ArticlePLoS computational biology2013
The timing and targeting of treatment in influenza pandemics influences the emergence of resistance in structured populations.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Spatial structure facilitates evolutionary rescue by drug resistance.PLoS computational biology · 2025Article
- Localization, epidemic transitions, and unpredictability of multistrain epidemics with an underlying genotype network.PLoS computational biology · 2021Article
- Seasonality of respiratory viruses causing hospitalizations for acute respiratory infections in children in Nha Trang, Vietnam.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2018Article
- Timely Antiviral Administration During an Influenza Pandemic: Key Components.American journal of public health · 2018Article
- Modelling the emergence of influenza drug resistance: The roles of surface proteins, the immune response and antiviral mechanisms.PloS one · 2017Article
- Eradicating infectious disease using weakly transmissible vaccines.Proceedings. Biological sciences · 2016Article
- Article
- Complex dynamics of synergistic coinfections on realistically clustered networks.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Timing of antimicrobial use influences the evolution of antimicrobial resistance during disease epidemics.Evolution, medicine, and public health · 2014Article
- Epidemic cycles driven by host behaviour.Journal of the Royal Society, Interface · 2014Article
- Optimizing treatment regimes to hinder antiviral resistance in influenza across time scales.PloS one · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antiviral resistance in influenza is rampant and has the possibility of causing major morbidity and mortality. Previous models have identified treatment regimes to minimize total infections and keep resistance low. However, the bulk of these studies have ignored stochasticity and heterogeneous contact structures. Here we develop a network model of influenza transmission with treatment and resistance, and present both standard mean-field approximations as well as simulated dynamics. We find differences in the final epidemic sizes for identical transmission parameters (bistability) leading to different optimal treatment timing depending on the number initially infected. We also find, contrary to previous results, that treatment targeted by number of contacts per individual (node degree) gives rise to more resistance at lower levels of treatment than non-targeted treatment. Finally we highlight important differences between the two methods of analysis (mean-field versus stochastic simulations), and show where traditional mean-field approximations fail. Our results have important implications not only for the timing and distribution of influenza chemotherapy, but also for mathematical epidemiological modeling in general. Antiviral resistance in influenza may carry large consequences for pandemic mitigation efforts, and models ignoring contact heterogeneity and stochasticity may provide misleading policy recommendations.
Indexed as
Identifiers
What OpenQuestion holds
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.