{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:11:26Z","timestamp":1760145086862,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Computational simulation models have been widely used to study the dynamics of COVID-19. Among those, bottom-up approaches such as agent-based models (ABMs) can account for population heterogeneity. While many studies have addressed COVID-19 spread at various scales, insufficient studies have investigated the spread of COVID-19 within closed indoor settings. This study aims to develop an ABM to simulate the spread of COVID-19 in a closed indoor setting using three transmission sub-models. Moreover, a comprehensive sensitivity analysis encompassing 4374 scenarios is performed. The model is calibrated using data from Calabria, Italy. The results indicated a decent consistency between the observed and predicted number of infected people (MAPE = 27.94%, RMSE = 0.87 and \u03c72(1,N=34)=(44.11,p=0.11)). Notably, the transmission distance was identified as the most influential parameter in this model. In nearly all scenarios, this parameter had a significant impact on the outbreak dynamics (total cases and epidemic peak). Also, the calibration process showed that the movement of agents and the number of initial asymptomatic agents are vital model parameters to simulate COVID-19 spread accurately. The developed model may provide useful insights to investigate different scenarios and dynamics of other similar infectious diseases in closed indoor settings.<\/jats:p>","DOI":"10.3390\/info15060362","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T08:06:06Z","timestamp":1718784366000},"page":"362","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling COVID-19 Transmission in Closed Indoor Settings: An Agent-Based Approach with Comprehensive Sensitivity Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Amir Hossein","family":"Ebrahimi","sequence":"first","affiliation":[{"name":"Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19967-15433, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9537-9401","authenticated-orcid":false,"given":"Ali Asghar","family":"Alesheikh","sequence":"additional","affiliation":[{"name":"Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology, Tehran 19967-15433, Iran"}]},{"given":"Navid","family":"Hooshangi","sequence":"additional","affiliation":[{"name":"Department of Surveying Engineering, College of Earth Sciences Engineering, Arak University of Technology, Arak 38181-46763, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1241-007X","authenticated-orcid":false,"given":"Mohammad","family":"Sharif","sequence":"additional","affiliation":[{"name":"Institute of Mobility and Urban Planning, University of Duisburg-Essen, 45127 Essen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5092-0698","authenticated-orcid":false,"given":"Abolfazl","family":"Mollalo","sequence":"additional","affiliation":[{"name":"Biomedical Informatics Center, Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC 29425, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, K., Li, Y., Zhou, R., and Jiang, X. 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