Research article

Modelling and analysis of the HIV/AIDS epidemic with fast and slow asymptomatic infections in China from 2008 to 2021

  • Acadecmic Editor: Hao Wang
  • Received: 01 August 2023 Revised: 29 October 2023 Accepted: 30 October 2023 Published: 17 November 2023
  • The aim of this paper is to investigate the spread of the HIV/AIDS epidemic in China during 2008–2021. A new mathematical model is proposed to study the dynamics of HIV transmission with acute infection, fast asymptomatic infections, and slow asymptomatic infections. The basic reproduction number is obtained by the next-generation matrix method. A quantitative analysis of the model, including the local behavior, global behavior, and permanence, is performed. Numerical simulations are presented to enhance the results of these analyses. The behavior or the model's parameters are estimated from real data. A sensitivity analysis shows that the proportion of asymptomatic infections co-infected with other diseases significantly affects the basic reproduction number. We further analyze the impact of implementing single and multiple measure(s) in parallel with the epidemic. The study results conclude that multiple measures are more effective in controlling the spread of AIDS compared to just one. The HIV epidemic can be effectively curbed by reducing the contact rate between fast asymptomatic infected individuals and susceptible populations, increasing the early diagnosis and screening of HIV-infected individuals co-infected with other diseases, and treating co-infected patients promptly.

    Citation: Nawei Chen, Shenglong Chen, Xiaoyu Li, Zhiming Li. Modelling and analysis of the HIV/AIDS epidemic with fast and slow asymptomatic infections in China from 2008 to 2021[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 20770-20794. doi: 10.3934/mbe.2023919

    Related Papers:

  • The aim of this paper is to investigate the spread of the HIV/AIDS epidemic in China during 2008–2021. A new mathematical model is proposed to study the dynamics of HIV transmission with acute infection, fast asymptomatic infections, and slow asymptomatic infections. The basic reproduction number is obtained by the next-generation matrix method. A quantitative analysis of the model, including the local behavior, global behavior, and permanence, is performed. Numerical simulations are presented to enhance the results of these analyses. The behavior or the model's parameters are estimated from real data. A sensitivity analysis shows that the proportion of asymptomatic infections co-infected with other diseases significantly affects the basic reproduction number. We further analyze the impact of implementing single and multiple measure(s) in parallel with the epidemic. The study results conclude that multiple measures are more effective in controlling the spread of AIDS compared to just one. The HIV epidemic can be effectively curbed by reducing the contact rate between fast asymptomatic infected individuals and susceptible populations, increasing the early diagnosis and screening of HIV-infected individuals co-infected with other diseases, and treating co-infected patients promptly.



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    [1] AIDS.gov, 2022. Available from: https://aids.gov/index.html.
    [2] M. S. Gottlieb, Pneumocystis Pneumonia-Los Angeles, Am. J. Public Health, 96 (2006), 980–981. https://doi.org/10.2105/AJPH.96.6.980 doi: 10.2105/AJPH.96.6.980
    [3] HIV, 2023. Available from: https://www.who.int/data/gho/data/themes/hiv-aids.
    [4] X. Sun, N. Wang, D. Li, X. Zheng, S. Qu, L. Wang, et al., The development of HIV/AIDS surveillance in China, Aids, 21 (2007), S33–S38. https://doi.org/10.1097/01.aids.0000304694.54884.06 doi: 10.1097/01.aids.0000304694.54884.06
    [5] Y. Xiao, S. Kristensen, J. Sun, L. Lu, S. H. Vermund, Expansion of HIV/AIDS in China: lessons from Yunnan Province, Soc. Sci. Med., 64 (2007), 665–675. https://doi.org/10.1016/j.socscimed.2006.09.019 doi: 10.1016/j.socscimed.2006.09.019
    [6] M. Jia, H. Luo, Y. Ma, N. Wang, K. Smith, J. Mei, et al., The HIV epidemic in Yunnan province, China, 1989–2007, J. Acquired Immune Defic. Syndr., 53 (2010), S34–S40. https://doi.org/10.1097/QAI.0b013e3181c7d6ff doi: 10.1097/QAI.0b013e3181c7d6ff
    [7] N. He, Research progress in the epidemiology of HIV/AIDS in China, China CDC Wkly., 3 (2021), 1022. https://doi.org/10.46234/ccdcw2021.249 doi: 10.46234/ccdcw2021.249
    [8] Y. Ding, Z. Ma, J. He, X. Xu, S. Qiao, L. Xu, et al., Evolving HIV epidemiology in mainland China: 2009–2018, Curr. HIV/AIDS Rep., 16 (2019), 423–430. https://doi.org/10.1007/s11904-019-00468-z doi: 10.1007/s11904-019-00468-z
    [9] Z. Wu, J. Chen, S. R. Scott, J. M. McGoogan, History of the HIV epidemic in China, Curr. HIV/AIDS Rep., 16 (2019), 458–466. https://doi.org/10.1007/s11904-019-00471-4 doi: 10.1007/s11904-019-00471-4
    [10] Y. Lu, S. Tang, Y. Qin, V. Harypursat, H. Wu, Y. Chen, Changes of human immunodeficiency virus (HIV) burden globally and in China over three decades: a secondary analysis of global HIV statistics, Chin. Med. J., 10 (2022), 1097. https://doi.org/10.1097/CM9.0000000000002500 doi: 10.1097/CM9.0000000000002500
    [11] H. Yang, Y. Li, F. He, F. Yuan, L. Liu, L. Li, et al., Demographic characteristics and hot-spot areas of recent infections among new HIV diagnoses in Sichuan, China, between 2018 and 2020, Infect. Drug Resist., 16 (2023), 779–789. https://doi.org/10.2147/IDR.S394828 doi: 10.2147/IDR.S394828
    [12] L. Wang, N. Zhao, Y. Wang, K. Sun, Y. Wang, S. Huang, et al., Impact of the COVID-19 pandemic and the dynamic COVID-zero strategy on HIV incidence and mortality in China, BMC Public Health, 23 (2023), 1–11. https://doi.org/10.1186/s12889-023-15268-9 doi: 10.1186/s12889-023-15268-9
    [13] J. M. Hyman, J. Li, E. A. Stanley, The differential infectivity and staged progression models for the transmission of HIV, Math. Biosci., 155 (1999), 77–109. https://doi.org/10.1016/S0025-5564(98)10057-3 doi: 10.1016/S0025-5564(98)10057-3
    [14] C. C. McCluskey, A model of HIV/AIDS with staged progression and amelioration, Math. Biosci., 181 (2003), 1–16. https://doi.org/10.1016/S0025-5564(02)00149-9 doi: 10.1016/S0025-5564(02)00149-9
    [15] R. Naresh, A. Tripathi, D. Sharma, A nonlinear HIV/AIDS model with contact tracing, Appl. Math. Comput., 217 (2011), 9575–9591. https://doi.org/10.1016/j.amc.2011.04.033 doi: 10.1016/j.amc.2011.04.033
    [16] K. Hattaf, H. Dutta, Modeling the dynamics of viral infections in presence of latently infected cells, Chaos Solitons Fractals, 136 (2020), 109916. https://doi.org/10.1016/j.chaos.2020.109916 doi: 10.1016/j.chaos.2020.109916
    [17] R. Wattanasirikosone, C. Modnak, Analysing transmission dynamics of HIV/AIDS with optimal control strategy and its controlled state, J. Biol. Dyn., 16 (2022), 499–527. https://doi.org/10.1080/17513758.2022.2096934 doi: 10.1080/17513758.2022.2096934
    [18] L. Xue, Y. Sun, X. Ren, W. Sun, Modelling the transmission dynamics and optimal control strategies for HIV infection in China, J. Biol. Dyn., 17 (2023), 2174275. https://doi.org/10.1080/17513758.2023.2174275 doi: 10.1080/17513758.2023.2174275
    [19] K. R. Cheneke, K. P. Rao, G. K. Edessa, Bifurcation and stability analysis of HIV transmission model with optimal control, J. Math., 2021 (2021), 1–14. https://doi.org/10.1155/2021/7471290 doi: 10.1155/2021/7471290
    [20] World Health Organization HIV/AIDS, 2023. Available from: http://www.who.int/mediacentre/factsheets/fs360/en/.
    [21] L. B. Shrestha, G. K. Yadav, S. Pradhan, A. Sharma, T. Pandit, R. Chhetry, et al., Co-infection of Hepatitis B and Hepatitis C among HIV-infected patients: A cross-sectional study from tertiary care hospital of eastern Nepal, Plos One, 17 (2022), e0264791. https://doi.org/10.1371/journal.pone.0264791 doi: 10.1371/journal.pone.0264791
    [22] T. Getaneh, A. Negesse, G. Dessie, M. Desta, The impact of tuberculosis co-infection on virological failure among adults living with HIV in Ethiopia: a systematic review and meta-analysis, J. Clin. Tuberc. Other Mycobact. Dis., 27 (2022), 100310. https://doi.org/10.1016/j.jctube.2022.100310 doi: 10.1016/j.jctube.2022.100310
    [23] J. Bruchfeld, M. Correia-Neves, G. Källenius, Tuberculosis and HIV coinfection, Cold Spring Harbor Perspect. Med., 5 (2015), a017871. https://doi.org/10.1101/cshperspect.a017871 doi: 10.1101/cshperspect.a017871
    [24] L. C. K. Bell, M. Noursadeghi, Pathogenesis of HIV-1 and Mycobacterium tuberculosis co-infection, Nat. Rev. Microbiol., 16 (2018), 80–90. https://doi.org/10.1038/nrmicro.2017.128 doi: 10.1038/nrmicro.2017.128
    [25] S. D. Lawn, AIDS in Africa: the impact of coinfections on the pathogenesis of HIV-1 infection, J. Infect., 48 (2004), 1–12. https://doi.org/10.1016/j.jinf.2003.09.001 doi: 10.1016/j.jinf.2003.09.001
    [26] P. van den 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
    [27] X. Q. Zhao, Dynamical Systems in Population Biology, Springer, New York, 2003.
    [28] National Bureau of Statistics, 2023. Available from: https://data.stats.gov.cn/index.html.
    [29] Z. Li, Z. Teng, H. Miao, Modeling and control for HIV/AIDS transmission in China based on data from 2004 to 2016, Comput. Math. Methods Med., 2017 (2017), 8935314. https://doi.org/10.1016/j.jctube.2022.100310 doi: 10.1016/j.jctube.2022.100310
    [30] L. Li, L. X. Du, Z. Yan, J. Zhang, Y. P. Wu, A method for parameters estimation in a dynamical model of Ebola virus transmission in Sierra Leone, Complexity, 2020 (2020), 1–9. https://doi.org/10.1155/2020/9172835 doi: 10.1155/2020/9172835
    [31] N. Chen, C. Xiong, W. Du, C. Wang, X. Lin, Z. Chen, An improved genetic algorithm coupling a back-propagation neural network model (IGA-BPNN) for water-level predictions, Water, 11 (2019), 1795. https://doi.org/10.3390/w11091795 doi: 10.3390/w11091795
    [32] A. Misevičius, D. Kuznecovaitė, J. Platužienė, Some further experiments with crossover operators for genetic algorithms, Informatica, 29 (2018), 499–516. https://doi.org/10.15388/Informatica.2018.178 doi: 10.15388/Informatica.2018.178
    [33] Z. Chen, J. Zhou, R. Sun, L. Kang, A new evolving mechanism of genetic algorithm for multi-constraint intelligent camera path planning, Soft Comput., 25 (2021), 5073–5092. https://doi.org/10.1007/s00500-020-05510-6 doi: 10.1007/s00500-020-05510-6
    [34] A. Ragalo, N. Pillay, Evolving dynamic fitness measures for genetic programming, Expert Syst. Appl., 109 (2018), 162–187. https://doi.org/10.1016/j.eswa.2018.03.060 doi: 10.1016/j.eswa.2018.03.060
    [35] P. Wu, H. Zhao, Mathematical analysis of an age-structured HIV/AIDS epidemic model with HAART and spatial diffusion, Nonlinear Anal. Real World Appl., 60 (2021), 103289. https://doi.org/10.1016/j.nonrwa.2021.103289 doi: 10.1016/j.nonrwa.2021.103289
    [36] H. Zhao, P. Wu, S. Ruan, Dynamic analysis and optimal control of a three-age-class HIV/AIDS epidemic model in China, Discrete Contin. Dyn. Syst. Ser. B, 25 (2020). https://doi.org/10.3934/dcdsb.2020070 doi: 10.3934/dcdsb.2020070
    [37] P. Wu, S. Ahmed, X. Wang, H. Wang, PrEP intervention in the mitigation of HIV/AIDS epidemics in China via a data-validated age-structured model, Bull. Math. Biol., 85 (2023), 41. https://doi.org/10.1007/s11538-023-01145-4 doi: 10.1007/s11538-023-01145-4
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