{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T06:24:27Z","timestamp":1772864667858,"version":"3.50.1"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>COVID-19 forecasting models have been critical in guiding decision-making on surveillance testing, social distancing, and vaccination requirements. Beyond influencing public health policies, an accurate COVID-19 forecasting model can impact community spread by enabling employers and university leaders to adapt worksite policies and practices to contain or mitigate outbreaks. While many such models have been developed for COVID-19 forecasting at the national, state, county, or city level, only a few models have been developed for workplaces and universities. Furthermore, COVID-19 forecasting models have rarely been validated against real COVID-19 case data. Here we present the systematic parameter fitting and validation of an agent-based compartment model for the forecasting of daily COVID-19 cases in single-site workplaces and universities with real-world data. Our approaches include manual fitting, where initial model parameters are chosen based on historical data, and automated fitting, where parameters are chosen based on candidate case trajectory simulations that result in best fit to prevalence estimation data. We use a 14-day fitting window and validate our approaches on 7- and 14-day testing windows with real COVID-19 case data from one employer. Our manual and automated fitting approaches accurately predicted COVID-19 case trends and outperformed the baseline model (no parameter fitting) across multiple scenarios, including a rising case trajectory (RMSLE values: 2.627 for baseline, 0.562 for manual fitting, 0.399 for automated fitting) and a decreasing case trajectory (RMSLE values: 1.155 for baseline, 0.537 for manual fitting, 0.778 for automated fitting). Our COVID-19 case forecasting model allows decision-makers at workplaces and universities to proactively respond to case trend forecasts, mitigate outbreaks, and promote safety.<\/jats:p>","DOI":"10.1371\/journal.pone.0283517","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T18:04:17Z","timestamp":1679594657000},"page":"e0283517","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":6,"title":["Fitting and validation of an agent-based model for COVID-19 case forecasting in workplaces and universities"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9086-2397","authenticated-orcid":true,"given":"Vignesh","family":"Kumaresan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8961-2542","authenticated-orcid":true,"given":"Niranjan","family":"Balachandar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0528-0577","authenticated-orcid":true,"given":"Sarah F.","family":"Poole","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lance J.","family":"Myers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paul","family":"Varghese","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vindell","family":"Washington","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yugang","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivian S.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"issue":"1","key":"pone.0283517.ref001","doi-asserted-by":"crossref","DOI":"10.4081\/jphr.2020.1786","article-title":"COVID-19 pandemic: Emerging perspectives and future trends","volume":"9","author":"S. A. Tabish","year":"2020","journal-title":"Journal of public health research"},{"key":"pone.0283517.ref002","first-page":"20","article-title":"COVID-19 weekly epidemiological update","volume":"56","author":"World Health Organization","year":"2022"},{"issue":"2","key":"pone.0283517.ref003","first-page":"133","article-title":"Deaths from COVID-19","volume":"325","author":"H. K. Koh","year":"2021","journal-title":"Jama"},{"key":"pone.0283517.ref004","article-title":"The impact of COVID-19 pandemic on conventional work settings","author":"R. Diab-Bahman","year":"2020","journal-title":"International Journal of Sociology and Social Policy"},{"issue":"1","key":"pone.0283517.ref005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11079-021-09638-2","article-title":"The economic effects of Covid-19 containment measures","volume":"33","author":"P. Deb","year":"2022","journal-title":"Open Economies Review"},{"issue":"2","key":"pone.0283517.ref006","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1177\/0973703020946700","article-title":"Impact of Covid-19 pandemic on higher education and research","volume":"14","author":"S. Rashid","year":"2020","journal-title":"Indian Journal of Human Development"},{"issue":"1","key":"pone.0283517.ref007","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1177\/2347631120983481","article-title":"A literature review on impact of COVID-19 pandemic on teaching and learning","volume":"8","author":"S. Pokhrel","year":"2021","journal-title":"Higher Education for the Future"},{"issue":"1","key":"pone.0283517.ref008","first-page":"9","article-title":"COVID-19 Pandemic and Possible Trends for the Future of Higher Education","volume":"8","author":"A. Kara","year":"2021","journal-title":"A Review. Journal of Education and Educational Development"},{"issue":"1","key":"pone.0283517.ref009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12916-020-01628-4","article-title":"Epidemiological models are important tools for guiding COVID-19 interventions","volume":"18","author":"R. N. Thompson","year":"2020","journal-title":"BMC medicine"},{"key":"pone.0283517.ref010","article-title":"Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal","volume":"369","author":"L. Wynants","year":"2020","journal-title":"bmj"},{"key":"pone.0283517.ref011","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.idm.2021.01.001","article-title":"COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models","volume":"6","author":"Y. Xiang","year":"2021","journal-title":"Infectious Disease Modelling"},{"issue":"10","key":"pone.0283517.ref012","doi-asserted-by":"crossref","first-page":"e498","DOI":"10.1016\/S2589-7500(20)30196-5","article-title":"Applications of predictive modelling early in the COVID-19 epidemic","volume":"2","author":"C. Poletto","year":"2020","journal-title":"The Lancet Digital Health"},{"key":"pone.0283517.ref013","first-page":"1","article-title":"A review on COVID-19 forecasting models","author":"I. Rahimi","year":"2021","journal-title":"Neural Computing and Applications"},{"issue":"8","key":"pone.0283517.ref014","doi-asserted-by":"crossref","first-page":"e0254798","DOI":"10.1371\/journal.pone.0254798","article-title":"A holistic approach for suppression of COVID-19 spread in workplaces and universities","volume":"16","author":"S. F. Poole","year":"2021","journal-title":"PloS one"},{"issue":"1","key":"pone.0283517.ref015","first-page":"1","article-title":"Age-stratified discrete compartment model of the COVID-19 epidemic with application to Switzerland","volume":"10","author":"F. Balabdaoui","year":"2020","journal-title":"Scientific"},{"issue":"3","key":"pone.0283517.ref016","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1007\/s11071-020-05743-y","article-title":"SEIR modeling of the COVID-19 and its dynamics","volume":"101","author":"S. He","year":"2020","journal-title":"Nonlinear dynamics"},{"key":"pone.0283517.ref017","doi-asserted-by":"crossref","first-page":"103746","DOI":"10.1016\/j.rinp.2020.103746","article-title":"A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: simulating control scenarios and multi-scale epidemics","volume":"21","author":"L. L\u00f3pez","year":"2021","journal-title":"Results in Physics"},{"issue":"11","key":"pone.0283517.ref018","doi-asserted-by":"crossref","first-page":"e0241951","DOI":"10.1371\/journal.pone.0241951","article-title":"Beyond the peak: A deterministic compartment model for exploring the Covid-19 evolution in Italy","volume":"15","author":"S. Romano","year":"2020","journal-title":"PloS one"},{"issue":"4","key":"pone.0283517.ref019","doi-asserted-by":"crossref","first-page":"3279","DOI":"10.1109\/TNSE.2020.3024723","article-title":"A time-dependent SIR model for COVID-19 with undetectable infected persons","volume":"7","author":"Y. C. Chen","year":"2020","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"pone.0283517.ref020","doi-asserted-by":"crossref","unstructured":"Calafiore, G. C., Novara, C., & Possieri, C. (2020, December). A modified SIR model for the COVID-19 contagion in Italy. In 2020 59th IEEE Conference on Decision and Control (CDC) (pp. 3889\u20133894). IEEE.","DOI":"10.1109\/CDC42340.2020.9304142"},{"issue":"6","key":"pone.0283517.ref021","doi-asserted-by":"crossref","first-page":"636","DOI":"10.3390\/math9060636","article-title":"An extended SEIR model with vaccination for forecasting the COVID-19 pandemic in Saudi Arabia using an ensemble Kalman filter","volume":"9","author":"R. Ghostine","year":"2021","journal-title":"Mathematics"},{"issue":"7","key":"pone.0283517.ref022","doi-asserted-by":"crossref","first-page":"e1009149","DOI":"10.1371\/journal.pcbi.1009149","article-title":"Covasim: an agent-based model of COVID-19 dynamics and interventions","volume":"17","author":"C. C. Kerr","year":"2021","journal-title":"PLOS Computational Biology"},{"issue":"9","key":"pone.0283517.ref023","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1038\/s41591-020-1001-6","article-title":"A stochastic agent-based model of the SARS-CoV-2 epidemic in France.","volume":"26","author":"N. Hoertel","year":"2020","journal-title":"Nature medicine"},{"issue":"7","key":"pone.0283517.ref024","doi-asserted-by":"crossref","first-page":"e1009146","DOI":"10.1371\/journal.pcbi.1009146","article-title":"OpenABM-Covid19\u2014An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing","volume":"17","author":"R. Hinch","year":"2021","journal-title":"PLoS computational biology"},{"key":"pone.0283517.ref025","first-page":"1","article-title":"An agent-based modeling of COVID-19: validation, analysis, and recommendations","author":"M. Shamil","year":"2021","journal-title":"Cognitive computation"},{"issue":"4","key":"pone.0283517.ref026","doi-asserted-by":"crossref","first-page":"e24192","DOI":"10.2196\/24192","article-title":"An agent-based model of the local spread of SARS-CoV-2: Modeling study","volume":"9","author":"A. Staffini","year":"2021","journal-title":"JMIR medical informatics"},{"issue":"3","key":"pone.0283517.ref027","doi-asserted-by":"crossref","first-page":"e24630","DOI":"10.2196\/24630","article-title":"A Full-Scale Agent-Based Model to Hypothetically Explore the Impact of Lockdown, Social Distancing, and Vaccination During the COVID-19 Pandemic in Lombardy, ItalyModel Development.","volume":"2","author":"G. Giacopelli","year":"2021","journal-title":"JMIRx med"},{"issue":"1","key":"pone.0283517.ref028","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13662-020-02762-2","article-title":"A fractional differential equation model for the COVID-19 transmission by using the Caputo\u2013Fabrizio derivative","volume":"2020","author":"D. Baleanu","year":"2020","journal-title":"Advances in difference equations"},{"issue":"1","key":"pone.0283517.ref029","doi-asserted-by":"crossref","first-page":"18","DOI":"10.3390\/axioms10010018","article-title":"Modeling and forecasting of COVID-19 spreading by delayed stochastic differential equations","volume":"10","author":"M. Mahrouf","year":"2021","journal-title":"Axioms"},{"key":"pone.0283517.ref030","author":"X. Liu","year":"2020","journal-title":"Modelling the situation of COVID-19 and effects of different containment strategies in China with dynamic differential equations and parameters estimation"},{"issue":"4","key":"pone.0283517.ref031","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1007\/s41745-020-00200-6","article-title":"Mathematical models for covid-19 pandemic: a comparative analysis","volume":"100","author":"A. Adiga","year":"2020","journal-title":"Journal of the Indian Institute of Science"},{"issue":"19","key":"pone.0283517.ref032","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1001\/jama.2020.6585","article-title":"Predictive mathematical models of the COVID-19 pandemic: underlying principles and value of projections","volume":"323","author":"N. P. Jewell","year":"2020","journal-title":"Jama"},{"key":"pone.0283517.ref033","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.idm.2020.12.008","article-title":"On the reliability of predictions on Covid-19 dynamics: a systematic and critical review of modelling techniques","volume":"6","author":"J. E. Gnanvi","year":"2021","journal-title":"Infectious Disease Modelling"},{"key":"pone.0283517.ref034","doi-asserted-by":"crossref","first-page":"108436","DOI":"10.1016\/j.mbs.2020.108436","article-title":"Simulating COVID-19 in a university environment","volume":"328","author":"P. T. Gressman","year":"2020","journal-title":"Mathematical biosciences"},{"key":"pone.0283517.ref035","article-title":"A model of COVID-19 transmission and control on university campuses","author":"B. Lopman","year":"2020","journal-title":"MedRxiv"},{"key":"pone.0283517.ref036","article-title":"Modelling testing frequencies required for early detection of a SARS-CoV-2 outbreak on a university campus","author":"N. K. Martin","year":"2020","journal-title":"medRxiv"},{"key":"pone.0283517.ref037","first-page":"1","article-title":"Assessment of the COVID-19 infection risk at a workplace through stochastic microexposure modeling","author":"S. Vecherin","year":"2022","journal-title":"Journal of exposure science & environmental epidemiology"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0283517","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T18:04:55Z","timestamp":1679594695000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0283517"}},"subtitle":[],"editor":[{"given":"Abdullah Hussein Abdullah","family":"Alamoodi","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2023,3,23]]},"references-count":37,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,3,23]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0283517","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,23]]}}}