ArticlePLoS computational biology2014
Unveiling time in dose-response models to infer host susceptibility to pathogens.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 27 citations in OpenAlex.
- The ghost of infections past: Accounting for heterogeneity in individual infection history improves accuracy in epidemic forecasting.PLoS biology · 2025Article
- Detecting and quantifying heterogeneity in susceptibility using contact tracing data.PLoS computational biology · 2024Article
- Predictive approach of COVID-19 propagation via multiple-terms sigmoidal transition model.Infectious Disease Modelling · 2022Article
- Reply to: "Enhancement of Aedes aegypti susceptibility to dengue by Wolbachia is not supported".Nature communications · 2020Article
- Heterogeneous susceptibility to rotavirus infection and gastroenteritis in two birth cohort studies: Parameter estimation and epidemiological implications.PLoS computational biology · 2019Article
- Variation in Wolbachia effects on Aedes mosquitoes as a determinant of invasiveness and vectorial capacity.Nature communications · 2018Article
- Model-based inference from multiple dose, time course data reveals Wolbachia effects on infection profiles of type 1 dengue virus in Aedes aegypti.PLoS neglected tropical diseases · 2018Article
- Host Responses to Pathogen Priming in a Natural Songbird Host.EcoHealth · 2017Article
- Article
- On the correlation between variance in individual susceptibilities and infection prevalence in populations.Journal of mathematical biology · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.