Towards dismantling healing illicit & counterfeit medicines seller networks (ICMSN) using percolation theory & machine learning: A simulation study

Illicit and counterfeit medicines (ICM) are responsible for over half a million deaths annually, accounting for approximately $75B of the $962B global pharmaceutical market. To reduce the societal harm that comes from these products, new approaches which can identify, intervene, and disrupt the trade of ICM are needed. Meanwhile, recent work has demonstrated the potential of machine learning (ML) for dismantling complex societal networks of interest better than current SOTA analytical methods. This project takes a rigorous scientific approach to learn what makes ICM seller networks (ICMSN) so adaptive and resilient by finding the fundamental static building blocks of ICMSN and modeling flows of drugs and money on dynamic ICMSN. We discuss the network characteristics of a static ICMSN constructed in-house from actual data, finding that its disconnected structure can be clustered using community detection techniques. We adapt the Graph Dismantling with Machine Learning (GDM) framework to explore dismantling clusters of ICMSN, using percolation theory as an oracle for understanding critical and emergent phenomena on these networks. We also present various modeling strategies for healing & rewiring between and within ICMSN clusters and their effects on the outcome of GDM.