Evidence map›Paper›PMID 36945423›Full record

ArticlemedRxiv : the preprint server for health sciences2023

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

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

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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

5 · Who and what money

Authors and funding

7 authors.

Thomas J HladishDepartment of Biology, University of Florida, Gainesville, Florida, USA.
Alexander N PillaiDepartment of Biology, University of Florida, Gainesville, Florida, USA.
Carl A B PearsonDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
Kok Ben TohDepartment of Biology, University of Florida, Gainesville, Florida, USA.
Andrea TamayoDepartment of Biology, University of Florida, Gainesville, Florida, USA.
Arlin StoltzfusOffice of Data and Informatics, National Institute of Standards and Technology, Gaithersburg, MD, USA.
Ira M LonginiEmerging Pathogens Institute, University of Florida, Gainesville, Florida, USA.

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 availability, reflecting different income settings' historical COVID-19 vaccine distribution. Our analysis indicates that the best strategy to reduce severe outcomes is to actively target high disease-risk individuals. This was true in every scenario, although the advantage was greatest for the middle-income-country availability assumptions, and relatively modest compared to a simple mass vaccination approach for rapid, high levels of 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 still outperforms both simple mass distribution and ring vaccination. We also find that the magnitude of strategy effectiveness depends on when assessment occurs (

Indexed as

Agent-based ModelCOVID-19FloridaLMICRing VaccinationVaccination

Identifiers

PMID36945423
PMCPMC10029084

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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.