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No single theory or method is sufficient to explain complex phenomena such as EID and the relationships between factors influencing disease outbreaks. Integrated approaches—bridging multiple social sciences and bridging social and non-social sciences—are time consuming and challenging enterprises, but arguably the most fruitful if they provide an in-depth description of and improved predictive capacity for a complex problem. The initial framework we present for the analysis of socialecological models of EIDs is useful for scholars from diverse disciplines as a method for examining the relationships within and among multiple components of CNH systems. Given that other researchers have already identified the relevance of these components for explaining HPAI, we have some confidence in our model as a starting point. Future research will need to examine the extent to which relationships among these components meaningfully capture the construct of transition and explain HPAI outbreaks in Vietnam.
Once a model has been proven robust, we will be able to examine specific conditions and identify specific components of CNH systems that amplify or attenuate HPAI risk. More systematic analyses of CNH systems will improve our understanding of how transformations in social-ecological systems produce feedbacks that affect natural communities, their pathogens, animal host, and human populations at diverse spatial, temporal, and organizational scales.
Given its importance and difficulty, we conclude that social science integration requires a carefully considered theoretical rationale and a model-guided methodological approach. This approach will provide for cumulative results from multiple studies designed to investigate various aspects of the model. To test the robustness of this approach, interdisciplinary research teams will need to examine the consistency of results across independent data sets, ideally with different operationalizations of the relevant theoretical constructs.
By further developing and applying conceptual frameworks that take into account the complexity of real-world systems we can build the knowledge base necessary to advance our understanding in a manner meaningful to policy makers. Ultimately, such frameworks offer a flexible tool for diagnosing and dealing with the multiple challenges facing rapidly developing communities.
Acknowledgments Author order is alphabetical; the authors contributed equally to the development of this manuscript. The authors acknowledge Drs. Nancy Lewis, Bruce Wilcox, Michael DiGregorio, and Durrell Kapan for their contributions to conceptualization of the framework. We are grateful for support from the East-West Center, Honolulu HI, and the National Science Foundation through grant 0909410.
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