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Understanding mobility dynamics and their influence on epidemic spread is crucial for effective management strategies, a concept that, despite its importance, has received limited integration in traditional epidemiological models. This study introduces a novel decision support tool that integrates an activity-based model for mobility dynamics with a multi-group compartmental SIRD (Susceptible–Infected–Recovered–Dead) model for infection transmission. The tool consists of a multi-objective optimization framework that evaluates the trade-offs between public health and economic factors in socioeconomic segments. Our findings show that policies targeted at specific demographic groups significantly improve the efficacy of interventions. The framework provides policymakers with a collection of optimized and customized strategies through a user-friendly dashboard, using a multi-objective modeling approach. This visualization compares potential outcomes along the Pareto frontier, helping to select balanced and effective policies. The proposed model offers a significant step forward in epidemic management, providing a robust platform for data-driven decision making in crisis scenarios.
Balcells et al. (Mon,) studied this question.