Unveiling time in dose-response models to infer host susceptibility to pathogens

Delphine Pessoa, Caetano Souto-Maior, Erida Gjini, Joao S. Lopes, Bruno Ceña, Cláudia T. Codeço, M. Gabriela M. Gomes

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

The biological effects of interventions to control infectious diseases typically depend on the intensity of pathogen challenge. As much as the levels of natural pathogen circulation vary over time and geographical location, the development of invariant efficacy measures is of major importance, even if only indirectly inferrable. Here a method is introduced to assess host susceptibility to pathogens, and applied to a detailed dataset generated by challenging groups of insect hosts (Drosophila melanogaster) with a range of pathogen (Drosophila C Virus) doses and recording survival over time. The experiment was replicated for flies carrying the Wolbachia symbiont, which is known to reduce host susceptibility to viral infections. The entire dataset is fitted by a novel quantitative framework that significantly extends classical methods for microbial risk assessment and provides accurate distributions of symbiont-induced protection. More generally, our data-driven modeling procedure provides novel insights for study design and analyses to assess interventions.

Original languageEnglish
Article numbere1003773
Pages (from-to)1-9
Number of pages9
JournalPLoS Computational Biology
Volume10
Issue number8
DOIs
Publication statusPublished - 14 Aug 2014

Keywords

  • dose-response models
  • mathematical model
  • host susceptibility
  • infection sensitivity
  • virus infection
  • virus load
  • epidemic
  • host pathogen interaction

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  • Cite this

    Pessoa, D., Souto-Maior, C., Gjini, E., Lopes, J. S., Ceña, B., Codeço, C. T., & Gomes, M. G. M. (2014). Unveiling time in dose-response models to infer host susceptibility to pathogens. PLoS Computational Biology, 10(8), 1-9. [e1003773]. https://doi.org/10.1371/journal.pcbi.1003773