1. Computational and Systems Biology
  2. Epidemiology and Global Health
Download icon

Reducing RSV hospitalisation in a lower-income country by vaccinating mothers-to-be and their households

  1. Samuel PC Brand  Is a corresponding author
  2. Patrick Munywoki
  3. David Walumbe
  4. Matthew J Keeling
  5. David James Nokes
  1. University of Warwick, United Kingdom
  2. KEMRI-Wellcome Trust Research Centre, Kenya
Research Article
  • Cited 1
  • Views 396
  • Annotations
Cite this article as: eLife 2020;9:e47003 doi: 10.7554/eLife.47003

Abstract

Respiratory syncytial virus is the leading cause of lower respiratory tract infection among infants. RSV is a priority for vaccine development. In this study, we investigate the potential effectiveness of a two-vaccine strategy aimed at mothers-to-be, thereby boosting maternally acquired antibodies of infants, and their household cohabitants, further cocooning infants against infection. We use a dynamic RSV transmission model which captures transmission both within households and communities, adapted to the changing demographics and RSV seasonality of a low-income country. Model parameters were inferred from past RSV hospitalisations, and forecasts made over a 10-year horizon. We find that a 50% reduction in RSV hospitalisations is possible if the maternal vaccine effectiveness can achieve 75 days of additional protection for newborns combined with a 75% coverage of their birth household co-inhabitants (∼7.5% population coverage).

Article and author information

Author details

  1. Samuel PC Brand

    The Zeeman Institute (SBIDER), School of Life Sciences, University of Warwick, Coventry, United Kingdom
    For correspondence
    S.Brand@warwick.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0645-5367
  2. Patrick Munywoki

    Virus Epidemiology and Control Group, KEMRI-Wellcome Trust Research Centre, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9419-7155
  3. David Walumbe

    Virus Epidemiology and Control Group, KEMRI-Wellcome Trust Research Centre, Kilifi, Kenya
    Competing interests
    The authors declare that no competing interests exist.
  4. Matthew J Keeling

    The Zeeman Institute (SBIDER), School of Life Sciences, University of Warwick, Coventry, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. David James Nokes

    The Zeeman Institute (SBIDER), School of Life Sciences, University of Warwick, Coventry, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5426-1984

Funding

The Wellcome Trust (102975)

  • David James Nokes

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Anna Akhmanova, Utrecht University, Netherlands

Publication history

  1. Received: March 19, 2019
  2. Accepted: March 26, 2020
  3. Accepted Manuscript published: March 27, 2020 (version 1)

Copyright

© 2020, Brand et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 396
    Page views
  • 53
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Michael E Rule et al.
    Short Report Updated

    Over days and weeks, neural activity representing an animal’s position and movement in sensorimotor cortex has been found to continually reconfigure or ‘drift’ during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories, which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. Analyzing long-term calcium imaging recordings from posterior parietal cortex in mice (Mus musculus), we show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioral variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

    1. Computational and Systems Biology
    2. Neuroscience
    Rafal Bogacz
    Research Article Updated

    This paper describes a framework for modelling dopamine function in the mammalian brain. It proposes that both learning and action planning involve processes minimizing prediction errors encoded by dopaminergic neurons. In this framework, dopaminergic neurons projecting to different parts of the striatum encode errors in predictions made by the corresponding systems within the basal ganglia. The dopaminergic neurons encode differences between rewards and expectations in the goal-directed system, and differences between the chosen and habitual actions in the habit system. These prediction errors trigger learning about rewards and habit formation, respectively. Additionally, dopaminergic neurons in the goal-directed system play a key role in action planning: They compute the difference between a desired reward and the reward expected from the current motor plan, and they facilitate action planning until this difference diminishes. Presented models account for dopaminergic responses during movements, effects of dopamine depletion on behaviour, and make several experimental predictions.