Applied Biostatistics: Current Challenges and Opportunities
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".
Deadline for manuscript submissions: 20 May 2026 | Viewed by 21
Special Issue Editor
Interests: applied statistics; biostatistics; computational statistics; ROC analysis; multivariate statistics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Given the high complexity and scale of the real world, data analysis requires advanced tools and methods to support effective decision-making. Today, with the development of data analytics, health information is more accessible and easier to analyze than ever before. In the health sciences, biostatistics plays a key role in evaluating data to better understand and address global health challenges faced by individuals and populations.
This Special Issue aims to highlight research in computational statistics, scientific computing, and their applications across disciplines dealing with large-scale or specialized datasets. It focuses on both computational statistics—such as the design of new algorithms, development in R/Python, and related topics—and applied biostatistics, including statistical case studies in medicine, biology, earth sciences, and social sciences.
Topics of interest include, but are not limited to, the following research areas: statistical inference, statistical computing, biostatistics, reliability, survival analysis, experimental design, multivariate analysis, nonparametric inference, statistical genetics, statistical quality control, survey sampling, and computational Bayesian methods.
This Special Issue follows the previous editions, “Applied Biostatistics & Statistical Computing” and “Applied Biostatistics: Challenges and Opportunities,” which concluded on December 31, 2024, and welcomes new research contributions.
Dr. Ana Cristina Braga
Guest Editor
Manuscript Submission Information
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Keywords
- applied statistics
- biostatistics
- computational statistics
- machine learning techniques
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