Data Statistics for Epidemiological Research
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".
Deadline for manuscript submissions: 20 February 2025 | Viewed by 748
Special Issue Editor
2. LASI – Associate Laboratory of Intelligent Systems, Guimarães, Portugal
Interests: artificial intelligence; biostatistics; cognitive psychology; cognitive science and applications; intelligent systems; human–machine interaction; statistics applied to health sciences; statistics applied to quality
Special Issue Information
Dear Colleagues,
Epidemiological research is at the forefront of public health efforts, seeking to comprehend the spread, causes and changes of diseases among populations. Key to this endeavor is the meticulous application of statistical methods to scrutinize intricate datasets, revealing trends, pinpointing risk factors and guiding evidence-based interventions. Acknowledging the pivotal significance of data statistics in propelling epidemiological understanding, we are delighted to unveil a Special Issue devoted to data statistics for epidemiological research. This Special Issue aims to offer a platform for researchers, statisticians, epidemiologists and public health practitioners to present their innovative approaches, methodologies and discoveries in the field of data statistics in epidemiological research. These papers are expected to utilize statistical methodologies in epidemiological research encompassing machine learning and artificial intelligence techniques to aid in uncovering complex relationships within epidemiological data, but are not limited to this field. Effective data visualization and interpretation facilitate the communication of epidemiological findings. Overcoming statistical challenges, integrating big data analytics and addressing ethical considerations are vital for ensuring the integrity and impact of epidemiological research.
Dr. Estela Vilhena
Guest Editor
Manuscript Submission Information
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Keywords
- big data analytics
- data analysis
- data visualization
- epidemiology
- health outcomes
- machine learning
- public health
- risk factors
- spatial analysis
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