.00587.x","article-title":"Probabilistic Forecasts, Calibration and Sharpness","volume":"69","author":"T Gneiting","year":"2007","journal-title":"J R Stat Soc B: Stat Methodol"},{"issue":"2","key":"pcbi.1014630.ref068","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1008618","article-title":"Evaluating epidemic forecasts in an interval format","volume":"17","author":"J Bracher","year":"2021","journal-title":"PLoS Comput Biol"},{"issue":"15","key":"pcbi.1014630.ref069","doi-asserted-by":"crossref","DOI":"10.1073\/pnas.2113561119","article-title":"Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States","volume":"119","author":"EY Cramer","year":"2022","journal-title":"Proc Natl Acad Sci U S A"}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1014630","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:39:40Z","timestamp":1787852380000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1014630"}},"subtitle":[],"editor":[{"given":"Benjamin M.","family":"Althouse","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,8,27]]},"references-count":69,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8,27]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1014630","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,27]]}}}