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Unveiling Time in Dose-Response Models to Infer Host Susceptibility to Pathogens

Overview of attention for article published in PLoS Computational Biology, August 2014
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Title
Unveiling Time in Dose-Response Models to Infer Host Susceptibility to Pathogens
Published in
PLoS Computational Biology, August 2014
DOI 10.1371/journal.pcbi.1003773
Pubmed ID
Authors

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

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.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Portugal 1 2%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 26%
Student > Ph. D. Student 8 17%
Student > Doctoral Student 5 11%
Professor > Associate Professor 5 11%
Student > Bachelor 3 7%
Other 7 15%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 35%
Medicine and Dentistry 6 13%
Biochemistry, Genetics and Molecular Biology 5 11%
Immunology and Microbiology 4 9%
Environmental Science 3 7%
Other 5 11%
Unknown 7 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 August 2014.
All research outputs
#20,656,820
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#8,208
of 8,960 outputs
Outputs of similar age
#178,245
of 243,822 outputs
Outputs of similar age from PLoS Computational Biology
#139
of 159 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 4th percentile – i.e., 4% of its peers scored the same or lower than it.
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We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.