{
  "abstract": "Health system resilience is essential for effective pandemic response and health security, yet existing assessments often lack actionable guidance for policymakers. This study develops a data-driven framework to identify key resilience contributors and country capability profiles using the complete 2021 Global Health Security Index (GHSI) dataset for 195 countries. Through a multi-method analytical approach, we derive a composite resilience measure that captures 70.6% of shared variance across six GHSI domains. After accounting for inter-domain correlations, Detection, Health Capacity, and Prevention show the strongest independent associations with overall resilience (ridge coefficients of 0.242, 0.228, and 0.219, respectively), with Detection contributing most. Cluster analysis reveals two distinct country typologies: a high-performing, balanced-capability group (33% of countries) spanning both high-income and several upper-middle-income countries, and a larger group (67%) with lower and more structurally uneven capabilities, particularly in Prevention and Health Capacity. These profiles suggest different strategic pathways for system strengthening—from sustaining advanced capacities in high-performing countries to targeted investments in detection and core health functions in settings with more fragmented capabilities. Our findings provide an essential framework for comparing resilience across countries and prioritising investments where they matter most, offering practical guidance for national and global health security planning.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Biometry, School of Mathematical Sciences, University of Technology and Applied Sciences, Navrongo, Ghana",
    "Department of Epidemiology and Biostatistics, School of Public Health, University of Technology and Applied Sciences, Navrongo, Ghana"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Richmond Balinia Adda"
    },
    {
      "affs": [
        1
      ],
      "name": "Conrad Pwayirane"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0ga7ak1c0q3mz.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0340560",
  "fulltext_url": null,
  "id": "0ga7ak1c0q3mz",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0340560",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3312|Global Security and Public Health"
    ],
    [
      "plos",
      "/Computer and information sciences/Network analysis/Network resilience"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Pandemics"
    ],
    [
      "plos",
      "/Medicine and health sciences/Health care/Socioeconomic aspects of health"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Global health"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health/Socioeconomic aspects of health"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Applied mathematics/Algorithms/Clustering algorithms"
    ],
    [
      "plos",
      "/Physical sciences/Mathematics/Statistics/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Statistical methods/Multivariate analysis/Principal component analysis"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Algorithms/Clustering algorithms"
    ],
    [
      "plos",
      "/Social sciences/Political science/National security"
    ]
  ],
  "openalex": "W7214116643",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0340560&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/0ga7ak1c0q3mz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0340560",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0340560"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "plos:statistical methods",
      "subfield": "statistics"
    },
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Determinants and typologies of health system resilience: A data-driven framework for prioritising investment",
  "title_text": "Determinants and typologies of health system resilience: A data-driven framework for prioritising investment",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/0ga7ak1c0q3mz",
  "venue": "PLOS One",
  "versions": []
}