Research article Special Issues

Parameter estimation of modeling schistosomiasis transmission for four provinces in China

  • Received: 07 November 2018 Accepted: 27 December 2018 Published: 30 January 2019
  • According to monitoring data of Anhui, Jiangxi, Hubei, Jiangsu Provinces, in this paper the transmission of schistosomiasis is studied based on Barbour's mathematical model. The values of the basic reproduction number and key parameters are obtained with two methods. The first method is to calculate directly by using parameter values in references. The second one is to estimate parameter values by the methods of noise measurement and data smoothing and then to obtain new values of basic reproduction number. Comparing these two methods, we found the second method is good to fit the data. This parameter values can be used as reference values in other provinces. Finally, some numerical simulations are carried out to discuss the development trend of prevalence of humans and snails in each province. It is found that schistosomiasis in four provinces is expected to be eliminated with the improvement or maintenance of the standards of prevention and control. Furthermore, the time needed is di erent in the di erent four provinces.

    Citation: Long-xing Qi, Yanwu Tang, Shou-jing Tian. Parameter estimation of modeling schistosomiasis transmission for four provinces in China[J]. Mathematical Biosciences and Engineering, 2019, 16(2): 1005-1020. doi: 10.3934/mbe.2019047

    Related Papers:

    [1] Michael Herty, Lorenzo Pareschi, Sonja Steffensen . Mean--field control and Riccati equations. Networks and Heterogeneous Media, 2015, 10(3): 699-715. doi: 10.3934/nhm.2015.10.699
    [2] Nastassia Pouradier Duteil . Mean-field limit of collective dynamics with time-varying weights. Networks and Heterogeneous Media, 2022, 17(2): 129-161. doi: 10.3934/nhm.2022001
    [3] Seung-Yeal Ha, Jeongho Kim, Jinyeong Park, Xiongtao Zhang . Uniform stability and mean-field limit for the augmented Kuramoto model. Networks and Heterogeneous Media, 2018, 13(2): 297-322. doi: 10.3934/nhm.2018013
    [4] Martino Bardi . Explicit solutions of some linear-quadratic mean field games. Networks and Heterogeneous Media, 2012, 7(2): 243-261. doi: 10.3934/nhm.2012.7.243
    [5] András Bátkai, Istvan Z. Kiss, Eszter Sikolya, Péter L. Simon . Differential equation approximations of stochastic network processes: An operator semigroup approach. Networks and Heterogeneous Media, 2012, 7(1): 43-58. doi: 10.3934/nhm.2012.7.43
    [6] Fabio Camilli, Italo Capuzzo Dolcetta, Maurizio Falcone . Preface. Networks and Heterogeneous Media, 2012, 7(2): i-ii. doi: 10.3934/nhm.2012.7.2i
    [7] Olivier Guéant . New numerical methods for mean field games with quadratic costs. Networks and Heterogeneous Media, 2012, 7(2): 315-336. doi: 10.3934/nhm.2012.7.315
    [8] Michele Gianfelice, Enza Orlandi . Dynamics and kinetic limit for a system of noiseless d-dimensional Vicsek-type particles. Networks and Heterogeneous Media, 2014, 9(2): 269-297. doi: 10.3934/nhm.2014.9.269
    [9] Mattia Bongini, Massimo Fornasier, Oliver Junge, Benjamin Scharf . Sparse control of alignment models in high dimension. Networks and Heterogeneous Media, 2015, 10(3): 647-697. doi: 10.3934/nhm.2015.10.647
    [10] Maria Teresa Chiri, Xiaoqian Gong, Benedetto Piccoli . Mean-field limit of a hybrid system for multi-lane car-truck traffic. Networks and Heterogeneous Media, 2023, 18(2): 723-752. doi: 10.3934/nhm.2023031
  • According to monitoring data of Anhui, Jiangxi, Hubei, Jiangsu Provinces, in this paper the transmission of schistosomiasis is studied based on Barbour's mathematical model. The values of the basic reproduction number and key parameters are obtained with two methods. The first method is to calculate directly by using parameter values in references. The second one is to estimate parameter values by the methods of noise measurement and data smoothing and then to obtain new values of basic reproduction number. Comparing these two methods, we found the second method is good to fit the data. This parameter values can be used as reference values in other provinces. Finally, some numerical simulations are carried out to discuss the development trend of prevalence of humans and snails in each province. It is found that schistosomiasis in four provinces is expected to be eliminated with the improvement or maintenance of the standards of prevention and control. Furthermore, the time needed is di erent in the di erent four provinces.




    [1] A. D. Barbour, Modelling the transmission of schistosomiasis: an introductory view, Am. J. Trop. Med. Hyg., 55 (1996), 135–143.
    [2] Y. Y. Chen, S. X. Cai, J. B. Liu, X. B. Huang, Z. M. Su, Z. W. Tu, X. W. Shan, G. Li, Assessment of schistosomiasis endemic situation in national monitoring sites in Hubei Province from 2005 to 2010 (in Chinese), Chin. J. Schisto. Control, 26 (2014), 260–264.
    [3] Y. Y. Chen, Spatial endemic situation and forecast for schistosomiasis in Hubei Province (in Chinese), Ph.D thesis Huadong University in China, 2014.
    [4] Z. Chen, X. N. Gu, S. B. LV, Y. F. Li, W. S. Jiang, C. Q. Hang, J. Ge, D. D. Lin, Endemic situation of schistosomiasis in the national monitoring sites in Jiangxi Province from 2005 to 2014 (in Chinese). J. Trop. Dis. Parasitol. 13 (2015), 193–196.
    [5] F. H. Gao, S. Q. Zhang, T. P. Wang, J. C. He, X. J. Xu, T. T. Li, G. H. Zhang, H. Wang, Endemic status of schistosomiasis in Anhui Province in 2015(in Chinese), J. Trop. Dis. Parasitol., 14 (2016), 137–140.
    [6] F. H. Gao, S. Q. Zhang, T. P. Wang, J. C. He, X. J. Xu, T. T. Li, G. H. Zhang, H. Wang, Endemic status of schistosomiasis in Anhui Province in 2016(in Chinese), J. Trop. Dis. Parasitol., 15 (2017), 125–130.
    [7] S. J. Gao, Y. Y. He, Y. J. Liu, G. J. Yang, X. N. Zhou, Field transmission intensity of Schistosoma japonicum measured by basic reproduction ratio from modified Barbour's model, Parasites Vector, 6 (2013), 141–151.
    [8] X. N. Gu, C. Q. Hang, H. G. Chen, S. B. Lv, Z. Chen, J. Sun, The analysis of national monitoring results of schistosomiasis in Jiangxi in 2005 (in Chinese), J. Trop. Dis. Parasitology, 4 (2006), 202–204.
    [9] Y. P. Li, Y. B. Zhou, Q. W. Jiang, Progress of research on mathematical model for transmission of schistosomiasis (in Chinese), Chin. J. Schisto. Control, 21 (2009), 568–571.
    [10] D. D. Lin, X. N. Gu, Z. Chen, X. J. Zeng, H. Y. Liu, Z. J. Li, W. S. Jiang, S. B. Lv, Z. L. Gao, H. G, Chen, Assessment and analysis of risks of realizing schistosomiasis transmission control in Jiangxi Province (in Chinese), Chin. J. Schisto. Control, 25 (2013), 348–366.
    [11] G. Macdonald, The dynamics of helminth infections, with special reference to schistosomes, Trans. R. Soc. Trop. Med. Hyg, 59 (1965), 489–506.
    [12] L. X. Qi, J. A. Cui, Qualitative analysis for Barbour's schistosomiasis model with diffusion, J. Biomath., 27 (2012), 54–64.
    [13] L. X. Qi, S. J. Tian, J. A. Cui, T. P. Wang, Multiple infection leads to backward bifurcation for a schistosomiasis model, Math. Biosci. Eng., in press.
    [14] L. X. Qi, M. Xue, J. A. Cui, Q. Z. Wang, T. P. Wang, Schistosomiasis transmission model and its control in Anhui province, B. Math. Biol., 80 (2018), 2435–2451.
    [15] J. O. Ramsay, G. Hooker, D. Campbell, J. Cao, Parameter estimation for differential equations: a generalized smoothing approach, J. R. Statist. Soc. B, 69 (2007), 741–796.
    [16] L. D. Wang, The key to the control of schistosomiasis in China is good management of livestock manure (in Chinese), Chin. J. Epidemiol., 26 (2005), 929–930.
    [17] L. D.Wang, H. G. Chen, J. G. Guo, X. J. Zeng, X. L. Hong, J. J. Xiong, X. H.Wu, X. H.Wang, L. Y.Wang, G. Xia, Y. Hao, D. P. Chin, X. N. Zhou, A strategy to control transmission of Schistosoma japonicum in China, N. Engl. J. Med., 360 (2009), 121–128.
    [18] T. P.Wang, F. Zhao, S. Q. Zhang, Z. J. Zhang, F. H. Gao, Y. B. Zhou, J. C. He, Q.W. Jiang, Spatial temporal clustering analysis of schistosomiasis in Anhui from 2000 to 2008 (in Chinese), J. Trop. Dis. Parasitol., 9 (2011), 127–130.
    [19] K. C. Wu, Mathematical model and transmission dynamics of schistosomiasis and its application (in Chinese), China Trop. Med., 5 (2005), 837–843.
    [20] K. Yang, G. J. Yang, Q. B. Hong, Y. X. Huang, L. P. Sun, Y. Gao, Y. Gao, L. H. Zhang, J. B. Yang, H. R. Zhu, Y. S. Liang, Monitoring of schistosomiasis in Jiangsu Province, China, 2005-2010 (in Chinese), Chin. J. Schisto. Control, 24 (2012), 527–532.
    [21] S. Q. Zhang, T. P.Wang, J. H. Ge, C. G. Tao, G. H. Zhang, D. B. Lv, W. Z.Wu, Q. Z.Wang, Influence on the diffusion of snail by flooding in Anhui province (in Chinese), J. Trop. Dis. Parasitol., 2 (2004), 90–93.
    [22] S. Q. Zhang, F. H. Gao, J. C. He, G. H. Zhang, H. Wang, T. P. Wang, Trend analysis of schistosomiasis endemic situation in Anhui Province from 2004 to 2014 (in Chinese), Chin. J. Schisto. Control, 27 (2015), 235–240.
  • This article has been cited by:

    1. Michael Herty, Dante Kalise, 2018, Suboptimal nonlinear feedback control laws for collective dynamics, 978-1-5386-6089-8, 556, 10.1109/ICCA.2018.8444303
    2. Melanie Harms, Simone Bamberger, Eva Zerz, Michael Herty, On d-Collision-Free Dynamical Systems, 2022, 55, 24058963, 25, 10.1016/j.ifacol.2022.11.303
    3. Fuguo Xu, Qiaobin Fu, Tielong Shen, PMP-based numerical solution for mean field game problem of general nonlinear system, 2022, 146, 00051098, 110655, 10.1016/j.automatica.2022.110655
    4. M. K. Banda, M. Herty, T. Trimborn, 2020, Chapter 7, 978-3-030-50449-6, 133, 10.1007/978-3-030-50450-2_7
    5. Michael Herty, Anna Thunen, 2021, Consistent Control of a Stackelberg Game with Infinitely many Followers, 978-1-6654-3659-5, 918, 10.1109/CDC45484.2021.9682798
    6. Michael Herty, Hui Yu, 2016, Boundary stabilization of hyperbolic conservation laws using conservative finite volume schemes, 978-1-5090-1837-6, 5577, 10.1109/CDC.2016.7799126
    7. Giacomo Albi, Michael Herty, Dante Kalise, Chiara Segala, Moment-Driven Predictive Control of Mean-Field Collective Dynamics, 2022, 60, 0363-0129, 814, 10.1137/21M1391559
    8. Giacomo Albi, Emiliano Cristiani, Lorenzo Pareschi, Daniele Peri, 2020, Chapter 8, 978-3-030-50449-6, 159, 10.1007/978-3-030-50450-2_8
    9. Michael Herty, Sonja Steffensen, Anna Thünen, Multiscale control of Stackelberg games, 2022, 200, 03784754, 468, 10.1016/j.matcom.2022.04.028
    10. Marco Caponigro, Benedetto Piccoli, Francesco Rossi, Emmanuel Trélat, Mean-field sparse Jurdjevic–Quinn control, 2017, 27, 0218-2025, 1223, 10.1142/S0218202517400140
    11. Bertram Düring, Lorenzo Pareschi, Giuseppe Toscani, Kinetic models for optimal control of wealth inequalities, 2018, 91, 1434-6028, 10.1140/epjb/e2018-90138-1
    12. Yan Ma, Minyi Huang, Linear quadratic mean field games with a major player: The multi-scale approach, 2020, 113, 00051098, 108774, 10.1016/j.automatica.2019.108774
    13. Michael Herty, Mattia Zanella, Performance bounds for the mean-field limit of constrained dynamics, 2017, 37, 1553-5231, 2023, 10.3934/dcds.2017086
    14. Aylin Aydoğdu, Marco Caponigro, Sean McQuade, Benedetto Piccoli, Nastassia Pouradier Duteil, Francesco Rossi, Emmanuel Trélat, 2017, Chapter 3, 978-3-319-49994-9, 99, 10.1007/978-3-319-49996-3_3
    15. Giacomo Albi, Lorenzo Pareschi, Mattia Zanella, Boltzmann Games in Heterogeneous Consensus Dynamics, 2019, 175, 0022-4715, 97, 10.1007/s10955-019-02246-y
    16. Michael Herty, Lorenzo Pareschi, Sonja Steffensen, 2019, Chapter 5, 978-3-030-20296-5, 149, 10.1007/978-3-030-20297-2_5
    17. A. Medaglia, G. Colelli, L. Farina, A. Bacila, P. Bini, E. Marchioni, S. Figini, A. Pichiecchio, M. Zanella, Uncertainty quantification and control of kinetic models of tumour growth under clinical uncertainties, 2022, 141, 00207462, 103933, 10.1016/j.ijnonlinmec.2022.103933
    18. Giacomo Albi, Federica Ferrarese, Chiara Segala, 2021, Chapter 5, 978-3-030-91645-9, 97, 10.1007/978-3-030-91646-6_5
    19. Minyi Huang, Mengjie Zhou, Linear Quadratic Mean Field Games: Asymptotic Solvability and Relation to the Fixed Point Approach, 2020, 65, 0018-9286, 1397, 10.1109/TAC.2019.2919111
    20. Eva Zerz, Michael Herty, Collision-Free Dynamical Systems , 2019, 52, 24058963, 72, 10.1016/j.ifacol.2019.11.029
    21. Giacomo Albi, Michael Herty, Chiara Segala, Robust Feedback Stabilization of Interacting Multi-agent Systems Under Uncertainty, 2024, 89, 0095-4616, 10.1007/s00245-023-10078-2
    22. Xiaoqian Gong, Michael Herty, Benedetto Piccoli, Giuseppe Visconti, Crowd Dynamics: Modeling and Control of Multiagent Systems, 2023, 6, 2573-5144, 261, 10.1146/annurev-control-060822-123629
    23. Christian Fiedler, Michael Herty, Sebastian Trimpe, Mean-Field Limits for Discrete-Time Dynamical Systems via Kernel Mean Embeddings, 2023, 7, 2475-1456, 3914, 10.1109/LCSYS.2023.3341280
    24. Martin Gugat, Michael Herty, Jiehong Liu, Chiara Segala, The turnpike property for high‐dimensional interacting agent systems in discrete time, 2024, 45, 0143-2087, 2557, 10.1002/oca.3172
    25. Michael Herty, Yizhou Zhou, Exponential turnpike property for particle systems and mean-field limit, 2025, 0956-7925, 1, 10.1017/S0956792524000871
    26. Giacomo Albi, Sara Bicego, Michael Herty, Yuyang Huang, Dante Kalise, Chiara Segala, 2025, Chapter 2, 978-3-031-85255-8, 29, 10.1007/978-3-031-85256-5_2
    27. Giacomo Albi, Sara Bicego, Dante Kalise, Control of high-dimensional collective dynamics by deep neural feedback laws and kinetic modelling, 2025, 539, 00219991, 114229, 10.1016/j.jcp.2025.114229
  • Reader Comments
  • © 2019 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(4565) PDF downloads(628) Cited by(1)

Article outline

Figures and Tables

Figures(12)  /  Tables(5)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog