Exploring individuals' effective preventive measures against epidemics through reinforcement learning

doi:10.1088/1674-1056/abcfa5

Abstract Individuals' preventive measures, as an effective way to suppress epidemic transmission and to protect themselves from infection, have attracted much academic concern, especially during the COVID-19 pandemic. In this paper, a reinforcement learning-based model is proposed to explore individuals' effective preventive measures against epidemics. Through extensive simulations, we find that the cost of preventive measures influences the epidemic transmission process significantly. The infection scale increases as the cost of preventive measures grows, which means that the government needs to provide preventive measures with low cost to suppress the epidemic transmission. In addition, the effective preventive measures vary from individual to individual according to the social contacts. Individuals who contact with others frequently in daily life are highly recommended to take strict preventive measures to protect themselves from infection, while those who have little social contacts do not need to take any measures considering the inevitable cost. Our research contributes to exploring the effective measures for individuals, which can provide the government and individuals useful suggestions in response to epidemics.

Keywords: epidemic simulation complex networks reinforcement learning preventive measures

Received: 22 September 2020
Revised: 21 October 2020
Accepted manuscript online: 02 December 2020

PACS:

89.75.Hc

(Networks and genealogical trees)

Fund: Project supported by the National Key Technology Research and Development Program of China (Grant No. 2018YFF0301000) and the National Natural Science Foundation of China (Grant Nos. 71673161 and 71790613).

Corresponding Authors: ^†Corresponding author. E-mail: sjni@tsinghua.edu.cn

Cite this article:

Ya-Peng Cui(崔亚鹏), Shun-Jiang Ni (倪顺江), and Shi-Fei Shen(申世飞) Exploring individuals' effective preventive measures against epidemics through reinforcement learning 2021 Chin. Phys. B 30 048901

1 Dong E, Du H and Gardner L 2020 Lancet Infect. Dis. 20 533
2 Helbing D, Ammoser H and Kühnert C2006 Extreme Events in Nature and Society(Berlin: Springer)
3 IMF2020 World Economic Outlook April 2020 The Great Lockdown, Report
4 Molinari N A M, Ortega-Sanchez I R, Messonnier M L, Thompson W W, Wortley P M, Weintraub E and Bridges C B 2007 Vaccine 25 5086
5 Sahneh F D, Chowdhury F N and Scoglio C M 2012 Sci. Rep. 2 632
6 Wang Z, Andrews M A, Wu Z X, Wang L and Bauch C T 2015 Phys. Life Rev. 15 1
7 Keeling M J, Woolhouse M E J, May R M, Davies G and Grenfell B T 2003 Nature 421 136
8 Sartore S, Bonfanti L, Lorenzetto M, Cecchinato M and Marangon S 2010 Poult. Sci. 89 1115
9 Tildesley M J, Savill N J, Shaw D J, Deardon R, Brooks S P, Woolhouse M E, Grenfell B T and Keeling M J 2006 Nature 440 83
10 Galvani A P, Reluga T C and Chapman G B 2007 Proc. Natl. Acad. Sci. USA 104 5692
11 Hu Z L, Liu J G and Ren Z M 2013 Acta Phys. Sin. 62 218901 (in Chinese)
12 Ferguson N M, Mallett S, Jackson H, Roberts N and Ward P 2003 J. Antimicrob. Chemother. 51 977
13 Ferguson N M, Cummings D A T, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S and Burke D S 2005 Nature 437 209
14 Ferguson N M, Cummings D A, Fraser C, Cajka J C, Cooley P C and Burke D S 2006 Nature 442 448
15 Longini I M, Nizam A, Xu S, Ungchusak K, Hanshaoworakul W, Cummings D A T and Halloran M E 2005 Science 309 1083
16 Crosby A1990 America's forgotten pandemic: The influenza of 1918 (Cambridge: Cambridge University Press)
17 Scott S and Duncan C J2001 Biology of Plagues: Evidence from Historical Populations (Cambridge: Cambridge University Press)
18 Lau J T F, Yang X, Pang E, Tsui H Y, Wong E and Wing Y K 2005 Emerg. Infect. Dis. 11 417
19 Jiang C 2007 Int. J. Syst. Sci. 38 451
20 Funk S, Salathe M and Jansen V A A 2010 J. R. Soc. Interface 7 1247
21 Ma X, Cui Y P, Yan X L, Ni S J and Shen S F 2019 Chin. Phys. B 28 128901
22 Bauch C T, Galvani A P and Earn D J D 2003 Proc. Natl. Acad. Sci. USA 100 10564
23 Bauch C T and Earn D J D 2004 Proc. Natl. Acad. Sci. USA 101 13391
24 Shi B Y, Liu G L, Qiu H J, Wang Z, Ren Y Z and Chen D 2019 Physica A 515 171
25 Bauch C T 2005 Proc. R. Soc. B 272 1669
26 Fu F, Rosenbloom D I, Wang L and Nowak M A 2011 Proc. Royal Soc. B 278 42
27 Zhang H F, Shu P P, Wang Z, Tang M and Small M 2017 Appl. Math. Comput. 294 332
28 Vardavas R, Breban R and Blower S 2007 PLoS Comput. Biol. 3 e85
29 Perisic A and Bauch C T 2009 BMC Infect. Dis. 77 15
30 Perisic A and Bauch C T 2009 PLoS Comput. Biol. 5 e1000280
31 Cornforth D M, Reluga T C, Shim E, Bauch C T, Galvani A P and Meyers L A 2011 PLoS Comput. Biol. 7 e1001062
32 Reluga T C 2010 PLoS Comput. Biol. 6 e1000973
33 van Boven M, Klinkenberg D, Pen I, Weissing F J and Heesterbeek H 2008 PLoS One 3 e1558
34 Sutton R and Barto A1998 Reinforcement Learning: An Introduction (Cambridge: MIT Press)
35 Kermack W O and McKendrick A G1991 Bull. Math. Biol. 53 33
36 Watts D J2004 Small Worlds: the Dynamics of Networks between Order and Randomness (Princeton: Princeton University Press)
37 Newman M E 2000 J. Stat. Phys. 101 819
38 Strogatz S H 2001 Nature 410 268
39 Xu X L, Liu C P and He D R 2016 Chin. Phys. Lett. 33 048901
40 Catanzaro M, Boguna M and Pastor-Satorras R 2005 Phys. Rev. E 71 027103
41 Watts D J and Strogatz S H 1998 Nature 393 440
42 Holme P and Kim B J 2002 Phys. Rev. E 65 026107

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