Edge-based SEIR dynamics with or without infectious force in latent period on random networks

被引:27
|
作者
Wang, Yi [1 ,2 ,3 ]
Cao, Jinde [3 ,4 ]
Alsaedi, Ahmed [5 ]
Ahmad, Bashir [5 ]
机构
[1] China Univ Geosci, Sch Math & Phys, Wuhan 430074, Peoples R China
[2] Univ Victoria, Dept Math & Stat, Victoria, BC V8W 3R4, Canada
[3] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China
[4] King Abdulaziz Univ, Fac Sci, Dept Math, Jeddah 21589, Saudi Arabia
[5] King Abdulaziz Univ, Fac Sci, Dept Math, NAAM Res Grp, Jeddah 21589, Saudi Arabia
基金
中国国家自然科学基金;
关键词
SEIR models; Latent period; Final epidemic size; Random networks; Probability generating function; Stochastic simulations; EPIDEMIC MODEL; GLOBAL STABILITY; SIR DYNAMICS; SIMULATION; GAME;
D O I
10.1016/j.cnsns.2016.09.014
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In nature, most of the diseases have latent periods, and most of the networks look as if they were spun randomly at the first glance. Hence, we consider SEIR dynamics with or without infectious force in latent period on random networks with arbitrary degree distributions. Both of these models are governed by intrinsically three dimensional nonlinear systems of ordinary differential equations, which are the same as classical SEIR models. The basic reproduction numbers and the final size formulae are explicitly derived. Predictions of the models agree well with the large-scale stochastic SEIR simulations on contact networks. In particular, for SEIR model without infectious force in latent period, although the length of latent period has no effect on the basic reproduction number and the final epidemic size, it affects the arrival time of the peak and the peak size; while for SEIR model with infectious force in latent period it also affects the basic reproduction number and the final epidemic size. These accurate model predictions, may provide guidance for the control of network infectious diseases with latent periods. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:35 / 54
页数:20
相关论文
共 50 条
  • [21] Dynamics of Nonlinear Stochastic SEIR Infectious Disease Model with Isolation and Latency Period
    Xu, Wenbin
    Liu, Helong
    Qin, Chuangliang
    SYMMETRY-BASEL, 2025, 17 (02):
  • [22] Multistrain edge-based compartmental model on networks
    Lv, JianPing
    Jin, Zhen
    MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 2019, 42 (05) : 1529 - 1552
  • [23] Edge-based traffic engineering for OSPF networks
    Wang, J
    Yang, YL
    Xiao, L
    Nahrstedt, K
    COMPUTER NETWORKS, 2005, 48 (04) : 605 - 625
  • [24] Analysis of an edge-based SEIR epidemic model with sexual and non-sexual transmission routes
    Yang, Qian
    Huo, Hai-Feng
    Xiang, Hong
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2023, 609
  • [25] The Analysis of Epidemic Network Model with Infectious Force in Latent and Infected Period
    Zhang, Juping
    Jin, Zhen
    DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2010, 2010
  • [26] Edge-based analysis of networks: curvatures of graphs and hypergraphs
    Eidi, Marzieh
    Farzam, Amirhossein
    Leal, Wilmer
    Samal, Areejit
    Jost, Juergen
    THEORY IN BIOSCIENCES, 2020, 139 (04) : 337 - 348
  • [27] Double edge-based traceback for wireless sensor networks
    Zhang, Zhiming
    Li, Ping
    Wu, Fuying
    Deng, Jiangang
    INTERNATIONAL JOURNAL OF COMMUNICATION NETWORKS AND DISTRIBUTED SYSTEMS, 2015, 15 (01) : 107 - 126
  • [28] Edge-Based Shortest Path Caching in Road Networks
    Zhang, Detian
    Liu, An
    Jin, Gaoming
    Li, Qing
    DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, 2019, 11448 : 411 - 414
  • [29] Edge-based analysis of networks: curvatures of graphs and hypergraphs
    Marzieh Eidi
    Amirhossein Farzam
    Wilmer Leal
    Areejit Samal
    Jürgen Jost
    Theory in Biosciences, 2020, 139 : 337 - 348
  • [30] Model hierarchies in edge-based compartmental modeling for infectious disease spread
    Joel C. Miller
    Erik M. Volz
    Journal of Mathematical Biology, 2013, 67 : 869 - 899