• A stratified and fairness-aware TPPI framework benchmarks pandemic performance across 166 countries. • A hybrid BWM entropy weighting scheme produces a transparent and reproducible composite index. • Random Forest analysis and effect sizes identify actionable and context-specific performance drivers. • The framework provides policy guidance and gap analysis tailored to each development stratum. • Robustness checks confirm highly stable country rankings across alternative specifications (ρ ≈ 0.975). To develop an equitable, process-oriented framework for comparing national pandemic performance that avoids conflating epidemiological outcomes with policy quality, and to identify actionable improvement levers within development-stratified country groups. We propose the Stratified Tripartite Pandemic Assessment Framework (STPAF) and construct the Tripartite Pandemic Performance Index (TPPI) across three pillars of Government, People, and Interaction, weighted using a hybrid Best-Worst Method and Shannon entropy. Using hierarchical clustering, 166 countries were stratified into Advanced, Emerging, and Developing groups. We then applied correlational analysis and machine-learning-based determinant assessment to prioritize policy-relevant drivers and tested robustness across alternative specifications. TPPI scores showed a clear developmental gradient (Advanced: 58.6; Emerging: 44.6; Developing: 29.1; p < 0.001), while high-performing outliers indicated that governance and process agility can partially offset structural constraints. Governance effectiveness (r = 0.640, p < 0.001), readiness capacity (r = 0.609, p < 0.001), and gender equity (r = -0.602, p < 0.001) were dominant determinants. Stratified findings highlighted governance integrity, digital infrastructure, and International Health Regulations compliance as key levers. Robustness checks showed high stability (mean ρ = 0.975). STPAF provides a rigorous, development-aware benchmark for pandemic preparedness and response quality. By separating controllable processes from structural conditions, it supports feasible gap identification and context-specific interventions for more resilient and inclusive health systems. This paper sets out to answer a simple question with big consequences: what practical steps help countries handle a pandemic better? Using openly available global data, the paper has built a fair comparison tool that groups countries by similar development levels and turns dozens of indicators like clean water access, lab capacity, data systems, and governance, into an easy-to-read score and policy “playbook.” Instead of pointing fingers, the results show where progress is most achievable for each group. For example, strengthening disease surveillance and speeding lab turnaround often deliver outsized gains, while better water and sanitation reduce risks that hit families hardest. The findings do not claim cause and effect, but they spotlight doable actions that governments can take now, track over time, and explain to the public. The goal is to help leaders translate data into fair, transparent, and life-improving choices.
Salehi et al. (2026) studied this question.