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A Model for Chagas Disease with Oral and Congenital Transmission

Overview of attention for article published in PLOS ONE, June 2013
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Title
A Model for Chagas Disease with Oral and Congenital Transmission
Published in
PLOS ONE, June 2013
DOI 10.1371/journal.pone.0067267
Pubmed ID
Authors

Daniel J. Coffield, Anna Maria Spagnuolo, Meir Shillor, Ensela Mema, Bruce Pell, Amanda Pruzinsky, Alexandra Zetye

Abstract

This work presents a new mathematical model for the domestic transmission of Chagas disease, a parasitic disease affecting humans and other mammals throughout Central and South America. The model takes into account congenital transmission in both humans and domestic mammals as well as oral transmission in domestic mammals. The model has time-dependent coefficients to account for seasonality and consists of four nonlinear differential equations, one of which has a delay, for the populations of vectors, infected vectors, infected humans, and infected mammals in the domestic setting. Computer simulations show that congenital transmission has a modest effect on infection while oral transmission in domestic mammals substantially contributes to the spread of the disease. In particular, oral transmission provides an alternative to vector biting as an infection route for the domestic mammals, who are key to the infection cycle. This may lead to high infection rates in domestic mammals even when the vectors have a low preference for biting them, and ultimately results in high infection levels in humans.

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Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 1%
Colombia 1 1%
Germany 1 1%
Argentina 1 1%
Unknown 72 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 25%
Student > Ph. D. Student 10 13%
Student > Bachelor 10 13%
Student > Doctoral Student 8 11%
Student > Postgraduate 6 8%
Other 15 20%
Unknown 8 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 33%
Medicine and Dentistry 8 11%
Mathematics 8 11%
Immunology and Microbiology 6 8%
Biochemistry, Genetics and Molecular Biology 3 4%
Other 14 18%
Unknown 12 16%