INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
Next
|
|
|
Epidemic threshold influenced by non-pharmaceutical interventions in residential university environments |
Zechao Lu(卢泽超)1, Shengmei Zhao(赵生妹)1, Huazhong Shu(束华中)2, and Long-Yan Gong(巩龙延)2,† |
1 Institute of Signal Processing and Transmission, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; 2 College of Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China |
|
|
Abstract The control of highly contagious disease spreading in campuses is a critical challenge. In residential universities, students attend classes according to a curriculum schedule, and mainly pack into classrooms, dining halls and dorms. They move from one place to another. To simulate such environments, we propose an agent-based susceptible-infected-recovered model with time-varying heterogeneous contact networks. In close environments, maintaining physical distancing is the most widely recommended and encouraged non-pharmaceutical intervention. It can be easily realized by using larger classrooms, adopting staggered dining hours, decreasing the number of students per dorm and so on. Their real-world influence remains uncertain. With numerical simulations, we obtain epidemic thresholds. The effect of such countermeasures on reducing the number of disease cases is also quantitatively evaluated.
|
Received: 31 March 2023
Revised: 05 June 2023
Accepted manuscript online: 29 June 2023
|
PACS:
|
87.19.X-
|
(Diseases)
|
|
89.75.Hc
|
(Networks and genealogical trees)
|
|
87.23.Ge
|
(Dynamics of social systems)
|
|
05.10.-a
|
(Computational methods in statistical physics and nonlinear dynamics)
|
|
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61871234). |
Corresponding Authors:
Long-Yan Gong
E-mail: lygong@njupt.edu.cn
|
Cite this article:
Zechao Lu(卢泽超), Shengmei Zhao(赵生妹), Huazhong Shu(束华中), and Long-Yan Gong(巩龙延) Epidemic threshold influenced by non-pharmaceutical interventions in residential university environments 2024 Chin. Phys. B 33 028707
|
[1] Hekmati A, Luhar M, Krishnamachari B and Matarić M 2021 IEEE International Conference on Communications Workshops (ICC Workshops) pp. 1-6 [2] Borowiak M, Ning F, Pei J, Zhao S, Tung H and Durrett R 2021 Math. Biosci. Eng. 18 551 [3] Brooks-Pollock E, Christensen H, Trickey A, Hemani G, Nixon E, Thomas A C, Turner K, Finn A, Hickman M, Relton C and Danon L 2021 Nat. Commun. 12 5017 [4] Frazier P I, Cashore J M, Duan N, Henderson S G, Janmohamed A, Liu B, Shmoys D B, Wan J and Zhang Y 2022 Proc. Natl. Acad. Sci. USA 119 e2112532119 [5] Gressman P T and Peck J R 2020 Math. Biosci. 328 108436 [6] Ranoa D R E, Holland R L, Alnaji F G, et al. 2022 Nat. Commun. 13 3207 [7] Perra N 2021 Phys. Rep. 913 1 [8] Lai S, Ruktanonchai N W, Zhou L, et al. 2020 Nature 585 410 [9] Xiao T, Mu T, Shen S, Song Y, Yang S and He J 2022 Physica A 592 126734 [10] Gaetaa G 2020 Chaos, Solitons and Fractals 140 110074 [11] d'Onofrio A and Manfredi P 2022 Chaos, Solitons and Fractals 159 112072 [12] Tong Y H, King C and Hu Y H 2021 Chin. Phys. B 30 098903 [13] Ge Y, Zhang W, Wu X, et al. 2022 Nat. Commun. 13 3106 [14] Martcheva M 2015 An Introduction to Mathematical Epidemiology (New York: Springer) [15] Kermack W O and McKendrick A G 1927 Proc. R. Soc. London A: Math. Phys. Eng. Sci. 115 700 [16] Pastor-Satorras R, Castellano C, Van Mieghem P and Vespignani A 2015 Rev. Mod. Phys. 87 925 [17] Leitch J, Alexander K A and Sengupta S 2019 Appl. Netw. Sci. 4 105 [18] Pastor-Satorras R and Vespignani A 2001 Phys. Rev. Lett. 86 3200 [19] Moreno Y, Pastor-Satorras R and Vespignani A 2002 Eur. Phys. J. B 26 521 [20] Newman M E J 2002 Phys. Rev. E 66 016128 [21] Hasegawa T and Nemoto K 2016 Phys. Rev. E 93 032324 [22] Perra N, Gonçalves B, Pastor-Satorras R and Vespignani A 2012 Sci. Rep. 2 469 [23] Liu S, Perra N, Karsai M and Vespignani A 2014 Phys. Rev. Lett. 112 118702 [24] Starnini M and Pastor-Satorras R 2014 Phys. Rev. E 89 032807 [25] Cui Y P, Ni S J and Shen S F 2021 Chin. Phys. B 30 048901 [26] Peng X L and Zhang Y D 2021 Chin. Phys. B 30 058901 [27] Hambridge H L, Kahn R and Onnela J P 2021 Int. J. Infect. Dis. 113 325 [28] https://www.webmd.com/covid/covid-recovery-overview [29] Crépey P, Alvarez F P and Barthélemy M 2006 Phys. Rev. E 73 046131 [30] Shu P, Tang M, Gong K and Liu Y 2012 Chaos 22 043124 [31] Shu P, Wang W, Tang M and Do Y 2015 Chaos 25 063104 [32] Silva D H, Anteneodo C and Ferreira S C 2023 Commun. Nonlinear. Sci. Numer. Simula. 116 106877 [33] Landau D and Binder K 2009 A guide to Monte Carlo simulations in statistical physics, 3rd edn. (Cambridge University Press, New York) [34] Nazarimehr F, Jafari S, Perc M and Sprott J C 2020 Europhys. Lett. 132 18001 [35] Southall E, Brett T S, Tildesley M J, Dyson L. 2021 J. R. Soc. Interface 18 20210555 [36] Due to the stochastic effect brought by the underlying networks and initial conditions (only one infected seed, see the 4-th step of the simulation procedure given in Subsection 2.3), r∞ may be small even at large λ. |
No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|