ArticleScientific reports2025
Developing an intuitive decision support system for equitable vaccine distribution during pandemics.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Public Health Responsible AI Capability (PH-RAIC) Framework: A Conceptual Model for Integrating AI into Public Health Agencies.Healthcare (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective organization of the vaccine supply chain is vital to achieve high vaccination rates in pandemics. This paper presents a novel approach for developing a decision support system (DSS) to support health officials and policymakers who must make timely and impactful decisions with limited information for distributing vaccines. We combine a stakeholder-informed systems approach for problem definition with a user-centered design approach for DSS development. The methodology has been tested during the COVID-19 pandemic. We used system modeling to capture stakeholders' knowledge, experience, and learnings from the H1N1 pandemic, leading us to focus on the central vaccine allocation problem (CVAP), which involves determining the number of vaccines allocated to each municipality in an equitable way. We designed a dashboard that embedded a mathematical model as a DSS for in-country CVAP during a pandemic. A Lightning Decision Jam workshop was conducted to define the DSS's characteristics, with the visualization of scenarios and decision-making transparency being key features during the development process. We validated the DSS with policymakers and built it for the Norwegian context, with flexibility for adaptation to other regions, particularly those still grappling with high vaccine supply deficits. Our approach offers a novel and practical way to develop DSSs to support policymakers making critical decisions during pandemics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.