AbdylGaniwu/qualitative-analysis-on-informal-antimicrobial-supply-chain

A Python-driven supply chain data analytics project modeling informal, non-prescription antibiotic sales networks, tracking data surveillance gaps, and simulating localized antimicrobial resistance (AMR) risk factors against Ghana Health Service frameworks. # Qualitative Analysis on Informal Antimicrobial Supply Chain A Python-driven supply chain data analytics project modeling informal, non-prescription antibiotic sales networks, tracking data surveillance gaps, and simulating localized antimicrobial resistance (AMR) risk factors against Ghana Health Service frameworks. ## 📌 Project Overview This repository contains a qualitative and data-driven analysis of informal antimicrobial supply chains, focusing on how non-prescription antibiotic sales contribute to the acceleration of localized Antimicrobial Resistance (AMR). By modeling informal distribution networks and mapping them against official healthcare frameworks (such as the Ghana Health Service), this project aims to identify critical data surveillance gaps and simulate AMR risk factors in regions heavily reliant on informal drug markets. ## 🛠️ Repository Contents * **`data-analysis.ipynb`**: A Jupyter Notebook containing the core Python data analytics pipeline, network visualizations, and risk simulation models. * **`qualitative-analysis-on-informal-antimicrobial-supply.pdf`**: The comprehensive research paper detailing the methodology, qualitative framework, policy implications, and analytical findings. ## 🚀 Key Features & Methodology * **Supply Chain Modeling:** Simulating the flow of antimicrobials through informal vendors, open-air markets, and non-prescription over-the-counter channels. * **Surveillance Gap Identification:** Tracking where formal health data collection fails to capture informal antibiotic consumption. * **AMR Risk Simulation:** Utilizing predictive data analytics to pinpoint localized hot spots at high risk for accelerated resistance profiles. * **Policy Alignment:** Evaluating findings against established public health frameworks, specifically targeting regional interventions. ## 🧰 Tech Stack * **Language:** Python 3.x * **Environment:** Jupyter Notebook * **Core Libraries:** *[Add libraries used, e.g., Pandas, NumPy, NetworkX, Matplo …