Examining Association Between Cohesion and Diversity in Collaboration Networks of Pharmaceutical Clinical Trials with Drug Approvals


Understanding the dynamics of a changing world are of great interest to policy-makers, nonprofit organizations, governments, and businesses since society largely operates as a system. We develop system models to capture the complexity of the world in a logical and quantitative manner. Specifically, we use methods such as network analysis, time series analysis, system dynamics, and Markov Chains to explore systemic issues. These methods are applied to a socio-technical system related to public health and sustainability. We will also explore ways to capture this complexity by first identifying and analyzing the system with an interdisciplinary perspective then propose a method to integrate system models. We begin by identifying the complexity of large-scale systems, such as Research & Development (R&D) of pharmaceutical treatments. In this project, we utilize a network representation to investigate collaboration among pharmaceutical companies and other stakeholders to determine the causes that enable success in developing a regulatory-approved therapeutic treatment. Secondly, we propose an integrated multi-component model to capture the feedback loops that couples global population growth, environmental sustainability, and health systems. Finally, we investigate a system dynamics integration of a Markov Chain that describes migration patterns of the United States with respect to climate change.

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