Frontiers in Epidemiology is a multidisciplinary, DOAJ-indexed journal seeking to improve health at the clinical and population levels.

The journal publishes research at the intersection of clinical medicine and population/public health that actively develops suitable methods to address the pressing questions about medicine and population health that have a regional, national, or global relevance. The journal welcomes submissions in all areas of epidemiological research focusing on topics including health conditions, underlying causes of diseases, health outcome prediction, social inequalities, climate change impact on health, and innovative solutions for data analysis and interpretation. The editorial board is led by Tobias Kurth, MD, ScD (Charité - Universitätsmedizin Berlin, Germany).

The journal covers the following subject areas:

aging and life-course epidemiology
cardiovascular epidemiology
clinical epidemiology
epidemiology of autoimmune diseases
epidemiology of chronic diseases and prevention
infectious disease epidemiology
neurological and mental health epidemiology
occupational and environmental epidemiology
research methods and advances in epidemiology.

Contributions and active involvement from early career researchers in all stages of the review and editorial processes are encouraged. In particular, the journal welcomes submissions which support and advance the UN’s Sustainable Development Goals (SDGs), notably SDG 3: good health and well-being.

Manuscripts that focus solely on randomized clinical trials, medical treatments, or health outcomes without a relevance to epidemiological research are not suitable for publication in this journal. Additionally, studies that are purely theoretical or computational, without a relevance to real-world epidemiological issues, are not appropriate for this journal.

Frontiers in Epidemiology is committed to advancing developments in the field of epidemiology by communicating scientific knowledge to researchers and the public alike, enabling future scientific breakthroughs.

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Research Topics

Causal Methods in Clinical Prediction