A Conceptual Framework for Analyzing Social-Ecological Models of Emerging Infectious Diseases

Unraveling mechanisms underlying new and reemerging infectious diseases (EID) requires exploring complex interactions within and among coupled natural and human (CNH) systems. To address this difficult scientific problem, we need to understand how transformations in social-ecological systems caused by multifaceted interactions with anthropogenic environmental changes such as urbanization, agricultural transformations, and natural habitat alterations, produce feedbacks that affect natural communities and ultimately their pathogens, animal host, and human populations. Focusing on the complex interactions among natural and human systems at diverse spatial, temporal, and organizational scales, we describe the development of a framework for analyzing social-ecological models, to understand how these systems function and the processes through which these systems interact with each other to influence disease outbreaks. To address multi-scale issues within the framework, we draw upon multiple social science theories and methods (e.g., environmental economics, geography, decision and risk science, urban and regional development, and spatial information science). We posit that the framework helps to identify potential vulnerabilities of CNH systems to disturbances, describing important elements as a starting point for the development and testing of more general CNH systems. We also posit that transformations in the elements and how they relate to each other are key in determining the robustness of CNH systems. Given the importance and difficulty of research on social-ecological systems, we recommend a carefully considered theoretical rationale and a model-guided methodological approach. We conclude that no single theory or method is sufficient to explain complex phenomena such as EID and the relationships between factors influencing disease outbreaks. Integrated approaches are arguably the best way to provide an in-depth description and analysis of a complex problem.