Statistical and Simulation Methods in Public Health, Medical Diagnosis, Prognosis, and Treatment
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Global Health".
Deadline for manuscript submissions: 28 February 2027 | Viewed by 167
Special Issue Editor
Interests: system modeling; health informatics; machine learning and health policy
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Statistical and simulation methods play an increasingly vital role in advancing public health and clinical care. This Special Issue showcases innovations that enhance population‑level surveillance, diagnostic accuracy, prognostic evaluation, and treatment optimisation. Contributions highlight applications of machine learning and advanced statistical modelling for disease prediction, Bayesian methods for uncertainty quantification, and simulation techniques, such as system dynamics, agent‑based modelling, and discrete event simulation, that support policy planning and intervention evaluation. By integrating diverse data sources, including epidemiological, genomic, imaging, and real‑world clinical datasets, as well as clinician’s opinions, consumers’ preferences, and AI inputs, these approaches generate insights that strengthen evidence‑based decision‑making. This collection aims to bridge methodological advances with practical applications, supporting precision medicine, effective public health responses, and improved patient outcomes across diverse healthcare contexts.
Dr. Hadi Akbarzadeh Khorshidi
Guest Editor
Manuscript Submission Information
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Keywords
- statistical modelling in public health decision-making
- innovative techniques for health policy evaluation
- simulation for health technology assessment and interventions
- optimal decision-making for complex healthcare systems
- modelling for precision & personalised medicine
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