ArticlePloS one2026
How do social network models compare to all-to-all models for forecasting tuberculosis epidemics? A mathematical modeling study.
Article in PloS one, 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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Abstract
backgroundMathematical 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.
methodsWe 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.
resultsUnder 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.
conclusionsAccounting for social networks can attenuate projected impacts for sub‑optimal TB interventions. Forecasts and target‑setting should include sensitivity to social network structure.
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