Evidence map›Paper›PMID 41497509›Full record

ArticleEClinicalMedicine2025

Age-specific chikungunya outbreak response immunisation strategies in Brazil: a modelling study.

Hyolim Kang, Ahyoung Lim, Andrew Clark, Felipe J Colón González, Hannah Eleanor Clapham, Jean-Paul Carrera, Jong-Hoon Kim, Megan Auzenbergs, Preethi Lakshminarayanan, Sandra López-Vergès and 9 more

Abstract read
In one paragraph

Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Hyolim KangDepartment of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Ahyoung LimDepartment of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Andrew ClarkDepartment of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK.
Felipe J Colón GonzálezData for Science and Health, Wellcome Trust, London, UK.
Hannah Eleanor ClaphamSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.
Jean-Paul CarreraCarson Centre for Health and Ecosystems Research, Darien, Panama.
Jong-Hoon KimInternational Vaccine Institute, Seoul, South Korea.
Megan AuzenbergsImperial College London, London, UK.
Preethi LakshminarayananDepartment of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Sandra López-VergèsGorgas Memorial Institute for Health Studies, Panama City, Panama.
So Yoon SimDepartment of Immunization, Vaccines, and Biologicals, World Health Organization, Geneva, Switzerland.
Su Myat HanSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
Thiago Cerqueira-SilvaDepartment of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Timothy EndyCoalition for Epidemic Preparedness Innovations, Washington, DC, USA.
Zulma M CucunubáInstitute of Public Health, Pontificia Universidad Javeriana, Colombia.
W John EdmundsDepartment of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Sushant SahastrabuddheInstitute of Tropical Medicine, Nagasaki University, Nagasaki, Japan.
Oliver J BradyDepartment of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.
Kaja AbbasDepartment of Infectious Disease Epidemiology and Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, UK.

Funding

World Health Organization 001
6 · The paper itself

Abstract

Background: Two chikungunya vaccines, Ixchiq and Vimkunya are licensed. In April 2025, Brazil is the first endemic country to license Ixchiq, but optimal age groups for vaccination remain unclear. Our aim is to model the public health impact of age-specific chikungunya outbreak response immunisation strategies in Brazil and infer broader implications for vaccine use case scenarios in outbreak prone regions. Methods: We developed an age-structured transmission dynamic model calibrated with state-level Brazilian surveillance data for 2022 and long-term average annual force of infections. We simulated outbreak response immunisation strategies targeting ages 1-11, 12-17, 18-59, and ≥60 years for Ixchiq and Vimkunya across 11 out of 27 states in Brazil. We assessed vaccine impact by symptomatic cases, deaths, and disability-adjusted life years (DALYs) averted and number needed to vaccinate (NNV) based on vaccine protection against disease only and against both disease and infection. Findings: Ixchiq and Vimkunya showed similar vaccine impact. Across strategies, vaccinating children 1-11 years yielded the lowest NNV for both vaccines, whereas vaccinating adults 18-59 years achieved the greatest absolute reduction in symptomatic cases, averting 62.5% (95% Uncertainty Intervals [UI]: 54.2-84.1) of total symptomatic cases with Vimkunya and 66.2% (58.2-86.0) with Ixchiq, under disease and infection blocking mechanism. Vaccinating adults 18-59 years with Ixchiq or Vimkunya yielded similar efficiency, with NNVs to avert a DALY of 339 (39-3412) and 361 (40-3777) respectively, under disease and infection-blocking mechanism. Interpretation: Under current licensure, vaccinating adolescents aged 12-17 years first, followed by 18-59 years are efficient strategies, with similar NNVs for both Ixchiq and Vimkunya. If eligibility expands to younger populations, vaccinating 1-11-year age group will have relatively higher efficiency. Funding: International Vaccine Institute and Japan Agency for Medical Research and Development.

Indexed as

Age-specific vaccinationChikungunyaOutbreak response immunisationVaccine impact modelling

Identifiers

PMID41497509
PMCPMC12766480

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Registered trials

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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.