Large and moderate deviation principles for susceptible-infected-removed epidemic in a random environment

Xiaofeng XUE, Yumeng SHEN

PDF(445 KB)
PDF(445 KB)
Front. Math. China ›› 2021, Vol. 16 ›› Issue (4) : 1117-1161. DOI: 10.1007/s11464-021-0958-x
RESEARCH ARTICLE
RESEARCH ARTICLE

Large and moderate deviation principles for susceptible-infected-removed epidemic in a random environment

Author information +
History +

Abstract

We are concerned with SIR epidemics in a random environment on complete graphs, where edges are assigned with i.i.d. weights. Our main results give large and moderate deviation principles of sample paths of this model. Our results generalize large and moderate deviation principles of the classic SIR models given by E. Pardoux and B. Samegni-Kepgnou [J. Appl. Probab., 2017, 54: 905-920] and X. F. Xue [Stochastic Process. Appl., 2019, 140: 49-80].

Keywords

large deviation / moderate deviation / susceptible-infected-removed (SIR) / epidemic / random environment

Cite this article

Download citation ▾
Xiaofeng XUE, Yumeng SHEN. Large and moderate deviation principles for susceptible-infected-removed epidemic in a random environment. Front. Math. China, 2021, 16(4): 1117‒1161 https://doi.org/10.1007/s11464-021-0958-x

References

[1]
Agazzi A, Dembo A, Eckmann J P. Large deviations theory for Markov jump models of chemical reaction networks. Ann Appl Probab, 2018, 28: 1821–1855
CrossRef Google scholar
[2]
Anderson R M, May R M. Infectious Diseases of Humans: Dynamic and Control. Oxford: Oxford Univ Press, 1991
[3]
Bertacchi D, Lanchier N, Zucca F. Contact and voter processes on the infinite percolation cluster as models of host-symbiont interactions. Ann Appl Probab, 2011, 21: 1215–1252
CrossRef Google scholar
[4]
Britton T, Pardoux E. Stochastic epidemics in a homogeneous community. arXiv: 1808.05350
[5]
Durrett R. Random Graph Dynamics. Cambridge: Cambridge Univ Press, 2007
CrossRef Google scholar
[6]
Ethier N, Kurtz T. Markov Processes: Characterization and Convergence. Hoboken: John Wiley and Sons, 1986
CrossRef Google scholar
[7]
Gao F Q, Quastel J. Moderate deviations from the hydrodynamic limit of the symmetric exclusion process. Sci China Math, 2003, 46(5): 577–592
CrossRef Google scholar
[8]
Kurtz T. Strong approximation theorems for density dependent Markov chains. Stochastic Process Appl, 1978, 6: 223–240
CrossRef Google scholar
[9]
Pardoux E, Samegni-Kepgnou B. Large deviation principle for epidemic models. J Appl Probab, 2017, 54: 905–920
CrossRef Google scholar
[10]
Peterson J. The contact process on the complete graph with random vertex-dependent infection rates. Stochastic Process Appl, 2011, 121(3): 609–629
CrossRef Google scholar
[11]
Puhalskii A. The method of stochastic exponentials for large deviations. Stochastic Process Appl, 1994, 54: 45–70
CrossRef Google scholar
[12]
Schuppen V J, Wong E. Transformation of local martingales under a change of law. Ann Probab, 1974, 2: 879–888
CrossRef Google scholar
[13]
Schwartz A, Weiss A. Large Deviations for Performance Analysis. London: Chapman and Hall, 1995
[14]
Sion M. On general minimax theorems. Pacific J Math, 1958, 8: 171–176
CrossRef Google scholar
[15]
Xue X F. Law of large numbers for the SIR model with random vertex weights on Erdos-Renyi graph. Phys A, 2017, 486: 434–445
CrossRef Google scholar
[16]
Xue X F. Moderate deviations of density-dependent Markov chains. Stochastic Process Appl, 2019, 140: 49–80
CrossRef Google scholar
[17]
Yao Q, Chen X X. The complete convergence theorem holds for contact processes in a random environment on Zd×Z+. Stoch Anal Appl, 2012, 122(9): 3066–3100

RIGHTS & PERMISSIONS

2021 Higher Education Press
AI Summary AI Mindmap
PDF(445 KB)

Accesses

Citations

Detail

Sections
Recommended

/