Decentralized versus Centralized COVID‑19 Interventions in Mozambique: A Metapopulation Modeling Study
DOI:
https://doi.org/10.63595/vetor.v36i1.20939Keywords:
COVID-19, Mozambique, Decentralized interventions, Metapopulation modeling, Nonpharmaceutical interventionsAbstract
Country-wide lockdowns often face resistance when perceived as overly broad or misaligned with local realities. Designing effective, context-sensitive interventions requires understanding whether centralized national or decentralized provincial measures are more appropriate. This study extends previous analyses by developing a stochastic metapopulation model to simulate COVID-19 transmission across Mozambique’s 11 provinces during the first epidemic wave from March 22, 2020 to March 7, 2021. Human mobility was modeled via a radiation-based transition matrix, and the model was calibrated using effective population estimates and reported active cases. Three core intervention scenarios were evaluated including mobility without interventions, mobility with centralized national-triggered interventions, and mobility with decentralized province-specific triggers. Additional simulations assessed the robustness of interventions under varying physical distancing effectiveness ε, national thresholds νG, and provincial thresholds νL. Results indicate that decentralized interventions outperformed centralized approaches, delaying provincial epidemic peaks by 29 to 82 days, with a national average of 37 days, and reducing cumulative infections by 0.74-6.86% across provinces, with a national reduction greater than 2.49%. Sensitivity analyses show that higher physical distancing effectiveness and stricter local thresholds further delay peaks, particularly in less-connected provinces. Global sensitivity analysis highlights transmission rate, interprovincial connectivity, and intervention efficacy as the most influential factors. These findings suggest that province-specific strategies provide a superior balance between epidemic control and socioeconomic resilience in resource-limited settings and demonstrate the importance of spatially explicit mobility-aware models to guide adaptive public health policies and future pandemic preparedness under heterogeneous regional conditions.
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