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Record W4416004240 · doi:10.1016/j.vaccine.2025.127934

Implications of respiratory syncytial virus seasonality for the timing of passive immunisation scenarios in Latin America and the caribbean – a cross-sectional modelling study

2025· article· en· W4416004240 on OpenAlexaff
Paula Couto, Harry Campbell, You Li, Marc Rondy, Juliana da Silva Leite, Angel Rodríguez, Jairo Méndez‐Rico, Francisco Nogareda, Jorge Jara, Andrea Vicari, Harish Nair

Bibliographic record

VenueVaccine · 2025
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsCentre for Global Health Research
FundersPan American Health OrganizationCenters for Disease Control and PreventionWorld Health Organization
KeywordsLatin AmericansPneumovirinaePublic healthPneumovirusMononegaviralesSeasonalityVirusVaccinationCaribbean region

Abstract

fetched live from OpenAlex

BACKGROUND: The variation in respiratory syncytial virus (RSV) seasonality presents challenges for the timing of RSV prophylaxis. Prevention policies must consider seasonal dynamics to protect infants from severe RSV infections. We evaluated the timing and impact of passive immunisation on birth cohorts in Latin America and the Caribbean, accounting for RSV seasonality, duration of protection and uptake. METHODS: We characterised the 2010-2019 RSV seasonality by climate region using a moving averages-based method and surveillance data and identified newborns eligible for passive RSV immunisation under varied assumptions of protection duration and strategies. Lastly, we explored different intervention time windows and estimated RSV-associated acute lower respiratory infections (ALRI) averted among newborns. FINDINGS: In 2010-2019, 28 countries reported 317,951 RSV-positive respiratory samples (12.6 % RSV positivity). RSV epidemics followed a south-to-north progression, with onset ranging from March to November in subtropical climates, and year-round epidemics in the tropics. Seasonal immunisation benefited newborns born during January-September in temperate countries, while those born year-round in the tropics benefited from immunisation during at least one epidemic. Year-round vaccination covered newborns for at least one season; long-acting monoclonal antibodies (mAb) administered at five months to infants of immunised mothers extended protection through the remaining season. Year-round campaigns suited tropical climates. At 80 % coverage, 61.3 % (95 % CI 60.7-67) and 55 % (95 % CI 55.0-56.0) RSV-ALRI cases among 0- < 12 months were averted through mAb in exemplar temperate and tropical countries, respectively. In subtropical countries, combined RSV maternal vaccination and long-acting mAb averted 57.3 % (95 %CI:56.8-57.8) of RSV-ALRI. Efficiency per 100,000 doses administered varied minimally across strategies. CONCLUSIONS: RSV climatic variations underscored the importance of surveillance in tailoring the timing and extent of annual campaigns. RSV seasonality informs the selection of interventions. Public health initiatives can enhance prevention for newborns by defining optimal windows for immunising at-risk-infants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.087
GPT teacher head0.407
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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