Evidence map›Paper›PMID 39899630›Full record

ArticlePLoS computational biology2025

Design of field trials for the evaluation of transmissible vaccines in animal populations.

Justin K Sheen, Lee Kennedy-Shaffer, Michael Z Levy, Charlotte Jessica E Metcalf

Erratum issuedAbstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Justin K SheenDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.ORCID 0000-0001-7842-1364
Lee Kennedy-ShafferDepartment of Mathematics and Statistics, Vassar College, Poughkeepsie, New York, United States of America.ORCID 0000-0001-7604-3638
Michael Z LevyDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Charlotte Jessica E MetcalfDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.

Funding

Princeton Catalysis Initiative
6 · The paper itself

Abstract

Vaccines which can transmit from vaccinated to unvaccinated animals may be especially useful for increasing immunity in hard to reach populations or in populations where achieving high coverage is logistically infeasible. However, gauging the public health utility for future use of such transmissible vaccines and assessing their risk-benefit tradeoff, given their potential for unintended evolution, hinges on accurate estimates of their indirect protective effect. Here, we establish the conditions under which a two-stage randomized field trial can characterize the protective effects of a transmissible vaccine relative to a traditional vaccine. We contrast the sample sizes required to adequately power these trials when the vaccine is weakly and strongly transmissible. We also identify how required sample sizes change based on the characteristics of host ecology such as the overdispersion of the contact structure of the population, as well as the efficacy of the vaccine and timing of vaccination. Our results indicate the range of scenarios where two-stage randomized field trial designs are feasible and appropriate to capture the protective effects of transmissible vaccines. Our estimates identify the protective benefit of using transmissible vaccines compared to traditional vaccines, and thus can be used to weigh against evolutionary risks.

Indexed as

VaccinationVaccinesAnimalsComputational BiologyResearch DesignVaccines

Identifiers

PMID39899630
PMCPMC11790233

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.