Evidence map›Paper›PMID 41541004›Full record

ArticleInfectious Disease Modelling2026

Impact of the first booster vaccine against SARS-CoV-2 in Chile.

Emilio Molina, Diego Olguín, Antoine Brault, Paula Uribe, Pedro Gajardo, Mauricio Canals, Héctor Ramírez

Abstract read
In one paragraph

Article in Infectious Disease Modelling, 2026. 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

7 authors.

Emilio MolinaINSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Villeurbanne, France.
Diego OlguínDepartamento de Ingeniería Matemática, Universidad de Chile, Santiago, Chile.
Antoine BraultCentro de Modelamiento Matemático, Universidad de Chile and CNRS IRL2807, Santiago, Chile.
Paula UribeCentro de Modelamiento Matemático, Universidad de Chile and CNRS IRL2807, Santiago, Chile.
Pedro GajardoDepartamento de Matemática, Universidad Técnica Federico Santa María, Valparaíso, Chile.
Mauricio CanalsPrograma de Salud Ambiental, Escuela de Salud Pública, Facultad de Medicina, Universidad de Chile & Departamento de Medicina, Santiago, Chile.
Héctor RamírezDepartamento de Ingeniería Matemática, Universidad de Chile, Santiago, Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present paper proposes a novel methodology for evaluating the impact of a vaccination plan against a transmissible disease. The methodology has two distinct stages. The initial stage comprises a compartmental model that describes the transmission of the disease within the population. This model is composed of fundamental parts representing the vaccination status and is used in this initial stage to estimate the number of cases and deaths averted by the vaccination plan. The case dynamics generated by the compartmental model serve as the input for a data-driven model in the second part of the methodology. The second model is statistical in nature and provides additional information regarding the number of hospitalizations and ICU admissions averted. This methodology is applied to assess the impact of booster SARS-CoV-2 vaccines in Chile, resulting in an estimation of 84 % (95 % confidence interval (CI): 74 %-92 %) of cases, 73 % (95 % CI: 57 %, 80 %) of hospital admissions, 77 % (95 % CI: 61 %, 84 %) of intensive care unit admissions, and 78 % (95 % CI: 62 %-85 %) of deaths averted between August 16 and December 31, 2021.

Indexed as

Averted cases and outcomesBooster vaccinationCompartmental modelData-driven modelSARS-CoV-2Vaccination effectiveness

Identifiers

PMID41541004
PMCPMC12800598

What OpenQuestion holds

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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.