Non-analytic behaviour in large-deviations of the susceptible-infected-recovered model under the influence of lockdowns
We numerically investigate the dynamics of an SIR model with infection level-based lockdowns on Small-World networks. Using a large-deviation approach, namely the Wang–Landau algorithm, we study the distribution of the cumulative fraction of infected individuals. We are able to resolve the density o...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2023-01-01
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| Series: | New Journal of Physics |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1367-2630/ad0991 |
| _version_ | 1797542472795029504 |
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| author | Leo Patrick Mulholland Yannick Feld Alexander K Hartmann |
| author_facet | Leo Patrick Mulholland Yannick Feld Alexander K Hartmann |
| author_sort | Leo Patrick Mulholland |
| collection | DOAJ |
| description | We numerically investigate the dynamics of an SIR model with infection level-based lockdowns on Small-World networks. Using a large-deviation approach, namely the Wang–Landau algorithm, we study the distribution of the cumulative fraction of infected individuals. We are able to resolve the density of states for values as low as 10 ^−85 . Hence, we measure the distribution on its full support giving a complete characterization of this quantity. The lockdowns are implemented by severing a certain fraction of the edges in the Small-World network, and are initiated and released at different levels of infection, which are varied within this study. We observe points of non-analytical behaviour for the pdf and discontinuous transitions for correlations with other quantities such as the maximum fraction of infected and the duration of outbreaks. Further, empirical rate functions were calculated for different system sizes, for which a convergence is clearly visible indicating that the large-deviation principle is valid for the system with lockdowns. |
| first_indexed | 2024-03-10T13:31:03Z |
| format | Article |
| id | doaj.art-7e8a9aa3db1444f9829d6f56807f9040 |
| institution | Directory Open Access Journal |
| issn | 1367-2630 |
| language | English |
| last_indexed | 2024-03-10T13:31:03Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | New Journal of Physics |
| spelling | doaj.art-7e8a9aa3db1444f9829d6f56807f90402023-11-21T08:00:10ZengIOP PublishingNew Journal of Physics1367-26302023-01-01251111303410.1088/1367-2630/ad0991Non-analytic behaviour in large-deviations of the susceptible-infected-recovered model under the influence of lockdownsLeo Patrick Mulholland0https://orcid.org/0009-0003-2668-5589Yannick Feld1https://orcid.org/0000-0003-4305-0430Alexander K Hartmann2https://orcid.org/0000-0001-6865-5474School of Mathematics and Physics, Queen’s University Belfast , Belfast BT71NN, United KingdomInstitut für Physik, Carl von Ossietzky Universität Oldenburg , 26111 Oldenburg, GermanyInstitut für Physik, Carl von Ossietzky Universität Oldenburg , 26111 Oldenburg, GermanyWe numerically investigate the dynamics of an SIR model with infection level-based lockdowns on Small-World networks. Using a large-deviation approach, namely the Wang–Landau algorithm, we study the distribution of the cumulative fraction of infected individuals. We are able to resolve the density of states for values as low as 10 ^−85 . Hence, we measure the distribution on its full support giving a complete characterization of this quantity. The lockdowns are implemented by severing a certain fraction of the edges in the Small-World network, and are initiated and released at different levels of infection, which are varied within this study. We observe points of non-analytical behaviour for the pdf and discontinuous transitions for correlations with other quantities such as the maximum fraction of infected and the duration of outbreaks. Further, empirical rate functions were calculated for different system sizes, for which a convergence is clearly visible indicating that the large-deviation principle is valid for the system with lockdowns.https://doi.org/10.1088/1367-2630/ad0991SIR modellarge deviationsMonte Carlo simulationsepidemic spreadingrate functionepidemic control |
| spellingShingle | Leo Patrick Mulholland Yannick Feld Alexander K Hartmann Non-analytic behaviour in large-deviations of the susceptible-infected-recovered model under the influence of lockdowns New Journal of Physics SIR model large deviations Monte Carlo simulations epidemic spreading rate function epidemic control |
| title | Non-analytic behaviour in large-deviations of the susceptible-infected-recovered model under the influence of lockdowns |
| title_full | Non-analytic behaviour in large-deviations of the susceptible-infected-recovered model under the influence of lockdowns |
| title_fullStr | Non-analytic behaviour in large-deviations of the susceptible-infected-recovered model under the influence of lockdowns |
| title_full_unstemmed | Non-analytic behaviour in large-deviations of the susceptible-infected-recovered model under the influence of lockdowns |
| title_short | Non-analytic behaviour in large-deviations of the susceptible-infected-recovered model under the influence of lockdowns |
| title_sort | non analytic behaviour in large deviations of the susceptible infected recovered model under the influence of lockdowns |
| topic | SIR model large deviations Monte Carlo simulations epidemic spreading rate function epidemic control |
| url | https://doi.org/10.1088/1367-2630/ad0991 |
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