Document Type

Article

Publication Date

4-1-2026

Publisher

Public Library of Science (PLoS)

Source Publication

PLoS One

Source ISSN

1932-6203

Abstract

Background. Mathematical models guide tuberculosis (TB) target-setting, yet most assume homogeneous “all-to-all” mixing. We compared projected intervention impacts between an all-to-all compartmental model and a Barabási–Albert (BA) scale‑free social network model under otherwise identical disease assumptions.

Methods. We calibrated transmission parameters so both models produced similar baseline trends, then introduced vaccination (coverage 30–70%; efficacy 80–95%) and treatment (20–50% increases in recovery) after a 400‑day burn‑in. Outcomes were assessed 300 days post‑intervention.

Results. Under 60% coverage, increasing vaccine efficacy from 80% to 95% yielded smaller projected reductions in active TB with the network model than with all‑to‑all mixing. Treatment improvements showed the same pattern: lower reductions under the network than the all‑to‑all model at modest efficacy, converging at high efficacy/coverage. Findings were robust across baseline prevalence scenarios.

Conclusions. Accounting for social networks can attenuate projected impacts for sub‑optimal TB interventions. Forecasts and target‑setting should include sensitivity to social network structure.

Comments

Published version. PLoS One, Vol. 21, No. 4 (2026): e0343421. DOI. © 2026 The Author(s) Public Library of Science (PLoS). Used with permission.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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