Evidence map›Paper›PMID 41530292›Full record

ArticleScientific reports2026

A generalized SEIRW-VN framework for modeling infectious disease dynamics.

Abdoulaye Sow, Cherif Diallo, Hocine Cherifi

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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.

Abdoulaye SowDepartment of Computer Science, Algebra Laboratory for Cryptography, Codes and Applications, Gaston Berger University, Saint-Louis, Senegal. sow.abdoulaye6@ugb.edu.sn.
Cherif DialloDepartment of Computer Science, Algebra Laboratory for Cryptography, Codes and Applications, Gaston Berger University, Saint-Louis, Senegal.
Hocine CherifiDepartment of Computer Science, ICB, UMR 6303 CNRS, Université Bourgogne Europe France, Dijon, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding how infectious diseases spread requires models that integrate both human contact structure and environmental factors. We propose SEIRW-VN, a generalized epidemic framework that combines network heterogeneity, indirect transmission via environmental reservoirs, and vaccination. Using COVID-19 data from several European countries, the model outperforms classical homogeneous approaches, capturing more realistic epidemic peaks and timing. Our simulations reveal that highly connected individuals disproportionately sustain outbreaks, while environmental transmission can account for up to one quarter of infections, prolonging epidemic duration. Intervention analysis shows that non-pharmaceutical measures delay peaks and vaccination reduces long-term incidence, but only their combination yields strong synergistic effects, lowering both peak size and overall burden. These findings highlight the importance of integrating contact structure, environmental persistence, and vaccination into epidemic models to design robust and effective public health strategies.

Indexed as

Communicable DiseasesCOVID-19Epidemiological ModelsComputer SimulationDisease OutbreaksEuropeHumansSARS-CoV-2Vaccination

Identifiers

PMID41530292
PMCPMC12876884

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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.