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BMC Medical Informatics and Decision Making is calling for submissions to our Collection on Infectious disease outbreak detection and prevention. The emergence and re-emergence of infectious diseases have highlighted the critical need for effective outbreak detection and prevention strategies. With the rise of global travel and interconnectedness, pathogens can spread rapidly across borders, necessitating robust systems for monitoring and managing outbreaks. This Collection aims to gather research that focuses on innovative methods and technologies for detecting disease outbreaks and preventing their spread, including the use of data analytics, machine learning, and real-time surveillance systems.
The importance of this research lies in its potential to save lives and mitigate the economic and social impacts of infectious disease outbreaks. Advances in health informatics and data-driven decision-making have transformed our approach to infection management, enabling timely responses to emerging threats. Notably, the integration of monitoring sensors and tracking systems has improved our ability to detect patterns of disease transmission, facilitating proactive measures to prevent infection and control outbreaks before they escalate.
Looking to the future, continued research in this field holds the promise of developing sophisticated predictive models that leverage big data and artificial intelligence. These advancements could revolutionize our understanding of disease dynamics, allowing for more accurate outbreak predictions and tailored prevention strategies. Enhanced collaboration among researchers, public health officials, and technology developers will be essential for creating integrated systems that are responsive to the evolving landscape of infectious diseases. Potential topics include but are not limited to:
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.
Submit your manuscript to this collection through the participating journal.
BMC Medical Informatics and Decision Making is an open access, peer-reviewed journal that considers articles related to the design, development, implementation, use, and evaluation of...
Dr Bankole (Banky) Olatosi is an Associate Professor of Health Services Policy and Management at the University of South Carolina. He earned his PhD in Health Services Policy and Management from the University of South Carolina and an MPH in Public Health Administration and Policy from the University of Minnesota. His research integrates business intelligence, applied data science, and advanced statistical methods to address complex population health challenges. Dr Olatosi focuses on the use of large-scale electronic health record, surveillance, and geospatial data to improve HIV prevention, treatment, and care, with particular attention to health disparities, viral suppression, retention in care, and the intersection of HIV with emerging infectious diseases such as COVID‑19. His work also includes the development of machine learning and predictive modeling approaches to enhance healthcare delivery and population-level outcomes.
Dr Xiaomin Zhong is a Health Data Epidemiologist at the Nuffield Department of Population Health, University of Oxford, and an Honorary Research Associate at the University of Manchester. His research uses large-scale, real-world health data to inform public health policy and improve clinical care, with collaborative projects spanning cardiovascular disease, infectious diseases, and oncology. Dr Zhong holds a PhD in Health Informatics and an MSc in Health Data Science from the University of Manchester, and a BSc in Medical Laboratory Technology from Peking University.