BMC Infectious Diseases invites submissions for a Collection on Personalized medicine in infectious diseases.

The field of personalized medicine is rapidly advancing, particularly among infectious diseases, where the traditional ‘one-size-fits-all’ treatment approaches are increasingly inadequate. As our understanding of the genetic, environmental, and lifestyle factors influencing disease susceptibility and treatment response grows, this Collection aims to explore innovative strategies and technologies that can enhance the application of personalized medicine in combating infections.

Recent advancements in genomics, pharmacogenomics, and bioinformatics are transforming the application of personalized approaches for infectious diseases. These developments have enabled the identification of specific biomarkers that inform individualized treatment strategies, improving patient outcomes and minimizing adverse effects. In parallel, advances in molecular diagnostics and tailored antimicrobial therapies offer new opportunities to address the escalating challenge of antimicrobial resistance.

Improved understanding of host-pathogen interactions and the genetic determinants of infections may support the development of targeted antiviral and antimicrobial strategies that enhance their effectiveness, while limiting the emergence of resistance. Research in this area may also enable personalized antimicrobial dosing and optimized treatment guidelines that balance individual patient benefits with broader public health considerations.

This Collection welcomes original research articles on topics including but not limited to:

• Tailored antimicrobial therapy in infectious diseases
• Patient-specific interventions to improve clinical outcomes
• Personalized strategies to address antimicrobial resistance
• Precision medicine approaches in antiviral therapy
• Novel methodologies for disease modelling and therapeutic targeting
• Precision diagnostics, including biomarker validation and metagenomic sequencing
• Model-informed personalized dosing in infectious diseases
• Therapeutic drug monitoring in anti-infective therapy

This Collection supports and amplifies research related to SDG 3 (Good Health and Well-being).

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

BMC Global and Public Health is calling for submissions to our Collection on how the integration of advanced data-driven methods in public health policy is rapidly transforming how we understand and respond to global health challenges. This Collection focuses on the application of innovative statistical modelling, machine learning, and geographic information systems (GIS) to inform health policy decisions. By integrating data from surveys and health records with information on social and environmental determinants, researchers can uncover actionable insights that enhance the effectiveness and efficiency of health interventions across diverse populations.

The relevance of these advanced methodologies is underscored by recent advancements in methods for data collection and analysis, which have enabled researchers to create more precise models for predicting disease outbreaks, evaluating health interventions, and addressing health disparities. As public health continues to confront complex issues such as pandemics and non-communicable diseases, the ability to leverage data-driven strategies becomes increasingly vital for crafting evidence-based policies that are responsive to real-world challenges.

Continued research in this area could lead to groundbreaking methodologies that enhance our understanding of health dynamics and improve public health responses. Future advancements may include the development of real-time data analytics for tracking disease spread, enhanced spatial epidemiological models that account for environmental variables, and improved collaboration between data scientists and public health professionals. By further integrating data science with public health, we can pave the way for more effective interventions and ultimately improve health outcomes globally.

We encourage submissions in the following areas, with a particular interest in studies relevant to low- and middle-income countries (LMICs); however, contributions from all settings are welcome, including:

• Applications of Machine Learning in Public Health
• Spatial Epidemiology and Disease Mapping
• Statistical Modeling for Health Policy Decisions
• GIS in Public Health Decision Making
• Evidence-Based Approaches to Infectious and Non-communicable Diseases

This Collection supports and amplifies research related to SDG 3: Good Health and Well-being.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.