Clinical impact of childhood and adolescence dengue vaccination in Indonesia: A dynamic transmission modeling study
Epidemics·
- DOI
- 10.1016/j.epidem.2026.100932
- PMID
- 42424822
- PMCID
- —
- OpenAlex
- W7166692193
- Study type
- Mathematical modelling
- Publisher
- Elsevier BV
- Article type
- journal-article
- Integrity
- current
Why this research matters now
The results suggest that prioritizing early childhood immunization could meaningfully decrease national dengue incidence if a broadly protective vaccine becomes available.
Structured evidence summary
Research question
The investigation evaluates how varying vaccine characteristics and age-targeted deployment strategies might influence symptomatic dengue case reduction in Indonesia.
Study design
Researchers utilized a dynamic transmission modeling framework to simulate disease spread under numerous hypothetical conditions.
Population and setting
The simulation focused on pediatric and adolescent cohorts ranging from one to eighteen years of age within Indonesia.
Main findings
Targeting infants and toddlers produced the highest projected reductions in symptomatic infections, whereas older adolescents showed comparatively lower prevention rates. Over a twenty-year period, modeled interventions were estimated to prevent between four point nine and eight point six million cases depending on assumed vaccine performance.
Public-health relevance
The results suggest that prioritizing early childhood immunization could meaningfully decrease national dengue incidence if a broadly protective vaccine becomes available.
Important limitations
The conclusions derive entirely from computational simulations rather than observed clinical or field data. Outcomes are contingent upon predefined assumptions about vaccine durability and effectiveness, excluding real-world operational factors.
GIDS interpretation
This computational work offers theoretical insight into age-stratified immunization planning for dengue-endemic regions. It establishes a baseline for understanding potential epidemiological shifts without linking to ongoing monitoring networks or active detection efforts.
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 9 auditable classifier relationships to diseases, places, topics, and study design.