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Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations
Citation:
Comess, S., A. Akbay, M. Vasiliou, R. Hines, L. Joppa, V. Vasiliou, AND N. Kleinstreuer. Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations. Frontiers in Artificial Intelligence. Frontiers, Lausanne, Switzerland, 3:31, (2020). https://doi.org/10.3389/frai.2020.00031
Impact/Purpose:
Understanding interactions between individuals and their environment often involves large, complex data sets. There have been a number of private and public efforts to confront significant unknowns in the field of environmental public health,yet an absence of comprehensive and accessible data persists. Even when available, the ability to create, analyze and draw conclusions from these data using emerging technologies involves a set of technical skills not yet thoughtfully implemented across research hubs and academic institutions. We argue that the absence of collaborative efforts in both data storage and innovation hinders the ability of researchers within environmental and the broader public health community to satiate their potential. This paper offers possible solutions and directions forward in the application of AI to the environmental health sciences.
Description:
Understanding the role that the environment plays in influencing public health often involves collecting and studying large, complex data sets. There have been a number of private and public efforts to gather sufficient information and confront significant unknowns in the field of environmental public health, yet there is a persistent and largely unmet need for findable, accessible, interoperable, and reusable (FAIR) data. Even when data are readily available, the ability to create, analyze, and draw conclusions from these data using emerging computational tools, such as augmented and artificial intelligence (AI) and machine learning, requires technical skills not currently implemented on a programmatic level across research hubs and academic institutions. We argue that collaborative efforts in data curation and storage, scientific computing, and training are of paramount importance to empower researchers within environmental sciences and the broader public health community to apply AI approaches and fully realize their potential. Leaders in the field were asked to prioritize challenges in incorporating big data in environmental public health research: inconsistent implementation of FAIR principles in data collection and sharing, a lack of skilled data scientists and appropriate cyber-infrastructures, and limited understanding of possibilities and communication of benefits were among those identified. These issues are discussed, and actionable recommendations are provided.
URLs/Downloads:
DOI: Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations