{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T11:10:43Z","timestamp":1775646643427,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T00:00:00Z","timestamp":1626134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Data-centric models of COVID-19 have been attempted, but have certain limitations. In this work, we propose an agent-based model of the epidemic in a confined space of agents representing humans. An extension to the SEIR model allows us to consider the difference between the appearance (black-box view) of the spread of disease and the real situation (glass-box view). Our model allows for simulations of lockdowns, social distancing, personal hygiene, quarantine, and hospitalization, with further considerations of different parameters, such as the extent to which hygiene and social distancing are observed in a population. Our results provide qualitative indications of the effects of various policies and parameters, for instance, that lockdowns by themselves are extremely unlikely to bring an end to an epidemic and may indeed make things worse, that social distancing is more important than personal hygiene, and that the growth of infection is significantly reduced for moderately high levels of social distancing and hygiene, even in the absence of herd immunity.<\/jats:p>","DOI":"10.3390\/systems9030053","type":"journal-article","created":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T04:26:06Z","timestamp":1626150366000},"page":"53","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Modeling the Spread and Control of COVID-19"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4976-491X","authenticated-orcid":false,"given":"Ashutosh","family":"Trivedi","sequence":"first","affiliation":[{"name":"Spext, Bengaluru 560053, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3703-9948","authenticated-orcid":false,"given":"Nanda Kishore","family":"Sreenivas","sequence":"additional","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0625-5103","authenticated-orcid":false,"given":"Shrisha","family":"Rao","sequence":"additional","affiliation":[{"name":"International Institute of Information Technology Bangalore, Bengaluru 560100, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,13]]},"reference":[{"key":"ref_1","unstructured":"Vynnycky, E., and White, R. (2010). An Introduction to Infectious Disease Modelling, Oxford University Press."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Keeling, M.J., and Rohani, P. (2007). Modeling Infectious Diseases in Humans and Animals, Princeton University Press.","DOI":"10.1515\/9781400841035"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Brauer, F., Castillo-Chavez, C., and Feng, Z. (2019). 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