{
  "abstract": "Antimicrobial resistance (AMR) continues to pose a major public health threat across Africa, yet available surveillance data remain highly fragmented across private, public, and academic sources. This study analysed continent-wide AMR surveillance patterns by integrating datasets from multiple independent repositories and visualising them through interactive Geographic Information System (GIS) dashboards. The objective was to generate an integrated evidence base that highlights resistance patterns, surveillance disparities, reporting gaps, and opportunities for improved AMR monitoring across Africa. Data were compiled from major private AMR surveillance programmes including Pfizer’s ATLAS, GSK’s SOAR, Johnson & Johnson’s DREAM, Venatorx’s GEARS, and Shionogi’s SIDERO-WT covering the period 2004–2022. Public datasets from the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) and the Fleming Fund’s Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) were incorporated for 2016–2020, together with published AMR studies conducted between 2010 and 2024. Datasets were harmonised to align key variables including bacterial species, isolate identifiers, antibiotics tested, surveillance source, geographical location, and categorical AMR outcomes while preserving the original structure of the contributing datasets. Interactive dashboards were developed using R Shiny to support spatial visualisation and dynamic analytical exploration of resistance patterns, temporal trends, species distribution, and country-level surveillance coverage. Descriptive analyses including means, standard deviations, medians, interquartile ranges (IQR), frequency distributions, Gini coefficients, Shannon entropy, Herfindahl–Hirschman Index (HHI), and Lorenz curves were used to assess inequality and concentration in country-level AMR reporting across surveillance systems. The integrated analyses revealed substantial heterogeneity and concentration in AMR surveillance reporting across Africa, reflecting major differences in surveillance intensity, laboratory infrastructure, reporting systems, and diagnostic capacity across countries. Private datasets demonstrated broader antibiotic panels and longer temporal coverage, whereas public datasets exhibited substantial gaps in country participation and pathogen-antibiotic representation. Published AMR studies additionally highlighted important surveillance information absent from formal surveillance databases. By integrating multiple streams of AMR evidence, this study demonstrates the value of interactive GIS dashboards as exploratory and updateable surveillance-support tools for improving visibility of fragmented AMR datasets, identifying surveillance disparities, supporting geographically informed interpretation of resistance trends, and strengthening future AMR surveillance harmonisation efforts across Africa.",
  "abstract_shown": true,
  "affiliations": [
    "University of Ghana, Accra, Ghana",
    "Patira Data Science, Westerville, Ohio, United States of America",
    "Patira Data Science, Kampala, Uganda",
    "University of Sacred Heart, Gulu, Uganda"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wendy Akushika Dogbegah"
    },
    {
      "affs": [
        1
      ],
      "name": "Stephen Obol Opiyo"
    },
    {
      "affs": [
        2
      ],
      "name": "Peace Proscovia Aber"
    },
    {
      "affs": [
        3
      ],
      "name": "Christian Kaembou Tiambo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9merev9xyd9en.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0356734",
  "fulltext_url": null,
  "id": "9merev9xyd9en",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0356734",
  "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",
      "2402|Antibiotic Use and Resistance"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Microbial control/Antimicrobial resistance"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Microbial control/Antimicrobial resistance/Antibiotic resistance"
    ],
    [
      "plos",
      "/Biology and life sciences/Microbiology/Microbial control/Antimicrobials/Antibiotics"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Data visualization"
    ],
    [
      "plos",
      "/Computer and information sciences/Geoinformatics/Geographic information systems"
    ],
    [
      "plos",
      "/Earth sciences/Geography/Geoinformatics/Geographic information systems"
    ],
    [
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      "/Medicine and health sciences/Epidemiology/Disease surveillance"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Antimicrobial resistance"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Antimicrobial resistance/Antibiotic resistance"
    ],
    [
      "plos",
      "/Medicine and health sciences/Pharmacology/Drugs/Antimicrobials/Antibiotics"
    ],
    [
      "plos",
      "/Medicine and health sciences/Public and occupational health"
    ],
    [
      "plos",
      "/People and places/Geographical locations/Africa"
    ]
  ],
  "openalex": "W7214220529",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0356734&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9merev9xyd9en.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pone.0356734",
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      "url": "https://doi.org/10.1371/journal.pone.0356734"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:microbiology",
      "subfield": "microbiology"
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    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
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    {
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      "origin": "plos:pharmacology",
      "subfield": "pharmacology-toxicology"
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  "title": "Using GIS Dashboards to highlight AMR data disparities in Africa for Policy, Research, and Public Health",
  "title_text": "Using GIS Dashboards to highlight AMR data disparities in Africa for Policy, Research, and Public Health",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/9merev9xyd9en",
  "venue": "PLOS One",
  "versions": []
}