Data Science for Environment and Health Applications
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 2025 | Viewed by 89969
Special Issue Editors
Interests: surrogate models; physics-informed neural netwroks; uncertainty quantifcation; deep learning; flood and hydrodynamic modelling; modelling extreme events
Special Issues, Collections and Topics in MDPI journals
Interests: Sustainable Development Goals (SDG); applied systems analysis; Sustainability Impact Assessment (SIA); systemic sustainability; resilience; causal modelling; AI policy
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning and applications; deep learning; image processing; autonomous cars and smart cities
Special Issues, Collections and Topics in MDPI journals
2. School of Health and Related Research, The University of Sheffield, Regent Court, Sheffield S1 4DA, UK
Interests: Bayesian statistics; health economics; statistical modelling; health related quality of life; Bayesian modeling of health state preferences
Special Issue Information
Dear Colleagues,
Data science and analytics is a growing academic discipline, and has applications in numerous fields, including environmental science and health-related research. The significant advances in data capture, storage and analytic technologies have given rise to immense data augmentation. Inadvertently, this has resulted in the requirement of low-cost and computationally efficient techniques which are needed to analyse data in order to provide pertinent and purposeful insights. These insights inform policy making, organisational practices, future research trajectories and, most importantly, the sustainability of our societies in terms of our health and environment. This Special Issue concentrates on state-of-the-art data science techniques, practices and applications within this field. Manuscripts are welcome for this Special Issue with the focus placed on the latest advances of data analytics methods that address the research challenges in the environmental science and public health fields. To date, plenty of research is conducted in this field and relates, for example, to the choice of the technique, methodological development, ability to capture specific healthcare informatics, etc. Particular emphasis is placed on complexity, spatial and temporal reasoning and managing uncertainty. The scope of this Special Issue includes but is not limited to the following key areas:
- Data science application in public healthcare informatics;
- Methods, techniques in data collecting for public healthcare;
- Health economic case studies; health-related quality of life;
- Medical and clinical data analysis case studies;
- Machine learning methods in health science;
- Deep learning methods with applications in health science;
- Intelligent medical diagnosis;
- Applications of AI in healthcare;
- Medical information systems;
- Smart healthcare systems;
- Coastal flooding and erosion;
- Quantifying and modelling wildfire risk;
- Mangrove forest resilience;
- Copula models in modelling extreme climatic events;
- Approximation of the impacts of environmental changes on public health;
- Sustainability and resilience modelling and simulation;
- Estimations in food systems and environmental capacity;
- Systematic review and meta-analysis studies in environmental science and public health;
- Emerging data science techniques and technologies for environmental science and public health research;
- Bootstrapping and Monte Carlo simulations for risk prediction;
- Statistical/epidemiological modelling of disease risk;
- Health hazards of environmental pollution and degradation.
Dr. Alireza Daneshkhah
Prof. Dr. Amin Hosseinian-Far
Prof. Dr. Vasile Palade
Dr. Samer A. Kharroubi
Guest Editors
Manuscript Submission Information
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Keywords
- health economic case studies
- health-related quality of life
- data science application in public healthcare informatics
- medical and clinical data analysis case studies
- machine learning methods in health science
- intelligent medical diagnosis and smart healthcare systems
- quantifying and modelling extreme climatic events
- approximation of the impacts of environmental changes on public health
- sustainability and resilience modelling and simulation
- systematic review and meta-analysis studies in environmental science and public health
- statistical/epidemiological modelling of disease risk
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