Machine Learning and Simulation for Public Health
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 9
Special Issue Editors
Interests: nonlinear systems and control; discrete and hybrid control; analysis; control applications; dynamic sensor networks; sampled data systems; machine learning for system modelling
Special Issues, Collections and Topics in MDPI journals
Interests: analysis, identification and control of biomedical systems; epidemic modelling and control; optimal control for resource management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning is becoming a key instrument in public health, offering innovative solutions for analysing complex datasets and supporting decision-making at both clinical and population levels. Modern health challenges generate vast amounts of heterogeneous data, from electronic health records to genomic sequences and from environmental exposures to social determinants of health. Machine learning can capture nonlinear relationships within these diverse sources and uncover predictive patterns that guide interventions.
An important example of the contribution of machine learning is in surveillance and early detection: predictive models trained on real-time demographic and health data can anticipate outbreaks before they become clinically evident, allowing timely interventions and more efficient allocation of resources. At the same time, algorithms that integrate genetic, behavioural, and environmental information enable the design of targeted prevention strategies, strengthening the bridge between individual care and population health policy. The adoption of these tools, however, raises challenges regarding data quality, interpretability, and fairness.
Soon, machine learning in public health is expected to benefit from real-time data streams generated by wearable devices and mobile health applications, offering continuous monitoring of population well-being. All these developments suggest that machine learning will soon become a cornerstone of proactive, data-informed public health strategies.
Alongside this fast expansion of machine learning approaches in public health applications, the present Special Issue aims at representing a reference point both for the state of the art and for suggesting and presenting possible lines of developments.
Dr. Paolo Di Giamberardino
Dr. Daniela Iacoviello
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- public health
- health data analytics
- population health management
- data-driven healthcare
- wearable devices
- disease surveillance
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