{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T02:39:16Z","timestamp":1779331156574,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012021","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000}}],"reference-count":59,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003006","name":"Eidgen\u00f6ssische Technische Hochschule Z\u00fcrich","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003006","name":"Eidgen\u00f6ssische Technische Hochschule Z\u00fcrich","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["200901\/Z\/16\/Z"],"award-info":[{"award-number":["200901\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002329","name":"Bundesamt f\u00fcr Gesundheit","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>The time-varying effective reproduction number <jats:italic>R<\/jats:italic><jats:sub><jats:italic>t<\/jats:italic><\/jats:sub> is a widely used indicator of transmission dynamics during infectious disease outbreaks. Timely estimates of <jats:italic>R<\/jats:italic><jats:sub><jats:italic>t<\/jats:italic><\/jats:sub> can be obtained from reported cases counted by their date of symptom onset, which is generally closer to the time of infection than the date of report. Case counts by date of symptom onset are typically obtained from line list data, however these data can have missing information and are subject to right truncation. Previous methods have addressed these problems independently by first imputing missing onset dates, then adjusting truncated case counts, and finally estimating the effective reproduction number. This stepwise approach makes it difficult to propagate uncertainty and can introduce subtle biases during real-time estimation due to the continued impact of assumptions made in previous steps. In this work, we integrate imputation, truncation adjustment, and <jats:italic>R<\/jats:italic><jats:sub><jats:italic>t<\/jats:italic><\/jats:sub> estimation into a single generative Bayesian model, allowing direct joint inference of case counts and <jats:italic>R<\/jats:italic><jats:sub><jats:italic>t<\/jats:italic><\/jats:sub> from line list data with missing symptom onset dates. We then use this framework to compare the performance of nowcasting approaches with different stepwise and generative components on synthetic line list data for multiple outbreak scenarios and across different epidemic phases. We find that under reporting delays realistic for hospitalization data (50% of reports delayed by more than a week), intermediate smoothing, as is common practice in stepwise approaches, can bias nowcasts of case counts and <jats:italic>R<\/jats:italic><jats:sub><jats:italic>t<\/jats:italic><\/jats:sub>, which is avoided in a joint generative approach due to shared regularization of all model components. On incomplete line list data, a fully generative approach enables the quantification of uncertainty due to missing onset dates without the need for an initial multiple imputation step. In a real-world comparison using hospitalization line list data from the COVID-19 pandemic in Switzerland, we observe the same qualitative differences between approaches. The generative modeling components developed in this work have been integrated and further extended in the R package epinowcast, providing a flexible and interpretable tool for real-time surveillance.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012021","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T18:30:55Z","timestamp":1713292255000},"page":"e1012021","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":16,"title":["Generative Bayesian modeling to nowcast the effective reproduction number from line list data with missing symptom onset dates"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6822-8437","authenticated-orcid":true,"given":"Adrian","family":"Lison","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8057-8037","authenticated-orcid":true,"given":"Sam","family":"Abbott","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1782-8109","authenticated-orcid":true,"given":"Jana","family":"Huisman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanja","family":"Stadler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"pcbi.1012021.ref001","doi-asserted-by":"crossref","first-page":"e71345","DOI":"10.7554\/eLife.71345","article-title":"Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2","volume":"11","author":"JS Huisman","year":"2022","journal-title":"eLife"},{"key":"pcbi.1012021.ref002","doi-asserted-by":"crossref","first-page":"112","DOI":"10.12688\/wellcomeopenres.16006.1","article-title":"Estimating the time-varying reproduction number of SARS-CoV-2 using national and subnational case counts","volume":"5","author":"S Abbott","year":"2020","journal-title":"Wellcome Open Research"},{"issue":"9","key":"pcbi.1012021.ref003","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1177\/09622802211037079","article-title":"Commentary on the use of the reproduction number R during the COVID-19 pandemic","volume":"31","author":"C Vegvari","year":"2022","journal-title":"Statistical Methods in Medical Research"},{"issue":"2","key":"pcbi.1012021.ref004","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S1473-3099(20)30785-4","article-title":"The Temporal Association of Introducing and Lifting Non-Pharmaceutical Interventions with the Time-Varying Reproduction Number (R) of SARS-CoV-2: A Modelling Study across 131 Countries","volume":"21","author":"Y Li","year":"2021","journal-title":"The Lancet Infectious Diseases"},{"issue":"10","key":"pcbi.1012021.ref005","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s10654-022-00908-y","article-title":"The Methodologies to Assess the Effectiveness of Non-Pharmaceutical Interventions during COVID-19: A Systematic Review","volume":"37","author":"N Banholzer","year":"2022","journal-title":"European Journal of Epidemiology"},{"issue":"1829","key":"pcbi.1012021.ref006","doi-asserted-by":"crossref","first-page":"20200283","DOI":"10.1098\/rstb.2020.0283","article-title":"Exploring Surveillance Data Biases When Estimating the Reproduction Number: With Insights into Subpopulation Transmission of COVID-19 in England","volume":"376","author":"K Sherratt","year":"2021","journal-title":"Philosophical Transactions of the Royal Society B: Biological Sciences"},{"issue":"12","key":"pcbi.1012021.ref007","doi-asserted-by":"crossref","first-page":"e1008409","DOI":"10.1371\/journal.pcbi.1008409","article-title":"Practical considerations for measuring the effective reproductive number, Rt","volume":"16","author":"KM Gostic","year":"2020","journal-title":"PLOS Computational Biology"},{"issue":"9","key":"pcbi.1012021.ref008","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1093\/aje\/kwt133","article-title":"A new framework and software to estimate time-varying reproduction numbers during epidemics","volume":"178","author":"A Cori","year":"2013","journal-title":"American Journal of Epidemiology"},{"issue":"12","key":"pcbi.1012021.ref009","doi-asserted-by":"crossref","first-page":"e047623","DOI":"10.1136\/bmjopen-2020-047623","article-title":"COVID-19 surveillance data quality issues: a national consecutive case series","volume":"11","author":"C Costa-Santos","year":"2021","journal-title":"BMJ Open"},{"issue":"7844","key":"pcbi.1012021.ref010","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/s41586-020-03095-6","article-title":"Underdetection of cases of COVID-19 in France threatens epidemic control","volume":"590","author":"G Pullano","year":"2021","journal-title":"Nature"},{"issue":"1","key":"pcbi.1012021.ref011","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/1742-5573-7-12","article-title":"Reporting Errors in Infectious Disease Outbreaks, with an Application to Pandemic Influenza A\/H1N1","volume":"7","author":"LF White","year":"2010","journal-title":"Epidemiologic Perspectives & Innovations"},{"issue":"5","key":"pcbi.1012021.ref012","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1097\/EDE.0000000000001050","article-title":"Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing","volume":"30","author":"J van de Kassteele","year":"2019","journal-title":"Epidemiology (Cambridge, Mass)"},{"key":"pcbi.1012021.ref013","doi-asserted-by":"crossref","DOI":"10.1002\/9781118032985","volume-title":"The Statistical Analysis of Failure Time Data","author":"JD Kalbfleisch","year":"2002","edition":"2"},{"key":"pcbi.1012021.ref014","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1111\/biom.12194","article-title":"Bayesian nowcasting during the STEC O104:H4 outbreak in Germany, 2011","volume":"70","author":"M H\u00f6hle","year":"2014","journal-title":"Biometrics"},{"issue":"22","key":"pcbi.1012021.ref015","doi-asserted-by":"crossref","first-page":"4363","DOI":"10.1002\/sim.8303","article-title":"A modelling approach for correcting reporting delays in disease surveillance data","volume":"38","author":"LS Bastos","year":"2019","journal-title":"Statistics in Medicine"},{"issue":"9","key":"pcbi.1012021.ref016","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1038\/s43588-022-00313-1","article-title":"Quantifying the information in noisy epidemic curves","volume":"2","author":"KV Parag","year":"2022","journal-title":"Nature Computational Science"},{"issue":"3","key":"pcbi.1012021.ref017","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1002\/bimj.202000112","article-title":"Nowcasting the COVID-19 pandemic in Bavaria","volume":"63","author":"F G\u00fcnther","year":"2021","journal-title":"Biometrical Journal"},{"issue":"3","key":"pcbi.1012021.ref018","doi-asserted-by":"crossref","first-page":"e1009964","DOI":"10.1371\/journal.pcbi.1009964","article-title":"Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data","volume":"18","author":"PMD Salazar","year":"2022","journal-title":"PLOS Computational Biology"},{"issue":"7","key":"pcbi.1012021.ref019","doi-asserted-by":"crossref","first-page":"e1009210","DOI":"10.1371\/journal.pcbi.1009210","article-title":"Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic","volume":"17","author":"T Li","year":"2021","journal-title":"PLOS Computational Biology"},{"key":"pcbi.1012021.ref020","unstructured":"Gelman A, Vehtari A, Simpson D, Margossian CC, Carpenter B, Yao Y, et al. Bayesian Workflow. arXiv:2011.01808v1 [Preprint]. 2020 [submitted 2020 Nov 3, cited 2024 Mar 29]. Available from: https:\/\/arxiv.org\/abs\/2011.01808v1"},{"key":"pcbi.1012021.ref021","doi-asserted-by":"crossref","unstructured":"Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis. Chapman & Hall \/ CRC Texts in Statistical Science; 2013.","DOI":"10.1201\/b16018"},{"issue":"8","key":"pcbi.1012021.ref022","doi-asserted-by":"crossref","first-page":"100310","DOI":"10.1016\/j.patter.2021.100310","article-title":"Accounting for uncertainty during a pandemic","volume":"2","author":"J Zelner","year":"2021","journal-title":"Patterns"},{"issue":"8","key":"pcbi.1012021.ref023","doi-asserted-by":"crossref","first-page":"e758","DOI":"10.1371\/journal.pone.0000758","article-title":"Estimating Individual and Household Reproduction Numbers in an Emerging Epidemic","volume":"2","author":"C Fraser","year":"2007","journal-title":"PLOS ONE"},{"issue":"6","key":"pcbi.1012021.ref024","doi-asserted-by":"crossref","first-page":"3258","DOI":"10.1137\/18M1186411","article-title":"Equivalence of the Erlang-Distributed SEIR Epidemic Model and the Renewal Equation","volume":"78","author":"D Champredon","year":"2018","journal-title":"SIAM Journal on Applied Mathematics"},{"issue":"1","key":"pcbi.1012021.ref025","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1186\/s12859-023-05428-4","article-title":"estimateR: An R Package to Estimate and Monitor the Effective Reproductive Number","volume":"24","author":"J Scire","year":"2023","journal-title":"BMC Bioinformatics"},{"key":"pcbi.1012021.ref026","unstructured":"Sam Abbott, Joel Hellewell, Katharine Sherratt, Katelyn Gostic, Joe Hickson, Hamada S Badr, et al. EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters; 2024 [cited 2024 Mar 29]. Repository: github [Internet] Available from: https:\/\/github.com\/epiforecasts\/EpiNow2."},{"key":"pcbi.1012021.ref027","unstructured":"Scott JA, Gandy A, Mishra S, Unwin J, Flaxman S, Bhatt S. Epidemia: Modeling of Epidemics Using Hierarchical Bayesian Models; 2021 [cited 2024 Mar 29]. Repository: github [Internet] Available from: https:\/\/github.com\/ImperialCollegeLondon\/epidemia."},{"key":"pcbi.1012021.ref028","unstructured":"Bhatt S, Ferguson N, Flaxman S, Gandy A, Mishra S, Scott JA. Semi-Mechanistic Bayesian Modeling of COVID-19 with Renewal Processes. arXiv:2012.00394v2 [Preprint]. 2020 [submitted 2020 Dec 1, revised 2020 Dec 29, cited 2024 Mar 29]. Available from: https:\/\/arxiv.org\/abs\/2012.00394v2"},{"issue":"7820","key":"pcbi.1012021.ref029","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/s41586-020-2405-7","article-title":"Estimating the Effects of Non-Pharmaceutical Interventions on COVID-19 in Europe","volume":"584","author":"S Flaxman","year":"2020","journal-title":"Nature"},{"issue":"Supplement_1","key":"pcbi.1012021.ref030","doi-asserted-by":"crossref","first-page":"S65","DOI":"10.1111\/rssa.12971","article-title":"Efficient Bayesian Inference of Instantaneous Reproduction Numbers at Fine Spatial Scales, with an Application to Mapping and Nowcasting the Covid-19 Epidemic in British Local Authorities","volume":"185","author":"YW Teh","year":"2022","journal-title":"Journal of the Royal Statistical Society Series A: Statistics in Society"},{"issue":"1821","key":"pcbi.1012021.ref031","doi-asserted-by":"crossref","first-page":"20152026","DOI":"10.1098\/rspb.2015.2026","article-title":"Intrinsic and realized generation intervals in infectious-disease transmission","volume":"282","author":"D Champredon","year":"2015","journal-title":"Proceedings of the Royal Society B: Biological Sciences"},{"issue":"6","key":"pcbi.1012021.ref032","doi-asserted-by":"crossref","first-page":"e0252827","DOI":"10.1371\/journal.pone.0252827","article-title":"Estimating the Effects of Non-Pharmaceutical Interventions on the Number of New Infections with COVID-19 during the First Epidemic Wave","volume":"16","author":"N Banholzer","year":"2021","journal-title":"PLOS ONE"},{"key":"pcbi.1012021.ref033","first-page":"12175","article-title":"How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?","volume":"33","author":"M Sharma","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"9","key":"pcbi.1012021.ref034","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1177\/09622802211023955","article-title":"Estimating a Time-to-Event Distribution from Right-Truncated Data in an Epidemic: A Review of Methods","volume":"31","author":"SR Seaman","year":"2022","journal-title":"Statistical Methods in Medical Research"},{"issue":"4","key":"pcbi.1012021.ref035","doi-asserted-by":"crossref","first-page":"e1007735","DOI":"10.1371\/journal.pcbi.1007735","article-title":"Nowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking","volume":"16","author":"SF McGough","year":"2020","journal-title":"PLOS Computational Biology"},{"issue":"3","key":"pcbi.1012021.ref036","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1111\/biom.13188","article-title":"Multivariate Hierarchical Frameworks for Modeling Delayed Reporting in Count Data","volume":"76","author":"O Stoner","year":"2020","journal-title":"Biometrics"},{"issue":"2","key":"pcbi.1012021.ref037","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression Models and Life-Tables","volume":"34","author":"DR Cox","year":"1972","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"12","key":"pcbi.1012021.ref038","doi-asserted-by":"crossref","first-page":"e1010767","DOI":"10.1371\/journal.pcbi.1010767","article-title":"Bayesian Nowcasting with Leading Indicators Applied to COVID-19 Fatalities in Sweden","volume":"18","author":"F Bergstr\u00f6m","year":"2022","journal-title":"PLOS Computational Biology"},{"key":"pcbi.1012021.ref039","unstructured":"Hawryluk I, Hoeltgebaum H, Mishra S, Miscouridou X, Schnekenberg RP, Whittaker C, et al. Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting. In: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. PMLR; 2021. p. 1258\u20131268."},{"key":"pcbi.1012021.ref040","doi-asserted-by":"crossref","first-page":"w20307","DOI":"10.4414\/smw.2020.20307","article-title":"A pitfall in estimating the effective reproductive number Rt for COVID-19","volume":"150","author":"M Petermann","year":"2020","journal-title":"Swiss Medical Weekly"},{"key":"pcbi.1012021.ref041","unstructured":"Stan development team. Stan Modeling Language Users Guide and Reference Manual, Version 2.31; 2022 [cited 2024 Mar 29]. Manual [Internet] Available from: https:\/\/mc-stan.org."},{"key":"pcbi.1012021.ref042","unstructured":"Gabry J, \u010ce\u0161novar R. CmdStanR: R Interface to \u2018CmdStan\u2019; 2022 [cited 2024 Mar 29]. Repository: github [Internet] Available from: https:\/\/github.com\/stan-dev\/cmdstanr."},{"issue":"4","key":"pcbi.1012021.ref043","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1214\/ss\/1177011136","article-title":"Inference from iterative simulation using multiple sequences","volume":"7","author":"A Gelman","year":"1992","journal-title":"Statistical Science"},{"key":"pcbi.1012021.ref044","doi-asserted-by":"crossref","DOI":"10.1201\/b10905-2","volume-title":"Introduction to Markov Chain Monte Carlo","author":"C Geyer","year":"2011"},{"key":"pcbi.1012021.ref045","unstructured":"Bosse NI, Gruson H, Cori A, van Leeuwen E, Funk S, Abbott S. Evaluating Forecasts with Scoringutils in R. arXiv:2205.07090v1 [Preprint]. 2022 [submitted 2022 May 14, cited 2024 Mar 29]. Available from: https:\/\/arxiv.org\/abs\/2205.07090v1"},{"issue":"2","key":"pcbi.1012021.ref046","doi-asserted-by":"crossref","first-page":"538","DOI":"10.3390\/jcm9020538","article-title":"Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data","volume":"9","author":"NM Linton","year":"2020","journal-title":"Journal of Clinical Medicine"},{"key":"pcbi.1012021.ref047","doi-asserted-by":"crossref","first-page":"e70767","DOI":"10.7554\/eLife.70767","article-title":"Inference of the SARS-CoV-2 Generation Time Using UK Household Data","volume":"11","author":"WS Hart","year":"2022","journal-title":"eLife"},{"issue":"10247","key":"pcbi.1012021.ref048","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/S0140-6736(20)31304-0","article-title":"Seroprevalence of Anti-SARS-CoV-2 IgG Antibodies in Geneva, Switzerland (SEROCoV-POP): A Population-Based Study","volume":"396","author":"S Stringhini","year":"2020","journal-title":"The Lancet"},{"issue":"2","key":"pcbi.1012021.ref049","doi-asserted-by":"crossref","first-page":"e1008618","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 Computational Biology"},{"issue":"477","key":"pcbi.1012021.ref050","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1198\/016214506000001437","article-title":"Strictly Proper Scoring Rules, Prediction, and Estimation","volume":"102","author":"T Gneiting","year":"2007","journal-title":"Journal of the American Statistical Association"},{"key":"pcbi.1012021.ref051","doi-asserted-by":"crossref","first-page":"e5","DOI":"10.1017\/S0950268822001947","article-title":"Estimation of the Incubation Period and Generation Time of SARS-CoV-2 Alpha and Delta Variants from Contact Tracing Data","volume":"151","author":"M Manica","year":"2023","journal-title":"Epidemiology & Infection"},{"issue":"5","key":"pcbi.1012021.ref052","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/S1473-3099(22)00001-9","article-title":"Generation Time of the Alpha and Delta SARS-CoV-2 Variants: An Epidemiological Analysis","volume":"22","author":"WS Hart","year":"2022","journal-title":"The Lancet Infectious Diseases"},{"key":"pcbi.1012021.ref053","volume-title":"Forecasting: principles and practice","author":"RJ Hyndman","year":"2018","edition":"2"},{"key":"pcbi.1012021.ref054","volume-title":"Adaptive computation and machine learning","author":"CE Rasmussen","year":"2006"},{"issue":"1","key":"pcbi.1012021.ref055","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1038\/s41467-021-21776-2","article-title":"Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing","volume":"12","author":"K Leung","year":"2021","journal-title":"Nature Communications"},{"issue":"10","key":"pcbi.1012021.ref056","doi-asserted-by":"crossref","first-page":"2100374","DOI":"10.2807\/1560-7917.ES.2022.27.10.2100374","article-title":"Estimating the effect of mobility on SARS-CoV-2 transmission during the first and second wave of the COVID-19 epidemic, Switzerland, March to December 2020","volume":"27","author":"A Lison","year":"2022","journal-title":"Eurosurveillance"},{"key":"pcbi.1012021.ref057","unstructured":"Abbott S, Lison A, Funk S, Pearson C, Gruson H, Guenther F. Epinowcast: Flexible Hierarchical Nowcasting; 2024 [cited 2024 Mar 29]. Repository: github [Internet] Available from: https:\/\/github.com\/epinowcast\/epinowcast."},{"issue":"9","key":"pcbi.1012021.ref058","doi-asserted-by":"crossref","first-page":"e1010405","DOI":"10.1371\/journal.pcbi.1010405","article-title":"Comparing human and model-based forecasts of COVID-19 in Germany and Poland","volume":"18","author":"NI Bosse","year":"2022","journal-title":"PLOS Computational Biology"},{"issue":"5","key":"pcbi.1012021.ref059","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1097\/EDE.0000000000001050","article-title":"Nowcasting the Number of New Symptomatic Cases During Infectious Disease Outbreaks Using Constrained P-spline Smoothing","volume":"30","author":"J van de Kassteele","year":"2019","journal-title":"Epidemiology (Cambridge, Mass)"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1012021","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012021","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T17:41:29Z","timestamp":1714153289000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1012021"}},"subtitle":[],"editor":[{"given":"Tom","family":"Britton","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,4,16]]},"references-count":59,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,4,16]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1012021","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1012021","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,16]]}}}