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dc.contributor.author
Tomasini, Nicolás
dc.contributor.author
Ragone, Paula Gabriela
dc.contributor.author
Gourbière, Sébastien
dc.contributor.author
Aparicio, Juan Pablo
dc.contributor.author
Diosque, Patricio
dc.date.available
2018-02-28T20:41:11Z
dc.date.issued
2017-05
dc.identifier.citation
Tomasini, Nicolás; Ragone, Paula Gabriela; Gourbière, Sébastien; Aparicio, Juan Pablo; Diosque, Patricio; Epidemiological modeling of Trypanosoma cruzi: Low stercorarian transmission and failure of host adaptive immunity explain the frequency of mixed infections in humans; Public Library of Science; Plos Computational Biology; 13; 5; 5-2017
dc.identifier.issn
1553-734X
dc.identifier.uri
http://hdl.handle.net/11336/37496
dc.description.abstract
People living in areas with active vector-borne transmission of Chagas disease have multiple contacts with its causative agent, Trypanosoma cruzi. Reinfections by T. cruzi are possible at least in animal models leading to lower or even hardly detectable parasitaemia. In humans, although reinfections are thought to have major public health implications by increasing the risk of chronic manifestations of the disease, there is little quantitative knowledge about their frequency and the timing of parasite re-inoculation in the course of the disease. Here, we implemented stochastic agent-based models i) to estimate the rate of re-inoculation in humans and ii) to assess how frequent are reinfections during the acute and chronic stages of the disease according to alternative hypotheses on the adaptive immune response following a primary infection. By using a hybrid genetic algorithm, the models were fitted to epidemiological data of Argentinean rural villages where mixed infections by different genotypes of T. cruzi reach 56% in humans. To explain this percentage, the best model predicted 0.032 (0.008–0.042) annual reinfections per individual with 98.4% of them occurring in the chronic phase. In addition, the parasite escapes to the adaptive immune response mounted after the primary infection in at least 20% of the events of re-inoculation. With these low annual rates, the risks of reinfection during the typically long chronic stage of the disease stand around 14% (4%-18%) and 60% (21%-70%) after 5 and 30 years, with most individuals being re-infected 1–3 times overall. These low rates are better explained by the weak efficiency of the stercorarian mode of transmission than a highly efficient adaptive immune response. Those estimates are of particular interest for vaccine development and for our understanding of the higher risk of chronic disease manifestations suffered by infected people living in endemic areas.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Public Library of Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Epidemiological Modeling
dc.subject
Trypanosoma Cruzi
dc.subject
Mixed Infections
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Humans Transmission
dc.subject.classification
Ciencias de la Computación
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Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
Epidemiological modeling of Trypanosoma cruzi: Low stercorarian transmission and failure of host adaptive immunity explain the frequency of mixed infections in humans
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-11-03T19:09:55Z
dc.journal.volume
13
dc.journal.number
5
dc.journal.pais
Estados Unidos
dc.journal.ciudad
San Francisco
dc.description.fil
Fil: Tomasini, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; Argentina
dc.description.fil
Fil: Ragone, Paula Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; Argentina
dc.description.fil
Fil: Gourbière, Sébastien. Université de Perpignan Via Domitia; Francia
dc.description.fil
Fil: Aparicio, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energia No Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energia No Convencional; Argentina
dc.description.fil
Fil: Diosque, Patricio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; Argentina
dc.journal.title
Plos Computational Biology
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1371/journal.pcbi.1005532
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005532
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