Evidence map›Paper›PMID 41815968›Full record

ArticleFrontiers in public health2026

Optimizing vaccination strategies under uncertainty to prevent epidemics.

Lewis Ntaimo, Mjumo Mzyece, David R Katerere

Abstract read
In one paragraph

Article in Frontiers in public health, 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
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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

3 authors.

Lewis NtaimoWm Michael Barnes Department of Industrial & Systems Engineering, Texas A&M University, College Station, TX, United States.
Mjumo MzyeceBusiness and Economics Department, Northwestern College, Orange City, IA, United States.
David R KaterereDepartment of Pharmaceutical Sciences, Tshwane University of Technology, Pretoria, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To be prepared for future infectious disease epidemics, this paper considers a data-driven approach for determining optimal vaccination strategies for multi-community settings with heterogeneous populations under uncertain social mixing, disease transmission, and vaccine efficacy. Specifically, we derive an integrated chance constraints stochastic programming (ICC-SP) disease spread model for optimal vaccination strategies to prevent epidemics by keeping the uncertain post-vaccination reproduction number below one at a specified level of risk. A vaccination strategy specifies the proportion of individuals in a given household-type and age-group to vaccinate and the intervention level needed to bound the expected excess of the reproduction number above one by an acceptable level of reliability (or risk). The ICC-SP model is data-driven and uses readily available data on census demographics, age-related disease susceptibility and infectivity, virus variants, and vaccine efficacy defined in three ways: vaccination in terms of effectiveness against infection, symptomatic cases, and hospitalization. This data-driven approach incorporates the decision-maker's level of risk to enable public health policy what-if analyses for future epidemics. A case study using the ICC-SP approach based on COVID-19 data was conducted, and the results of the study provide several insights. The study shows, for example, that to control disease outbreaks vaccination strategies must be combined with a specific intervention level. The proportion of the population to vaccinate to prevent epidemics depends on the criterion of vaccine efficacy used and decreases with increasing intervention level. The study also shows that optimal vaccination strategies prioritize the vaccination of specific households and age groups with high combined levels of relative susceptibility and infectivity.

Indexed as

COVID-19COVID-19 VaccinesEpidemicsVaccinationHumansSARS-CoV-2Stochastic ProcessesUncertaintyCOVID-19 VaccinesCOVID-19epidemicsinfectious diseasesintegrated chance constraintsoptimizationpublic healthstochastic programmingvaccination strategies

Identifiers

PMID41815968
PMCPMC12971655

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.