Modelling norovirus transmission and vaccination
Vaccine·
- DOI
- 10.1016/j.vaccine.2018.07.053
- PMID
- 30076105
- PMCID
- —
- OpenAlex
- W2886493390
- Study type
- Mathematical modelling
- Publisher
- Elsevier BV
- Article type
- journal-article
- Integrity
- current
Why this research matters now
Norovirus causes approximately one-fifth of acute gastroenteritis cases globally each year. With vaccines in clinical trials, model-based predictions can inform immunisation policy design and help optimize allocation across age groups depending on whether infection prevention or severe outcome prevention is prioritized.
Structured evidence summary
Research question
The study models norovirus transmission dynamics at the population level and evaluates the potential impact of various vaccination strategies.
Study design
Dynamic age-specific mathematical model of norovirus transmission and vaccination, incorporating a self-reporting Markov model to adjust for age- and time-varying statutory reporting. Model parameters estimated using sequential Monte Carlo particle filter and applied to German age-stratified notification data.
Population and setting
Population-level analysis using age-stratified case notification data from Germany.
Main findings
Routine immunisation could reduce norovirus incidence by up to 70.5% even with incomplete protection. Strategies targeting infants and toddlers are more efficient at preventing infection, while targeting older adults is preferable for preventing severe outcomes. Optimal strategy depends on the outcome prioritized and is sensitive to assumptions about vaccine mode of action.
Public-health relevance
Norovirus causes approximately one-fifth of acute gastroenteritis cases globally each year. With vaccines in clinical trials, model-based predictions can inform immunisation policy design and help optimize allocation across age groups depending on whether infection prevention or severe outcome prevention is prioritized.
Important limitations
The abstract notes that further work is required to determine vaccine efficacy, mode of action, and cost-effectiveness. This summary relies on abstract and metadata only; the original paper is required for decision-grade interpretation of model assumptions, parameter uncertainty, and scenario robustness.
GIDS interpretation
This article was identified through classifier links for gastroenteritis, vaccination topics, and Germany. It provides modeling evidence on potential vaccination impact but does not report primary surveillance data or outbreak investigation findings.
Related GIDS surveillance
Literature context does not validate, explain, or change a surveillance signal. Exact and contextual relationships are shown separately.
Evidence relationships
This article has 12 auditable classifier relationships to diseases, places, topics, and study design.