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<p>The Comprehensive Policy for the Prevention and Care of Psychoactive Substance Use in Colombia aims to improve the care provided to people, families, and communities at risk or struggling with psychoactive substance use through prevention and mitigation programs. The effectiveness of these programs depends on both population participation and access to intervention centers. This study proposes a bi-objective integer programming model within a location-allocation framework to support policy decisions under budget constraints. To estimate drug-related risk, we integrate sentiment analysis from social media data (X, formerly Twitter) as a key input into the optimization model. Specifically, negative sentiment derived from posts is used to inform the spatial distribution of risk between locations. The model simultaneously minimizes population-level risk and distance to services, while ensuring equitable coverage based on multidimensional poverty and rurality. The proposed approach was applied to real-world data from Atlántico. The results demonstrated that the bi-objective model achieved an average coverage of 24.67% of the population within a 40 km radius, effectively balancing service accessibility between high-risk urban areas and underserved rural zones. Compared to a population-based heuristic, which achieved only 8.85% coverage and excluded 22 of 23 locations, the proposed model significantly improved equity in service distribution. Furthermore, complementary topic modeling using Latent Dirichlet Allocation (LDA) revealed key themes in public discourse, including drug cartels, addiction risks, and social impacts, providing valuable information to support tailored communication and community engagement strategies for new intervention centers.</p>

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Bi-objective location-allocation model of interventions in high drug consumption areas incorporating X topic modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月8日

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  • Kevin Palomino,
  • Carmen Berdugo,
  • Jorge Acuña,
  • José L. Zayas-Castro

摘要

The Comprehensive Policy for the Prevention and Care of Psychoactive Substance Use in Colombia aims to improve the care provided to people, families, and communities at risk or struggling with psychoactive substance use through prevention and mitigation programs. The effectiveness of these programs depends on both population participation and access to intervention centers. This study proposes a bi-objective integer programming model within a location-allocation framework to support policy decisions under budget constraints. To estimate drug-related risk, we integrate sentiment analysis from social media data (X, formerly Twitter) as a key input into the optimization model. Specifically, negative sentiment derived from posts is used to inform the spatial distribution of risk between locations. The model simultaneously minimizes population-level risk and distance to services, while ensuring equitable coverage based on multidimensional poverty and rurality. The proposed approach was applied to real-world data from Atlántico. The results demonstrated that the bi-objective model achieved an average coverage of 24.67% of the population within a 40 km radius, effectively balancing service accessibility between high-risk urban areas and underserved rural zones. Compared to a population-based heuristic, which achieved only 8.85% coverage and excluded 22 of 23 locations, the proposed model significantly improved equity in service distribution. Furthermore, complementary topic modeling using Latent Dirichlet Allocation (LDA) revealed key themes in public discourse, including drug cartels, addiction risks, and social impacts, providing valuable information to support tailored communication and community engagement strategies for new intervention centers.