American Institute of Mathematical Sciences

Advanced
  • Previous Article
    Global stability of an HIV-1 model with distributed intracellular delays and a combination therapy
  • MBE Home
  • This Issue
  • Next Article
    A mathematical study of a syntrophic relationship of a model of anaerobic digestion process
2010, 7(3): 657-673. doi: 10.3934/mbe.2010.7.657

Alternative transmission modes for Trypanosoma cruzi

Christopher M. Kribs-Zaleta 1,

1. 

Mathematics Department, University of Texas at Arlington, Box 19408, Arlington, TX 76019-0408, United States

Received  July 2009 Revised  April 2010 Published  June 2010

The parasite Trypanosoma cruzi, which causes Chagas' disease, is typically transmitted through a cycle in which vectors become infected through bloodmeals on infected hosts and then infect other hosts through defecation at the sites of subsequent feedings. The vectors native to the southeastern United States, however, are inefficient at transmitting T. cruzi in this way, which suggests that alternative transmission modes may be responsible for maintaining the established sylvatic infection cycle. Vertical and oral transmission of sylvatic hosts, as well as differential behavior of infected vectors, have been observed anecdotally. This study develops a model which accounts for these alternative modes of transmission, and applies it to transmission between raccoons and the vector Triatoma sanguisuga. Analysis of the system of nonlinear differential equations focuses on endemic prevalence levels and on the infection's basic reproductive number, whose form may account for how a combination of traditionally secondary infection routes can maintain the transmission cycle when the usual primary route becomes ineffective.
Keywords: Trypanosoma cruzi, oral infection, vector infection., Chagas, vertical infection.
Mathematics Subject Classification: Primary: 92D30, 92D40; Secondary: 92D2.
Citation: Christopher M. Kribs-Zaleta. Alternative transmission modes for Trypanosoma cruzi . Mathematical Biosciences & Engineering, 2010, 7 (3) : 657-673. doi: 10.3934/mbe.2010.7.657
[1]

Xiaotian Wu, Daozhou Gao, Zilong Song, Jianhong Wu. Modelling Trypanosoma cruzi-Trypanosoma rangeli co-infection and pathogenic effect on Chagas disease spread. Discrete and Continuous Dynamical Systems - B, 2022  doi: 10.3934/dcdsb.2022110
[2]

Chandrani Banerjee, Linda J. S. Allen, Jorge Salazar-Bravo. Models for an arenavirus infection in a rodent population: consequences of horizontal, vertical and sexual transmission. Mathematical Biosciences & Engineering, 2008, 5 (4) : 617-645. doi: 10.3934/mbe.2008.5.617
[3]

Yan-Xia Dang, Zhi-Peng Qiu, Xue-Zhi Li, Maia Martcheva. Global dynamics of a vector-host epidemic model with age of infection. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1159-1186. doi: 10.3934/mbe.2017060
[4]

Carlota Rebelo, Alessandro Margheri, Nicolas Bacaër. Persistence in some periodic epidemic models with infection age or constant periods of infection. Discrete and Continuous Dynamical Systems - B, 2014, 19 (4) : 1155-1170. doi: 10.3934/dcdsb.2014.19.1155
[5]

Jinliang Wang, Xiu Dong. Analysis of an HIV infection model incorporating latency age and infection age. Mathematical Biosciences & Engineering, 2018, 15 (3) : 569-594. doi: 10.3934/mbe.2018026
[6]

Jaouad Danane. Optimal control of viral infection model with saturated infection rate. Numerical Algebra, Control and Optimization, 2021, 11 (3) : 363-375. doi: 10.3934/naco.2020031
[7]

Miguel Atencia, Esther García-Garaluz, Gonzalo Joya. The ratio of hidden HIV infection in Cuba. Mathematical Biosciences & Engineering, 2013, 10 (4) : 959-977. doi: 10.3934/mbe.2013.10.959
[8]

Nicola Bellomo, Youshan Tao. Stabilization in a chemotaxis model for virus infection. Discrete and Continuous Dynamical Systems - S, 2020, 13 (2) : 105-117. doi: 10.3934/dcdss.2020006
[9]

Wendi Wang. Epidemic models with nonlinear infection forces. Mathematical Biosciences & Engineering, 2006, 3 (1) : 267-279. doi: 10.3934/mbe.2006.3.267
[10]

Don A. Jones, Hal L. Smith, Horst R. Thieme. Spread of viral infection of immobilized bacteria. Networks and Heterogeneous Media, 2013, 8 (1) : 327-342. doi: 10.3934/nhm.2013.8.327
[11]

Yanxia Dang, Zhipeng Qiu, Xuezhi Li. Competitive exclusion in an infection-age structured vector-host epidemic model. Mathematical Biosciences & Engineering, 2017, 14 (4) : 901-931. doi: 10.3934/mbe.2017048
[12]

Sukhitha W. Vidurupola, Linda J. S. Allen. Basic stochastic models for viral infection within a host. Mathematical Biosciences & Engineering, 2012, 9 (4) : 915-935. doi: 10.3934/mbe.2012.9.915
[13]

Samantha Erwin, Stanca M. Ciupe. Germinal center dynamics during acute and chronic infection. Mathematical Biosciences & Engineering, 2017, 14 (3) : 655-671. doi: 10.3934/mbe.2017037
[14]

Xuejuan Lu, Shaokai Wang, Shengqiang Liu, Jia Li. An SEI infection model incorporating media impact. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1317-1335. doi: 10.3934/mbe.2017068
[15]

Kokum R. De Silva, Shigetoshi Eda, Suzanne Lenhart. Modeling environmental transmission of MAP infection in dairy cows. Mathematical Biosciences & Engineering, 2017, 14 (4) : 1001-1017. doi: 10.3934/mbe.2017052
[16]

Yuming Chen, Junyuan Yang, Fengqin Zhang. The global stability of an SIRS model with infection age. Mathematical Biosciences & Engineering, 2014, 11 (3) : 449-469. doi: 10.3934/mbe.2014.11.449
[17]

Fred Brauer. Age-of-infection and the final size relation. Mathematical Biosciences & Engineering, 2008, 5 (4) : 681-690. doi: 10.3934/mbe.2008.5.681
[18]

Hui li, Manjun Ma. Global dynamics of a virus infection model with repulsive effect. Discrete and Continuous Dynamical Systems - B, 2019, 24 (9) : 4783-4797. doi: 10.3934/dcdsb.2019030
[19]

Fred Brauer, Zhisheng Shuai, P. van den Driessche. Dynamics of an age-of-infection cholera model. Mathematical Biosciences & Engineering, 2013, 10 (5&6) : 1335-1349. doi: 10.3934/mbe.2013.10.1335
[20]

Jinliang Wang, Ran Zhang, Toshikazu Kuniya. A note on dynamics of an age-of-infection cholera model. Mathematical Biosciences & Engineering, 2016, 13 (1) : 227-247. doi: 10.3934/mbe.2016.13.227

2018 Impact Factor: 1.313

Tools

Metrics

  • PDF downloads (146)
  • HTML views (0)
  • Cited by (20)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy