Evidence map›Paper›PMID 35610105›Full record

ArticleVaccine2022

Risk-based cost-benefit analysis of alternative vaccines against COVID-19 in Brazil: Coronavac vs. Astrazeneca vs. Pfizer.

Paulo Gabriel Siqueira, Heitor Oliveira Duarte, Márcio das Chagas Moura

Abstract read
In one paragraph

Article in Vaccine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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

3 authors.

Paulo Gabriel SiqueiraCenter for Risk Analysis, Reliability Engineering and Environmental Modeling (CEERMA), Universidade Federal de Pernambuco, Recife, PE, Brazil; Industrial Engineering Department, Universidade Federal de Pernambuco, Recife, PE, Brazil. Electronic address: paulo.gabriel@ufpe.br.
Heitor Oliveira DuarteMechanical Engineering Department, Universidade Federal de Pernambuco, Recife, PE, Brazil.
Márcio das Chagas MouraCenter for Risk Analysis, Reliability Engineering and Environmental Modeling (CEERMA), Universidade Federal de Pernambuco, Recife, PE, Brazil; Industrial Engineering Department, Universidade Federal de Pernambuco, Recife, PE, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a probabilistic model to quantify the cost-benefit of mass Vaccination Scenarios (VSs) against COVID-19. Through this approach, we conduct a six-month simulation, from August 31st, 2021 to March 3rd, 2022, of nine VSs, i.e., the three primary vaccine brands in Brazil (CoronaVac, AstraZeneca and Pfizer), each with three different vaccination rates (2nd doses per week). Since each vaccine has different individual-level effectiveness, we measure the population-level benefit as the probability of reaching herd immunity (HI). We quantify and categorize the cost-benefit of VSs through risk graphs that show: (i) monetary cost vs. probability of reaching HI; and (ii) number of new deaths vs. probability of reaching HI. Results show that AstraZeneca has the best cost-benefit when prioritizing acquisition costs, while Pfizer is the most cost-beneficial when prioritizing the number of deaths. This work provides helpful information that can aid public health authorities in Brazil to better plan VSs. Furthermore, our approach is not restricted to Brazil, the COVID-19 pandemic, or the mentioned vaccine brands. Indeed, the method is flexible so that this study can be a valuable reference for future cost-benefit analyses in other countries and pandemics, especially in the early stages of vaccination, when data is scarce and uncertainty is high.

Indexed as

COVID-19VaccinesBrazilCost-Benefit AnalysisCOVID-19 VaccinesHumansPandemicsVaccinationCOVID-19 VaccinesVaccinesCost-benefit analysisCOVID-19Probabilistic epidemiological modelRisk assessmentVaccines

Identifiers

PMID35610105
PMCPMC9117164

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.