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Using agent-based simulation to assess diseaseprevention measures during pandemics |
| Yunhe Tong(童蕴贺)1, Christopher King1,†, and Yanghui Hu(胡杨慧)2,‡ |
1 Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UK;
2 State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230027, China |
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Abstract Despite the growing interest in macroscopic epidemiological models to deal with threats posed by pandemics such as COVID-19, little has been done regarding the assessment of disease spread in day-to-day life, especially within buildings such as supermarkets where people must obtain necessities at the risk of exposure to disease. Here, we propose an integrated customer shopping simulator including both shopper movement and choice behavior, using a force-based and discrete choice model, respectively. By a simple extension to the force-based model, we implement the following preventive measures currently taken by supermarkets; social distancing and one-way systems, and different customer habits, assessing them based on the average individual disease exposure and the time taken to complete shopping (shopping efficiency). Results show that maintaining social distance is an effective way to reduce exposure, but at the cost of shopping efficiency. We find that the one-way system is the optimal strategy for reducing exposure while minimizing the impact on shopping efficiency. Customers should also visit supermarkets less frequently, but buy more when they do, if they wish to minimize their exposure. We hope that this work demonstrates the potential of pedestrian dynamics simulations in assessing preventative measures during pandemics, particularly if it is validated using empirical data.
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Received: 31 March 2021
Revised: 07 June 2021
Accepted manuscript online: 28 June 2021
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PACS:
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89.40.-a
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(Transportation)
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07.05.Tp
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(Computer modeling and simulation)
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05.65.+b
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(Self-organized systems)
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| Fund: Project supported by the China Scholarship Council (Grant No. 201906370050). |
Corresponding Authors:
Christopher King, Yanghui Hu
E-mail: aa18187@bristol.ac.uk;huyang1@mail.ustc.edu.cn
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Cite this article:
Yunhe Tong(童蕴贺), Christopher King, and Yanghui Hu(胡杨慧) Using agent-based simulation to assess diseaseprevention measures during pandemics 2021 Chin. Phys. B 30 098903
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[1] Ghebreyesus T A 2020 WHO Director-General's opening remarks at the media briefing on COVID-19-2020.
[2] Deziel N C, Allen J G, Scheepers P T J and Levy J I 2020 J. Expo. Sci. Env. Epidemiol. 30 591
[3] Kissler S M, Tedijanto C, Goldstein E, Grad Y H and Lipsitch M 2020 Science 368 860
[4] Kucharski A J, Russell T W, Diamond C, Liu Y, Edmunds J, Funk S, Eggo R M, Sun F, Jit M, Munday J D, Davies N, Gimma A, van Zandvoort K, Gibbs H, Hellewell J, Jarvis C I, Clifford S, Quilty B J, Bosse N I, Abbott S, Klepac P and Flasche S 2020 Lancet Infect. Dis. 20 553
[5] Kermack W O and McKendrick A G 1991 Bull. Math. Biol. 53 89
[6] Dybiec B 2009 Eur. Phys. J. B 67 377
[7] Cooper I, Mondal A and Antonopoulos C G 2020 Chaos Solitons Fractals 139 110057
[8] Twarogowska M, Goatin P and Duvigneau R 2014 Transp. Res. Procedia 2 477
[9] Yue F R, Chen J, Ma J, Song W G and Lo S M 2018 Chin. Phys. B 27 124501
[10] Cao S, Song W, Lv W and Fang Z 2015 Phys. Stat. Mech. Appl. 436 45
[11] Chen C K and Tong Y H 2019 Chin. Phys. B 28 010503
[12] Helbing D, Farkas I and Vicsek T 2000 Nature 407 487
[13] Ma J, Shi D, Li T, Li X, Xu T and Lin P 2020 J. Stat. Mech. Theory Exp. 2020 073409
[14] Bode N W F, Chraibi M and Holl S 2019 Transp. Res. Part B Methodol. 119 197
[15] Cao S, Wang P, Yao M and Song W 2019 Commun. Nonlinear Sci. Numer. Simul. 69 329
[16] Guo N, Jiang R, Hu M B and Ding J X 2017 Chin. Phys. B 26 120506
[17] Chraibi M, Seyfried A and Schadschneider A 2010 Phys. Rev. E 82 046111
[18] Kłusek A, Topa P, Wąs J and Lubaś R 2018 Int J. High. Perform. Comput. Appl. 32 482
[19] Tong Y and Bode N W 2021 Transp. Res. Part C Emerg. Technol. 124 102909
[20] Department of Environment Food and Rural Affairs, Public Health England Accessed on: 25/03/2021
[21] Ronchi E and Lovreglio R 2020 Saf. Sci. 130 104834
[22] Xu Q and Chraibi M 2020 Sustainability 12 9385
[23] Train K 2008 Discrete Choice Methods with Simulation (2nd edn.) (Cambridge: Cambridge university press) pp. 40-71
[24] Rothan H A and Byrareddy S N 2020 J. Autoimmun. 109 102433
[25] Sun C and Zhai Z 2020 Sustainable Cities and Society 62 102390
[26] Bode N W F, Kemloh Wagoum A U and Codling E A 2014 J. R. Soc. Interface 11 20130904
[27] Ying F, Wallis A O G, Beguerisse-Diaz M, Porter M A and Howison S D 2019 Phys. Rev. E 100 062304
[28] Centre for Disease Control and Prevention Accessed on: 29/03/2021 |
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