Repositorio Institucional
Repositorio Institucional
CONICET Digital
  • Inicio
  • EXPLORAR
    • AUTORES
    • DISCIPLINAS
    • COMUNIDADES
  • Estadísticas
  • Novedades
    • Noticias
    • Boletines
  • Ayuda
    • General
    • Datos de investigación
  • Acerca de
    • CONICET Digital
    • Equipo
    • Red Federal
  • Contacto
JavaScript is disabled for your browser. Some features of this site may not work without it.
  • INFORMACIÓN GENERAL
  • RESUMEN
  • ESTADISTICAS
 
Artículo

Epidemiological modeling of Trypanosoma cruzi: Low stercorarian transmission and failure of host adaptive immunity explain the frequency of mixed infections in humans

Tomasini, NicolásIcon ; Ragone, Paula GabrielaIcon ; Gourbière, Sébastien; Aparicio, Juan PabloIcon ; Diosque, PatricioIcon
Fecha de publicación: 05/2017
Editorial: Public Library of Science
Revista: Plos Computational Biology
ISSN: 1553-734X
Idioma: Inglés
Tipo de recurso: Artículo publicado
Clasificación temática:
Ciencias de la Computación

Resumen

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.
Palabras clave: Epidemiological Modeling , Trypanosoma Cruzi , Mixed Infections , Humans Transmission
Ver el registro completo
 
Archivos asociados
Thumbnail
 
Tamaño: 3.342Mb
Formato: PDF
.
Descargar
Licencia
info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Unported (CC BY-NC-SA 2.5)
Identificadores
URI: http://hdl.handle.net/11336/37496
DOI: http://dx.doi.org/10.1371/journal.pcbi.1005532
URL: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005532
Colecciones
Articulos(IPE)
Articulos de INST.DE PATOLOGIA EXPERIMENTAL
Citación
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
Compartir
Altmétricas
 

Enviar por e-mail
Separar cada destinatario (hasta 5) con punto y coma.
  • Facebook
  • X Conicet Digital
  • Instagram
  • YouTube
  • Sound Cloud
  • LinkedIn

Los contenidos del CONICET están licenciados bajo Creative Commons Reconocimiento 2.5 Argentina License

https://www.conicet.gov.ar/ - CONICET

Inicio

Explorar

  • Autores
  • Disciplinas
  • Comunidades

Estadísticas

Novedades

  • Noticias
  • Boletines

Ayuda

Acerca de

  • CONICET Digital
  • Equipo
  • Red Federal

Contacto

Godoy Cruz 2290 (C1425FQB) CABA – República Argentina – Tel: +5411 4899-5400 repositorio@conicet.gov.ar
TÉRMINOS Y CONDICIONES