Epidemic models with nonlinear infection forces

  • Received: 01 January 2005 Accepted: 29 June 2018 Published: 01 November 2005
  • MSC : 92D30.

  • Epidemic models with behavior changes are studied to consider effects of protection measures and intervention policies. It is found that intervention strategies decrease endemic levels and tend to make the dynamical behavior of a disease evolution simpler. For a saturated infection force, the model may admit a stable disease-free equilibrium and a stable endemic equilibrium at the same time. If we vary a recovery rate, numerical simulations show that the boundaries of the region for the persistence of the disease undergo the changes from the separatrix of a saddle to an unstable limit cycle. If the inhibition effect from behavior changes is weak, we find two limit cycles and obtain bifurcations of the model as the population size changes. We also find that the disease may die out although there are two endemic equilibria.

    Citation: Wendi Wang. Epidemic models with nonlinear infection forces[J]. Mathematical Biosciences and Engineering, 2006, 3(1): 267-279. doi: 10.3934/mbe.2006.3.267

    Related Papers:

    [1] Abdelheq Mezouaghi, Salih Djillali, Anwar Zeb, Kottakkaran Sooppy Nisar . Global proprieties of a delayed epidemic model with partial susceptible protection. Mathematical Biosciences and Engineering, 2022, 19(1): 209-224. doi: 10.3934/mbe.2022011
    [2] Abdennasser Chekroun, Mohammed Nor Frioui, Toshikazu Kuniya, Tarik Mohammed Touaoula . Global stability of an age-structured epidemic model with general Lyapunov functional. Mathematical Biosciences and Engineering, 2019, 16(3): 1525-1553. doi: 10.3934/mbe.2019073
    [3] Jianquan Li, Yiqun Li, Yali Yang . Epidemic characteristics of two classic models and the dependence on the initial conditions. Mathematical Biosciences and Engineering, 2016, 13(5): 999-1010. doi: 10.3934/mbe.2016027
    [4] James M. Hyman, Jia Li . Differential susceptibility and infectivity epidemic models. Mathematical Biosciences and Engineering, 2006, 3(1): 89-100. doi: 10.3934/mbe.2006.3.89
    [5] Tingting Xue, Long Zhang, Xiaolin Fan . Dynamic modeling and analysis of Hepatitis B epidemic with general incidence. Mathematical Biosciences and Engineering, 2023, 20(6): 10883-10908. doi: 10.3934/mbe.2023483
    [6] Yicang Zhou, Zhien Ma . Global stability of a class of discrete age-structured SIS models with immigration. Mathematical Biosciences and Engineering, 2009, 6(2): 409-425. doi: 10.3934/mbe.2009.6.409
    [7] Qian Yan, Xianning Liu . Dynamics of an epidemic model with general incidence rate dependent on a class of disease-related contact functions. Mathematical Biosciences and Engineering, 2023, 20(12): 20795-20808. doi: 10.3934/mbe.2023920
    [8] Jinliang Wang, Hongying Shu . Global analysis on a class of multi-group SEIR model with latency and relapse. Mathematical Biosciences and Engineering, 2016, 13(1): 209-225. doi: 10.3934/mbe.2016.13.209
    [9] Christine K. Yang, Fred Brauer . Calculation of R0 for age-of-infection models. Mathematical Biosciences and Engineering, 2008, 5(3): 585-599. doi: 10.3934/mbe.2008.5.585
    [10] Yilei Tang, Dongmei Xiao, Weinian Zhang, Di Zhu . Dynamics of epidemic models with asymptomatic infection and seasonal succession. Mathematical Biosciences and Engineering, 2017, 14(5&6): 1407-1424. doi: 10.3934/mbe.2017073
  • Epidemic models with behavior changes are studied to consider effects of protection measures and intervention policies. It is found that intervention strategies decrease endemic levels and tend to make the dynamical behavior of a disease evolution simpler. For a saturated infection force, the model may admit a stable disease-free equilibrium and a stable endemic equilibrium at the same time. If we vary a recovery rate, numerical simulations show that the boundaries of the region for the persistence of the disease undergo the changes from the separatrix of a saddle to an unstable limit cycle. If the inhibition effect from behavior changes is weak, we find two limit cycles and obtain bifurcations of the model as the population size changes. We also find that the disease may die out although there are two endemic equilibria.


  • This article has been cited by:

    1. Li-Ming Cai, Xue-Zhi Li, Global analysis of a vector-host epidemic model with nonlinear incidences, 2010, 217, 00963003, 3531, 10.1016/j.amc.2010.09.028
    2. Rita Ghosh, Uttam Ghosh, 2015, Chapter 21, 978-81-322-2546-1, 219, 10.1007/978-81-322-2547-8_21
    3. Zhaoyang Zhang, Honggang Wang, Chonggang Wang, Hua Fang, Modeling Epidemics Spreading on Social Contact Networks, 2015, 3, 2168-6750, 410, 10.1109/TETC.2015.2398353
    4. Guihua Li, Wendi Wang, Bifurcation analysis of an epidemic model with nonlinear incidence, 2009, 214, 00963003, 411, 10.1016/j.amc.2009.04.012
    5. Zhixing Hu, Ping Bi, Wanbiao Ma, Shigui Ruan, Bifurcations of an SIRS epidemic model with nonlinear incidence rate, 2011, 15, 1553-524X, 93, 10.3934/dcdsb.2011.15.93
    6. GIUSEPPE MULONE, BRIAN STRAUGHAN, MODELING BINGE DRINKING, 2012, 05, 1793-5245, 1250005, 10.1142/S1793524511001453
    7. Jinhui Li, Zhidong Teng, Bifurcations of an SIRS model with generalized non-monotone incidence rate, 2018, 2018, 1687-1847, 10.1186/s13662-018-1675-y
    8. Xi-Chao Duan, I Hyo Jung, Xue-Zhi Li, Maia Martcheva, Dynamics and optimal control of an age-structured SIRVS epidemic model, 2020, 43, 01704214, 4239, 10.1002/mma.6190
    9. Yongli Cai, Weiming Wang, Dynamics of a parasite-host epidemiological model in spatial heterogeneous environment, 2015, 20, 1531-3492, 989, 10.3934/dcdsb.2015.20.989
    10. Nicolas Bacaër, Rachid Ouifki, Carel Pretorius, Robin Wood, Brian Williams, Modeling the joint epidemics of TB and HIV in a South African township, 2008, 57, 0303-6812, 557, 10.1007/s00285-008-0177-z
    11. Florinda Capone, Valentina De Cataldis, Roberta De Luca, Influence of diffusion on the stability of equilibria in a reaction–diffusion system modeling cholera dynamic, 2015, 71, 0303-6812, 1107, 10.1007/s00285-014-0849-9
    12. Yongli Cai, Yun Kang, Malay Banerjee, Weiming Wang, A stochastic SIRS epidemic model with infectious force under intervention strategies, 2015, 259, 00220396, 7463, 10.1016/j.jde.2015.08.024
    13. Min Lu, Jicai Huang, Shigui Ruan, Pei Yu, Bifurcation analysis of an SIRS epidemic model with a generalized nonmonotone and saturated incidence rate, 2019, 267, 00220396, 1859, 10.1016/j.jde.2019.03.005
    14. Andrew J. Black, Alan J. McKane, Ana Nunes, Andrea Parisi, Stochastic fluctuations in the susceptible-infective-recovered model with distributed infectious periods, 2009, 80, 1539-3755, 10.1103/PhysRevE.80.021922
    15. Alberto d’Onofrio, Piero Manfredi, Information-related changes in contact patterns may trigger oscillations in the endemic prevalence of infectious diseases, 2009, 256, 00225193, 473, 10.1016/j.jtbi.2008.10.005
    16. Ronan F. Arthur, James H. Jones, Matthew H. Bonds, Yoav Ram, Marcus W. Feldman, Rustom Antia, Adaptive social contact rates induce complex dynamics during epidemics, 2021, 17, 1553-7358, e1008639, 10.1371/journal.pcbi.1008639
    17. Paul Georgescu, Gheorghe Moroşanu, Pest regulation by means of impulsive controls, 2007, 190, 00963003, 790, 10.1016/j.amc.2007.01.079
    18. Haijun Hu, Xingfu Zou, Traveling waves of a diffusive SIR epidemic model with general nonlinear incidence and infinitely distributed latency but without demography, 2021, 58, 14681218, 103224, 10.1016/j.nonrwa.2020.103224
    19. Xiaojie Mu, Qimin Zhang, Optimal strategy of vaccination and treatment in an SIRS model with Markovian switching, 2019, 42, 01704214, 767, 10.1002/mma.5378
    20. Marcos A. Capistran, Antonio Capella, J. Andrés Christen, Giovanni Lo Iacono, Forecasting hospital demand in metropolitan areas during the current COVID-19 pandemic and estimates of lockdown-induced 2nd waves, 2021, 16, 1932-6203, e0245669, 10.1371/journal.pone.0245669
    21. Yongli Cai, Xinze Lian, Zhihang Peng, Weiming Wang, Spatiotemporal transmission dynamics for influenza disease in a heterogenous environment, 2019, 46, 14681218, 178, 10.1016/j.nonrwa.2018.09.006
    22. Feng Rao, Partha S. Mandal, Yun Kang, Complicated endemics of an SIRS model with a generalized incidence under preventive vaccination and treatment controls, 2019, 67, 0307904X, 38, 10.1016/j.apm.2018.10.016
    23. Qun Liu, Daqing Jiang, Tasawar Hayat, Ahmed Alsaedi, Stationary distribution of a stochastic within-host dengue infection model with immune response and regime switching, 2019, 526, 03784371, 121057, 10.1016/j.physa.2019.121057
    24. Muhammad Ozair, Analysis of Pine Wilt Disease Model with Nonlinear Incidence and Horizontal Transmission, 2014, 2014, 1110-757X, 1, 10.1155/2014/204241
    25. Ogunmiloro Oluwatayo Michael, Optimal control of educational campaign applied to the transmission of trachoma disease: a mathematical model, 2020, 2195-268X, 10.1007/s40435-020-00719-7
    26. Weiming Wang, Yun Kang, Yongli Cai, Global stability of the steady states of an epidemic model incorporating intervention strategies, 2017, 14, 1551-0018, 1071, 10.3934/mbe.2017056
    27. Yakui Xue, Xiaoming Tang, Xinpeng Yuan, Bifurcation Analysis of an SIV Epidemic Model with the Saturated Incidence Rate, 2014, 24, 0218-1274, 1450060, 10.1142/S0218127414500606
    28. Yan Wang, Daqing Jiang, Tasawar Hayat, Ahmed Alsaedi, Stationary distribution of an HIV model with general nonlinear incidence rate and stochastic perturbations, 2019, 356, 00160032, 6610, 10.1016/j.jfranklin.2019.06.035
    29. Guijie Lan, Sanling Yuan, Baojun Song, The impact of hospital resources and environmental perturbations to the dynamics of SIRS model, 2021, 358, 00160032, 2405, 10.1016/j.jfranklin.2021.01.015
    30. W. Wang, Modeling Adaptive Behavior in Influenza Transmission, 2012, 7, 0973-5348, 253, 10.1051/mmnp/20127315
    31. Guihua Li, Gaofeng Li, Bifurcation Analysis of an SIR Epidemic Model with the Contact Transmission Function, 2014, 2014, 1085-3375, 1, 10.1155/2014/930541
    32. Betty Nannyonga, Joseph Ssebuliba, Juliet Nakakawa, Betty Nabiyonga, J.Y.T. Mugisha, To apprehend or not to apprehend: A mathematical model for ending student strikes in a university, 2018, 339, 00963003, 607, 10.1016/j.amc.2018.07.034
    33. G. Rozhnova, A. Nunes, Fluctuations and oscillations in a simple epidemic model, 2009, 79, 1539-3755, 10.1103/PhysRevE.79.041922
    34. Ding Fang, Yongxin Zhang, Wendi Wang, Complex Behaviors of Epidemic Model with Nonlinear Rewiring Rate, 2020, 2020, 1076-2787, 1, 10.1155/2020/7310347
    35. Muhammad Ozair, Abid Ali Lashari, Il Hyo Jung, Kazeem Oare Okosun, Stability Analysis and Optimal Control of a Vector-Borne Disease with Nonlinear Incidence, 2012, 2012, 1026-0226, 1, 10.1155/2012/595487
    36. Dongchen Shangguan, Zhijun Liu, Lianwen Wang, Ronghua Tan, A stochastic epidemic model with infectivity in incubation period and homestead–isolation on the susceptible, 2021, 1598-5865, 10.1007/s12190-021-01504-1
    37. Yoichi Enatsu, Yukihiko Nakata, Stability and bifurcation analysis of epidemic models with saturated incidence rates: An application to a nonmonotone incidence rate, 2014, 11, 1551-0018, 785, 10.3934/mbe.2014.11.785
    38. Mingju Ma, Sanyang Liu, Jun Li, Traveling waves of a diffusive epidemic model with nonlinear infection forces, 2016, 2016, 1687-1847, 10.1186/s13662-016-0972-6
    39. Xiaoyan Gao, Yongli Cai, Feng Rao, Shengmao Fu, Weiming Wang, Positive steady states in an epidemic model with nonlinear incidence rate, 2018, 75, 08981221, 424, 10.1016/j.camwa.2017.09.029
    40. M Simões, M.M Telo da Gama, A Nunes, Stochastic fluctuations in epidemics on networks, 2008, 5, 1742-5689, 555, 10.1098/rsif.2007.1206
    41. Alberto d’Onofrio, Malay Banerjee, Piero Manfredi, Spatial behavioural responses to the spread of an infectious disease can suppress Turing and Turing–Hopf patterning of the disease, 2020, 545, 03784371, 123773, 10.1016/j.physa.2019.123773
    42. Gulshan Shrivastava, Prabhat Kumar, Rudra Pratap Ojha, Pramod Kumar Srivastava, Senthilkumar Mohan, Gautam Srivastava, Defensive Modeling of Fake News Through Online Social Networks, 2020, 7, 2329-924X, 1159, 10.1109/TCSS.2020.3014135
    43. Zigen Song, Jian Xu, Qunhong Li, Local and global bifurcations in an SIRS epidemic model, 2009, 214, 00963003, 534, 10.1016/j.amc.2009.04.027
    44. Weiming Wang, Xiaoyan Gao, Yongli Cai, Hongbo Shi, Shengmao Fu, Turing patterns in a diffusive epidemic model with saturated infection force, 2018, 355, 00160032, 7226, 10.1016/j.jfranklin.2018.07.014
    45. Bin Yang, Yongli Cai, Kai Wang, Weiming Wang, Global threshold dynamics of a stochastic epidemic model incorporating media coverage, 2018, 2018, 1687-1847, 10.1186/s13662-018-1925-z
    46. Yilei Tang, Deqing Huang, Shigui Ruan, Weinian Zhang, Coexistence of Limit Cycles and Homoclinic Loops in a SIRS Model with a Nonlinear Incidence Rate, 2008, 69, 0036-1399, 621, 10.1137/070700966
    47. Tahir Khan, Roman Ullah, Gul Zaman, 2023, 9780323995573, 29, 10.1016/B978-0-32-399557-3.00007-7
    48. Zafar Iqbal, J.E. Macías-Díaz, Nauman Ahmed, M. Aziz-ur Rehman, Ali Raza, Muhammad Rafiq, A SEIR model with memory effects for the propagation of Ebola-like infections and its dynamically consistent approximation, 2021, 209, 01692607, 106322, 10.1016/j.cmpb.2021.106322
    49. Udai Kumar, Partha Sarathi Mandal, Jai Prakash Tripathi, Vijay Pal Bajiya, Sarita Bugalia, SIRS epidemiological model with ratio‐dependent incidence: Influence of preventive vaccination and treatment control strategies on disease dynamics, 2021, 44, 0170-4214, 14703, 10.1002/mma.7737
    50. Sedrique A. Tiomela, J.E. Macías-Díaz, Alain Mvogo, Computer simulation of the dynamics of a spatial susceptible-infected-recovered epidemic model with time delays in transmission and treatment, 2021, 212, 01692607, 106469, 10.1016/j.cmpb.2021.106469
    51. Sarita Bugalia, Jai Prakash Tripathi, Hao Wang, Mathematical modeling of intervention and low medical resource availability with delays: Applications to COVID-19 outbreaks in Spain and Italy, 2021, 18, 1551-0018, 5865, 10.3934/mbe.2021295
    52. Zhang Lijuan, Wang Fuchang, Liang Hongri, Osamah Ibrahim Khalaf, A Stochastic SEIRS Epidemic Model with Infection Forces and Intervention Strategies, 2022, 2022, 2040-2309, 1, 10.1155/2022/4538045
    53. Tianyu Cheng, Xingfu Zou, A new perspective on infection forces with demonstration by a DDE infectious disease model, 2022, 19, 1551-0018, 4856, 10.3934/mbe.2022227
    54. Hermann J Eberl, Harry J Gaebler, Yrjö T Gröhn, A brief note on a multistrain SIR model with complete cross-protection and nonlinear force of infection, 2021, 103, 10075704, 106001, 10.1016/j.cnsns.2021.106001
    55. Fang Zhang, Wenzhe Cui, Yanfei Dai, Yulin Zhao, Bifurcations of an SIRS epidemic model with a general saturated incidence rate, 2022, 19, 1551-0018, 10710, 10.3934/mbe.2022501
    56. Maoxing Liu, Rong Yuan, Stability of a stochastic discrete SIS epidemic model with general nonlinear incidence rate, 2022, 28, 1023-6198, 561, 10.1080/10236198.2022.2055470
    57. Xinzhu Guan, Fan Yang, Yongli Cai, Weiming Wang, Global stability of an influenza A model with vaccination, 2022, 134, 08939659, 108322, 10.1016/j.aml.2022.108322
    58. Magdalena Ochab, Piero Manfredi, Krzysztof Puszynski, Alberto d’Onofrio, Multiple epidemic waves as the outcome of stochastic SIR epidemics with behavioral responses: a hybrid modeling approach, 2023, 111, 0924-090X, 887, 10.1007/s11071-022-07317-6
    59. Nicolò Cangiotti, Marco Capolli, Mattia Sensi, Sara Sottile, A survey on Lyapunov functions for epidemic compartmental models, 2023, 1972-6724, 10.1007/s40574-023-00368-6
    60. Abhishek Kumar, Kanica Goel, , Dynamics of a nonlinear epidemic transmission model incorporating a class of hospitalized individuals: a qualitative analysis and simulation, 2023, 56, 1751-8113, 415601, 10.1088/1751-8121/acf9cf
    61. Chunxian Huang, Zhenkun Jiang, Xiaojun Huang, Xiaoliang Zhou, Bifurcation analysis of an SIS epidemic model with a generalized non-monotonic and saturated incidence rate, 2024, 17, 1793-5245, 10.1142/S179352452350033X
    62. Helong Liu, Xinyu Song, Stationary distribution and extinction of a stochastic HIV/AIDS model with nonlinear incidence rate, 2024, 21, 1551-0018, 1650, 10.3934/mbe.2024072
    63. Jan B. Broekaert, Davide La Torre, Faizal Hafiz, The impact of the psychological effect of infectivity on Nash-balanced control strategies for epidemic networks, 2024, 0254-5330, 10.1007/s10479-023-05781-w
    64. Shengshuang Chen, Yingxin Guo, Chuan Zhang, Stationary distribution of a stochastic epidemic model with distributed delay under regime switching, 2024, 1598-5865, 10.1007/s12190-024-01985-w
    65. Sattwika Acharya, Ranjit Kumar Upadhyay, Bapin Mondal, Exploring the complex dynamics of a diffusive epidemic model: Stability and bifurcation analysis, 2024, 34, 1054-1500, 10.1063/5.0159015
    66. Vineet Srivastava, Pramod Kumar Srivastava, Ashok Kumar Yadav, 2024, chapter 9, 9798369352717, 169, 10.4018/979-8-3693-5271-7.ch009
    67. Tianyu Cheng, Xingfu Zou, Modelling the impact of precaution on disease dynamics and its evolution, 2024, 89, 0303-6812, 10.1007/s00285-024-02100-0
    68. MUSTAPHA BELABBAS, FETHI SOUNA, PANKAJ KUMAR TIWARI, YOUSSAF MENACER, ROLE OF INTERVENTION STRATEGIES AND PSYCHOLOGICAL EFFECT ON THE CONTROL OF INFECTIOUS DISEASES IN THE RANDOM ENVIRONMENT, 2024, 32, 0218-3390, 971, 10.1142/S0218339024500402
    69. Bruno Buonomo, Eleonora Messina, Claudia Panico, Antonia Vecchio, An integral renewal equation approach to behavioural epidemic models with information index, 2025, 90, 0303-6812, 10.1007/s00285-024-02172-y
    70. Pierre-Alexandre Bliman, Boureima Sangaré, Assane Savadogo, A framework for the modelling and the analysis of epidemiological spread in commuting populations, 2025, 00255564, 109403, 10.1016/j.mbs.2025.109403
    71. Zhaohua Wu, Yongli Cai, Zhiming Wang, Daihai He, Weiming Wang, Global dynamics of a fractional order SIRS epidemic model by the way of generalized continuous time random walk, 2025, 90, 0303-6812, 10.1007/s00285-025-02201-4
    72. Bruno Buonomo, Eleonora Messina, Claudia Panico, Minimal epidemic models with information index: from compartmental to integral formulation, 2025, 1972-6724, 10.1007/s40574-025-00473-8
  • Reader Comments
  • © 2006 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(4152) PDF downloads(765) Cited by(72)

Article outline

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog