Evidence map›Paper›PMID 38820570›Full record

ArticlePLoS computational biology2024

Evaluating targeted COVID-19 vaccination strategies with agent-based modeling.

Thomas J Hladish, Alexander N Pillai, Carl A B Pearson, Kok Ben Toh, Andrea C Tamayo, Arlin Stoltzfus, Ira M Longini

Abstract read
In one paragraph

Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Thomas J HladishDepartment of Biology, University of Florida, Gainesville, Florida, United States of America.ORCID 0000-0003-1819-6235
Alexander N PillaiDepartment of Biology, University of Florida, Gainesville, Florida, United States of America.ORCID 0000-0002-3342-9685
Carl A B PearsonDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom.
Kok Ben TohDepartment of Biology, University of Florida, Gainesville, Florida, United States of America.
Andrea C TamayoDepartment of Biology, University of Florida, Gainesville, Florida, United States of America.
Arlin StoltzfusOffice of Data and Informatics, National Institute of Standards and Technology, Gaithersburg, Maryland, United States of America.
Ira M LonginiEmerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America.ORCID 0000-0002-2780-9382

Funding

Mathematical and Statistical Methods for the Control of Global Infectious Disease ThreatsR56AI148284 · NIAID · UNIVERSITY OF FLORIDA · PI HALLORAN, M ELIZABETH, LONGINI, IRA M · 2020 to 2020
$802k
NIAID NIH HHS R56 AI148284
6 · The paper itself

Abstract

We evaluate approaches to vaccine distribution using an agent-based model of human activity and COVID-19 transmission calibrated to detailed trends in cases, hospitalizations, deaths, seroprevalence, and vaccine breakthrough infections in Florida, USA. We compare the incremental effectiveness for four different distribution strategies at four different levels of vaccine supply, starting in late 2020 through early 2022. Our analysis indicates that the best strategy to reduce severe outcomes would be to actively target high disease-risk individuals. This was true in every scenario, although the advantage was greatest for the intermediate vaccine availability assumptions and relatively modest compared to a simple mass vaccination approach under high vaccine availability. Ring vaccination, while generally the most effective strategy for reducing infections, ultimately proved least effective at preventing deaths. We also consider using age group as a practical surrogate measure for actual disease-risk targeting; this approach also outperforms both simple mass distribution and ring vaccination. We find that quantitative effectiveness of a strategy depends on whether effectiveness is assessed after the alpha, delta, or omicron wave. However, these differences in absolute benefit for the strategies do not change the ranking of their performance at preventing severe outcomes across vaccine availability assumptions.

Indexed as

COVID-19COVID-19 VaccinesSARS-CoV-2Computational BiologyFloridaHumansMass VaccinationSystems AnalysisVaccinationCOVID-19 Vaccines

Identifiers

PMID38820570
PMCPMC11230632

What Socratic holds

Textmetadata
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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 Socratic graph.