About this Research Topic
Traditional epidemiology has significantly contributed to understanding risk factors associated with chronic diseases. As high-throughput data and advanced analytical technologies become increasingly prevalent, there is a growing need to enhance epidemiological methods to elucidate the complex processes underlying disease risk better. This Research Topic focuses on systems epidemiology, which integrates biological, behavioral, and demographic data with state-of-the-art computational techniques to explore and better understand disease mechanisms.
This Research Topic aims to create a platform for interdisciplinary research that integrates big data, state-of-the-art computational methodologies, and traditional epidemiological practices. Topics of interest include, but are not limited to, the following:
- Systems epidemiology approaches to delineate pathways influencing chronic disease mechanisms.
- Advances in computational methods (e.g., machine learning, network analysis) to model disease progression and risk factors.
- Opportunities and challenges in integrating 'omics' data with traditional epidemiological approaches for assessing disease risk.
- Spatiotemporal mapping in environmental epidemiology to uncover actionable patterns in chronic disease risk.
- The potential and limitations of AI in refining epidemiological analyses and enhancing disease prediction models.
- Case studies highlighting the integration of big data in epidemiology, including successes and challenges.
- Evaluations of methodological strengths, limitations, and reproducibility in current epidemiological practices.
We seek contributions from researchers across epidemiology, data science, biology, and public health who are interested in addressing the challenges and opportunities posed by high-throughput data and computational advancements. Submissions can include original research articles, systematic reviews, case studies, methodology papers, and viewpoints that highlight novel research, rigorous methodologies, and interdisciplinary approaches.
This Research Topic aims to bridge traditional epidemiology with the complexities of modern chronic disease research. We encourage contributors to expand current knowledge, fostering innovative prevention and treatment strategies to address the rising burden of chronic diseases.
This Research Topic aims to create a platform for interdisciplinary research that integrates big data, state-of-the-art computational methodologies, and traditional epidemiological practices. Topics of interest include, but are not limited to, the following:
- Systems epidemiology approaches to delineate pathways influencing chronic disease mechanisms.
- Advances in computational methods (e.g., machine learning, network analysis) to model disease progression and risk factors.
- Opportunities and challenges in integrating 'omics' data with traditional epidemiological approaches for assessing disease risk.
- Spatiotemporal mapping in environmental epidemiology to uncover actionable patterns in chronic disease risk.
- The potential and limitations of AI in refining epidemiological analyses and enhancing disease prediction models.
- Case studies highlighting the integration of big data in epidemiology, including successes and challenges.
- Evaluations of methodological strengths, limitations, and reproducibility in current epidemiological practices.
We seek contributions from researchers across epidemiology, data science, biology, and public health who are interested in addressing the challenges and opportunities posed by high-throughput data and computational advancements. Submissions can include original research articles, systematic reviews, case studies, methodology papers, and viewpoints that highlight novel research, rigorous methodologies, and interdisciplinary approaches.
This Research Topic aims to bridge traditional epidemiology with the complexities of modern chronic disease research. We encourage contributors to expand current knowledge, fostering innovative prevention and treatment strategies to address the rising burden of chronic diseases.
Keywords: Epidemiology, Machine Learning, Artificial Intelligence, Disease Patterns, Systems Epidemiology, Computational Methods
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
