How do social network models compare to all-to-all models for forecasting tuberculosis epidemics? A mathematical modeling study

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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.
Funding Information
  • Section for Global Health, Department of Population Health, NYU Grossman School of Medicine
  • Division of Comparative Effectiveness & Decision Science, Department of Population Health, NYU Grossman School of Medicine
  • Division of Comparative Effectiveness & Decision Science, Department of Population Health, NYU Grossman School of Medicine
  • Division of Comparative Effectiveness & Decision Science, Department of Population Health, NYU Grossman School of Medicine

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