Research article

Application and analysis of a model with environmental transmission in a periodic environment

  • Received: 13 June 2023 Revised: 18 August 2023 Accepted: 20 August 2023 Published: 28 August 2023
  • The goal of this paper is to introduce a non-autonomous environmental transmission model for most respiratory and enteric infectious diseases to study the impact of periodic environmental changes on related infectious diseases. The transmission and decay rates of pathogens in the environment are set as periodic functions to summarize the influence of environmental fluctuations on diseases. The solutions of the model are qualitatively analyzed, and the equilibrium points and the reference criterion, $ R_0 $, for judging the infectivity of infectious diseases are deduced. The global stability of the disease-free equilibrium and the uniform persistence of the disease are proved by using the persistence theory. Common infectious diseases such as COVID-19, influenza, dysentery, pertussis and tuberculosis are selected to fit periodic and non-periodic models. Fitting experiments show that the periodic environmental model can respond to epidemic fluctuations more accurately than the non-periodic model. The periodic environment model is reasonable and applicable for seasonal infectious diseases. The response effects of the periodic and non-periodic models are basically the same for perennial infectious diseases. The periodic model can inform epidemiological trends in relevant emerging infectious diseases. Taking COVID-19 as an example, the sensitivity analysis results show that the virus-related parameters in the periodic model have the most significant influence on the system. It reminds us that, even late in the pandemic, we must focus on the viral load on the environment.

    Citation: Gaohui Fan, Ning Li. Application and analysis of a model with environmental transmission in a periodic environment[J]. Electronic Research Archive, 2023, 31(9): 5815-5844. doi: 10.3934/era.2023296

    Related Papers:

  • The goal of this paper is to introduce a non-autonomous environmental transmission model for most respiratory and enteric infectious diseases to study the impact of periodic environmental changes on related infectious diseases. The transmission and decay rates of pathogens in the environment are set as periodic functions to summarize the influence of environmental fluctuations on diseases. The solutions of the model are qualitatively analyzed, and the equilibrium points and the reference criterion, $ R_0 $, for judging the infectivity of infectious diseases are deduced. The global stability of the disease-free equilibrium and the uniform persistence of the disease are proved by using the persistence theory. Common infectious diseases such as COVID-19, influenza, dysentery, pertussis and tuberculosis are selected to fit periodic and non-periodic models. Fitting experiments show that the periodic environmental model can respond to epidemic fluctuations more accurately than the non-periodic model. The periodic environment model is reasonable and applicable for seasonal infectious diseases. The response effects of the periodic and non-periodic models are basically the same for perennial infectious diseases. The periodic model can inform epidemiological trends in relevant emerging infectious diseases. Taking COVID-19 as an example, the sensitivity analysis results show that the virus-related parameters in the periodic model have the most significant influence on the system. It reminds us that, even late in the pandemic, we must focus on the viral load on the environment.



    加载中


    [1] S. Li, J. N. S. Eisenberg, I. H. Spicknall, J. S. Koopman, Dynamics and control of infections transmitted from person to person through the environment, Am. J. Epidemiol., 170 (2009), 257–265. https://doi.org/10.1093/aje/kwp116 doi: 10.1093/aje/kwp116
    [2] M. A. Safi, M. Imran, A. B. Gumel, Threshold dynamics of a non-autonomous SEIRS model with quarantine and isolation, Theory Biosci., 131 (2012), 19–30. https://doi.org/10.1007/s12064-011-0148-6 doi: 10.1007/s12064-011-0148-6
    [3] N. N. Ye, L. Zhang, Z. D. Teng, The dynamical behaviour and periodic solution in delayed nonautonomous chemostat models, J. Appl. Anal. Comput., 13 (2023), 156–183. https://doi.org/10.11948/20210452 doi: 10.11948/20210452
    [4] M. M. Gao, D. Q. Jiang, T. Hayat, A. Alsaedi, B. Ahmad, Dynamics of a stochastic chemostat competition model with plasmid-bearing and plasmid-free organisms, J. Appl. Anal. Comput., 10 (2020), 1464–1481. https://doi.org/10.11948/20190236 doi: 10.11948/20190236
    [5] J. K. K. Asamoah, C. S. Bornaa, B. Seidu, Z. Jin, Mathematical analysis of the effects of controls on transmission dynamics of SARS-CoV-2, Alexandria Eng. J., 59 (2020), 5069–5078. https://doi.org/10.1016/j.aej.2020.09.033 doi: 10.1016/j.aej.2020.09.033
    [6] K. Rajagopal, N. Hasanzadeh, F. Parastesh, I. I. Hamarash, S. Jafari, I. Hussain, A fractional-order model for the novel coronavirus (COVID-19) outbreak, Nonlinear Dyn., 101 (2020), 711–718. https://doi.org/10.1007/s11071-020-05757-6 doi: 10.1007/s11071-020-05757-6
    [7] I. Owusu-Mensah, L. Akinyemi, B. Oduro, O. S. Iyiola, A fractional order approach to modeling and simulations of the novel COVID-19, Adv. Differ. Equations, 2020 (2020). https://doi.org/10.1186/s13662-020-03141-7
    [8] A. S. Shaikh, I. N. Shaikh, K. S. Nisar, A mathematical model of COVID-19 using fractional derivative: outbreak in India with dynamics of transmission and control, Adv. Differ. Equations, 2020 (2020). https://doi.org/10.1186/s13662-020-02834-3
    [9] K. S. Nisar, S. Ahmad, A. Ullah, K. Shah, H. Alrabaiah, M. Arfan, Mathematical analysis of SIRD model of COVID-19 with Caputo fractional derivative based on real data, Results Phys., 21 (2021). https://doi.org/10.1016/j.rinp.2020.103772
    [10] S. Ahmad, A. Ullah, Q. M. Al-Mdallal, H. Khan, K. Shah, A. Khan, Fractional order mathematical modeling of COVID-19 transmission, Chaos, Solitons Fractals, 139 (2020). https://doi.org/10.1016/j.chaos.2020.110256
    [11] K. Sarkar, S. Khajanchi, J. J. Nieto, Modeling and forecasting the COVID-19 pandemic in India, Chaos, Solitons Fractals, 139 (2020). https://doi.org/10.1016/j.chaos.2020.110049
    [12] M. A. Khan, A. Atangana, E. Alzahrani, Fatmawati, The dynamics of COVID-19 with quarantined and isolation, Adv. Differ. Equations, 2020 (2020). https://doi.org/10.1186/s13662-020-02882-9
    [13] M. W. Shen, J. Zu, C. K. Fairley, J. A. Pagán, L. An, Z. W. Du, et al., Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use, Vaccine, 39 (2021), 2295–2302. https://doi.org/10.1016/j.vaccine.2021.02.056 doi: 10.1016/j.vaccine.2021.02.056
    [14] S. Bentout, A. Tridane, S. Djilali, T. M. Touaoula, Age-structured modeling of COVID-19 epidemic in the USA, UAE and Algeria, Alexandria Eng. J., 60 (2021), 401–411. https://doi.org/10.1016/j.aej.2020.08.053 doi: 10.1016/j.aej.2020.08.053
    [15] F. A. Rihan, H. J. Alsakaji, C. Rajivganthi, Stochastic SIRC epidemic model with time-delay for COVID-19, Adv. Differ. Equations, 2020 (2020). https://doi.org/10.1186/s13662-020-02964-8
    [16] C. C. Yin, W. W. Zhao, P. Pereira, Meteorological factors' effects on COVID-19 show seasonality and spatiality in Brazil, Environ. Res., 208 (2022). https://doi.org/10.1016/j.envres.2022.112690
    [17] Y. J. Zhao, J. P. Huang, L. Zhang, S. Y. Chen, J. F. Gao, H. Jiao, The global transmission of new coronavirus variants, Environ. Res., 206 (2022). https://doi.org/10.1016/j.envres.2021.112240
    [18] Z. W. Huang, J. P. Huang, Q. Q. Gu, P. Y. Du, H. B. Liang, Q. Dong, Optimal temperature zone for the dispersal of COVID-19, Sci. Total Environ., 736 (2020). https://doi.org/10.1016/j.scitotenv.2020.139487
    [19] Y. C. Zheng, Y. P. Wang, How Seasonality and Control Measures Jointly Determine the Multistage Waves of the COVID-19 Epidemic: A Modelling Study and Implications, Int. J. Environ. Res. Public Health, 19 (2022). https://doi.org/10.3390/ijerph19116404
    [20] C. W. Chukwu, Modelling fractional-order dynamics of COVID-19 with environmental transmission and vaccination: A case study of Indonesia, AIMS Math., 7 (2022), 4416–4438. https://doi.org/10.3934/math.2022246 doi: 10.3934/math.2022246
    [21] M. A. A. Oud, A. Ali, H. Alrabaiah, S. Ullah, M. A. Khan, S. Islam, A fractional order mathematical model for COVID-19 dynamics with quarantine, isolation, and environmental viral load, Adv. Differ. Equations, 2021 (2021). https://doi.org/10.1186/s13662-021-03265-4
    [22] J. K. K. Asamoah, M. A. Owusu, Z. Jin, F. T. Oduro, A. Abidemi, E. O. Gyasi, Global stability and cost-effectiveness analysis of COVID-19 considering the impact of the environment: using data from Ghana, Chaos, Solitons Fractals, 140 (2020). https://doi.org/10.1016/j.chaos.2020.110103
    [23] S. S. Musa, A. Yusuf, S. Zhao, Z. U. Abdullahi, H. Abu-Odah, F. T. Saad, et al., Transmission dynamics of COVID-19 pandemic with combined effects of relapse, reinfection and environmental contribution: A modeling analysis, Results Phys., 38 (2022). https://doi.org/10.1016/j.rinp.2022.105653
    [24] H. d. Graaf, M. Ibrahim, A. R. Hill, D. Gbesemete, A. T. Vaughan, A. Gorringe, et al., Controlled human infection with Bordetella pertussis induces asymptomatic, immunizing colonization, Clin. Infect. Dis., 71 (2020), 403–411. https://doi.org/10.1093/cid/ciz840 doi: 10.1093/cid/ciz840
    [25] A. S. Richards, B. Sossen, J. C. Emery, K. C. Horton, T. Heinsohn, B. Frascella, et al., Quantifying progression and regression across the spectrum of pulmonary tuberculosis: a data synthesis study, Lancet Global Health, 11 (2023), 684–692. https://doi.org/10.1016/S2214-109X(23)00082-7 doi: 10.1016/S2214-109X(23)00082-7
    [26] Y. Gu, N. Komiya, H. Kamiya, Y. Yasui, K. Taniguchi, N. Okabe, Pandemic (H1N1) 2009 Transmission during Presymptomatic Phase, Japan, Emerging Infect. Dis., 17 (2011), 1737–1739. https://doi.org/10.3201/eid1709.101411 doi: 10.3201/eid1709.101411
    [27] M. P. Dafilis, F. Frascoli, J. McVernon, J. M. Heffernan, J. M. McCaw, Dynamical crises, multistability and the influence of the duration of immunity in a seasonally-forced model of disease transmission, Theor. Biol. Med. Modell., 11 (2014). https://doi.org/10.1186/1742-4682-11-43
    [28] C. Ward, A. Best, How seasonal variations in birth and transmission rates impact population dynamics in a basic SIR model, Ecol. Complexity, 47 (2021), 100949. https://doi.org/10.1016/j.ecocom.2021.100949 doi: 10.1016/j.ecocom.2021.100949
    [29] A. Chithra, I. R. Mohamed, Multiple attractors and strange nonchaotic dynamical behavior in a periodically forced system, Nonlinear Dyn., 105 (2021), 3615–3635. https://doi.org/10.1007/s11071-021-06608-8 doi: 10.1007/s11071-021-06608-8
    [30] J. P. S. M. de Carvalho, A. A. Rodrigues, Strange attractors in a dynamical system inspired by a seasonally forced SIR model, Phys. D, 434 (2022). https://doi.org/10.1016/j.physd.2022.133268
    [31] J. P. S. M. de Carvalho, A. A. Rodrigues, SIR model with vaccination: bifurcation analysis, Qual. Theory Dyn. Syst., 22 (2023). https://doi.org/10.1007/s12346-023-00802-2
    [32] W. D. Wang, X. Q. Zhao, Threshold dynamics for compartmental epidemic models in periodic environments, J. Dyn. Differ. Equations, 20 (2008), 699–717. https://doi.org/10.1007/s10884-008-9111-8 doi: 10.1007/s10884-008-9111-8
    [33] F. Zhang, X. Q. Zhao, A periodic epidemic model in a patchy environment, J. Math. Anal. Appl., 325 (2007), 496–516. https://doi.org/10.1016/j.jmaa.2006.01.085 doi: 10.1016/j.jmaa.2006.01.085
    [34] P. O. Lolika, S. Mushayabasa, C. P. Bhunu, C. Modnak, J. Wang, Modeling and analyzing the effects of seasonality on brucellosis infection, Chaos, Solitons Fractals, 104 (2017), 338–349. https://doi.org/10.1016/j.chaos.2017.08.027 doi: 10.1016/j.chaos.2017.08.027
    [35] Z. M. Li, T. L. Zhang, Analysis of a COVID-19 epidemic model with seasonality, Bull. Math. Biol., 84 (2022). https://doi.org/10.1007/s11538-022-01105-4
    [36] C. Y. Yang, P. O. Lolika, S. Mushayabasa, J. Wang, Modeling the spatiotemporal variations in brucellosis transmission, Nonlinear Anal. Real World Appl., 38 (2017), 49–67. https://doi.org/10.1016/j.nonrwa.2017.04.006 doi: 10.1016/j.nonrwa.2017.04.006
    [37] X. Zhang, J. F. Wu, L. M. Smith, X. Li, O. Yancey, A. Franzblau, et al., Monitoring SARS-CoV-2 in air and on surfaces and estimating infection risk in buildings and buses on a university campus, J. Exposure Sci. Environ. Epidemiol., 32 (2022), 751–758. https://doi.org/10.1038/s41370-022-00442-9 doi: 10.1038/s41370-022-00442-9
    [38] L. Stone, R. Olinky, A. Huppert, Seasonal dynamics of recurrent epidemics, Nature, 446 (2007), 533–536. https://doi.org/10.1038/nature05638 doi: 10.1038/nature05638
    [39] X. X. Wu, J. N. Liu, C. L. Li, J. Yin, Impact of climate change on dysentery: Scientific evidences, uncertainty, modeling and projections, Sci. Total Environ., 714 (2020). https://doi.org/10.1016/j.scitotenv.2020.136702
    [40] National Health Commission of the People's Republic of China, 2023. Available from: http://www.nhc.gov.cn/.
    [41] J. Mondal, S. Khajanchi, Mathematical modeling and optimal intervention strategies of the 30 COVID-19 outbreak, Nonlinear Dyn., 109 (2022), 177–202. https://doi.org/10.1007/s11071-022-07235-7 doi: 10.1007/s11071-022-07235-7
    [42] J. K. K. Asamoah, Z. Jin, G. Q. Sun, M. Y. Li, A deterministic model for Q fever transmission dynamics within dairy cattle herds: using sensitivity analysis and optimal controls, Comput. Math. Methods Med., 2020 (2020). https://doi.org/10.1155/2020/6820608
    [43] P. Driessche, J. Watmough, Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6
    [44] P. Driessche, J. Watmough, Further notes on the basic reproduction number, Math. Epidemiol., 1945 (2008), 159–178. https://doi.org/10.1007/978-3-540-78911-6_6 doi: 10.1007/978-3-540-78911-6_6
    [45] X. Q. Zhao, Dynamical Systems in Population Biology, Springer-Verlag, New York, 2003. https://doi.org/10.1007/978-3-319-56433-3
    [46] Shanghai Municipal Health Commission, 2022. Available from: https://wsjkw.sh.gov.cn/xwzx/.
    [47] I. Ullah, S. Ahmad, Q. Mdallal, Z. A. Khan, H. Khan, A. Khan, Stability analysis of a dynamical model of tuberculosis with incomplete treatment, Adv. Differ. Equations, 2020 (2020). https://doi.org/10.1186/s13662-020-02950-0
    [48] T. F. Hou, G. J. Lan, S. L. Yuan, T. H. Zhang, Threshold dynamics of a stochastic SIHR epidemic model of COVID-19 with general population-size dependent contact rate, Math. Biosci. Eng., 19 (2022), 4217–4236. https://doi.org/10.3934/mbe.2022195 doi: 10.3934/mbe.2022195
    [49] J. Danane, K. Allali, Z. Hammouch, K. S. Nisar, Mathematical analysis and simulation of a stochastic COVID-19 Levy jump model with isolation strategy, Results Phys., 23 (2021). https://doi.org/10.1016/j.rinp.2021.103994
  • Reader Comments
  • © 2023 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(585) PDF downloads(54) Cited by(0)

Article outline

Figures and Tables

Figures(12)  /  Tables(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog