{
  "abstract": "Accurate, timely and policy-relevant assessment of population health needs is essential for effective health planning, transparent prioritisation and equitable resource allocation. This skills-building workshop aims to strengthen participants’ capacity to use health data, advanced epidemiological methods and artificial intelligence (AI)-enabled analytics to assess disease burden and support public health action. The session will be delivered in PechaKucha format, consisting of three presentations (15 slides, 15 seconds each) followed by discussion. Organised by the Chronic Disease and Public Health Economics sections, the workshop will explore how linked routine data can support health profiling techniques, including disease identification, burden-of-disease assessment, disability estimation, trajectory forecasting and analysis of self-perceived health. Particular attention will be paid to the contribution of AI and advanced epidemiological methods in dealing with complex, high-dimensional and heterogeneous data, improving the precision, timeliness and policy relevance of population-level analyses. The workshop will consist of three complementary presentations. 1) AI-enabled health profiling, disability estimation and forecasting using linked health data will show how epidemiological approaches, machine learning and neural network applications can be combined to identify chronic conditions more accurately, estimate disability burden and model disease trajectories over time. Neurological disorders, including Alzheimer’s disease, other dementias and multiple sclerosis, will serve as the main case studies, illustrating how linked data and AI-supported analysis can strengthen surveillance and reveal unmet needs relevant to service organisation and decision-making. 2) Quantitative frameworks for health planning and prioritisation will examine how burden-of-disease evidence and related tools can inform the health planning cycle at national and local level, with emphasis on priority-setting and structured evidence for transparent planning processes.3) Health economic modelling for policy simulation will address how long-term health and cost consequences of interventions can be assessed to support policy choices under real-world budget constraints, highlighting the role of modelling in comparing strategies and translating evidence into policy insights. By combining methodological depth, applied case studies and interactive discussion, the workshop will provide participants with concepts and practical tools for integrating health data analysis, epidemiology and policy-oriented evaluation. It is particularly addressed to researchers, practitioners and decision-makers in epidemiology, biostatistics, health services research, data science and health economics who wish to deepen their understanding of how data-driven methods can support burden profiling, prioritisation and planning in public health.\n\nKey messages: • This seminar aims to build capacity in the use of advanced modelling tools to quantify and forecast chronic disease burden using integrated real-world health data.\n\n• Will provide practical,expert-led sessions on disease identification, disability estimation, priority setting,policy simulation, with applied examples focusing on neurological conditions and addiction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "abstract",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Department of Public Health - University of Naples Federico II (Italy), EUPHA Public Health Economics section, EUPHA Chronic Diseases section"
    },
    {
      "name": "Raffaele Palladino"
    },
    {
      "name": "Saverio Stranges"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/frh0ey998maa3.bib",
  "cited_by": 0,
  "doi": "10.1093/eurpub/ckag169.718",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13697778/",
  "id": "frh0ey998maa3",
  "issns": [
    "1101-1262",
    "1464-360X"
  ],
  "landing_url": "https://doi.org/10.1093/eurpub/ckag169.718",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "4. Pecha Kucha sessions"
    ],
    [
      "heading",
      "AcademicSubjects/MED00860"
    ],
    [
      "heading",
      "AcademicSubjects/SOC01210"
    ],
    [
      "heading",
      "AcademicSubjects/SOC02610"
    ],
    [
      "openalex",
      "2713|Chronic Disease Management Strategies"
    ]
  ],
  "openalex": "W7223670920",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13697778/pdf/?tool=EBI",
  "pmcid": "PMC13697778",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "Oxford University Press",
  "ris": "https://research.draconitic.com/paper/frh0ey998maa3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:46Z",
      "native_id": "PMC13697778",
      "source": "pmc",
      "url": "https://doi.org/10.1093/eurpub/ckag169.718"
    },
    {
      "first_seen": "2026-10-30T12:21:31Z",
      "native_id": "PMC:PMC13697778",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13697778"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2713|Chronic Disease Management Strategies",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Y.7.PK.1. From Data to Decisions: AI, Epidemiology and Modeling for Health Burden Profiling",
  "title_text": "Y.7.PK.1. From Data to Decisions: AI, Epidemiology and Modeling for Health Burden Profiling",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:34:45Z",
  "url": "https://research.draconitic.com/paper/frh0ey998maa3",
  "venue": "The European Journal of Public Health",
  "versions": []
}