Article
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The SIR and SEIR Epidemiological Models Revisited
Version 1
: Received: 2 May 2020 / Approved: 6 May 2020 / Online: 6 May 2020 (03:15:02 CEST)
How to cite: Maassen, J.P. The SIR and SEIR Epidemiological Models Revisited. Preprints 2020, 2020050090. https://doi.org/10.20944/preprints202005.0090.v1 Maassen, J.P. The SIR and SEIR Epidemiological Models Revisited. Preprints 2020, 2020050090. https://doi.org/10.20944/preprints202005.0090.v1
Abstract
We review and assess the classic SIR and SEIR epidemiological models regarding possible applications to the COVID-19 pandemic. In spite of numerous more complicated models, we show how the qualitative features of the solution to the SIR and SEIR models continue to provide valuable public health insights in some scenarios. Using estimated COVID-19 data as of this date, the SEIR model shows that if it were possible to reduce R0 from 2.5 to 1.25 through social distancing and other measures, the maximum fraction of the population that would become infected at any particular time would drop from 17% to 4%, provided that all of the model assumptions are satisfied. Finally, we compare the classic SIR model with a recent stochastic model with favorable results. Since this comparison underscores the importance of underlying connectivity assumptions, we conclude with Monte-Carlo simulations with specific connectivity that reproduce the classical SIR model with standard incidence.
Supplementary and Associated Material
Keywords
epidemiological model; basic reproduction number; SIR; SEIR; COVID-19; stochas-tic model; Monte-Carlo simulation
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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