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

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.