{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:09:13Z","timestamp":1790273353767,"version":"4.1.0"},"reference-count":43,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>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\u2019s ATLAS, GSK\u2019s SOAR, Johnson &amp; Johnson\u2019s DREAM, Venatorx\u2019s GEARS, and Shionogi\u2019s SIDERO-WT covering the period 2004\u20132022. Public datasets from the WHO Global Antimicrobial Resistance and Use Surveillance System (GLASS) and the Fleming Fund\u2019s Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) were incorporated for 2016\u20132020, 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\u2013Hirschman 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0356734","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:32:31Z","timestamp":1790271151000},"page":"e0356734","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Using GIS Dashboards to highlight AMR data disparities in Africa for Policy, Research, and Public Health"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-3260-8571","authenticated-orcid":true,"given":"Wendy Akushika","family":"Dogbegah","sequence":"first","affiliation":[{"name":"University of Ghana","place":["Accra, Ghana"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen Obol","family":"Opiyo","sequence":"additional","affiliation":[{"name":"Patira Data Science","place":["Westerville, Ohio, United States of America"]},{"name":"Patira Data Science","place":["Kampala, Uganda"]},{"name":"University of Sacred Heart","place":["Gulu, Uganda"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peace Proscovia","family":"Aber","sequence":"additional","affiliation":[{"name":"University of Sacred Heart","place":["Gulu, Uganda"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian Kaembou","family":"Tiambo","sequence":"additional","affiliation":[{"name":"International Livestock Research Institute","place":["Nairobi, Kenya"]},{"name":"Center for Tropical Livestock Genetics and Health","place":["Nairobi, Kenya"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"issue":"10325","key":"pone.0356734.ref001","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/S0140-6736(21)02724-0","article-title":"Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis","volume":"399","author":"Antimicrobial Resistance Collaborators.","year":"2022","journal-title":"Lancet"},{"key":"pone.0356734.ref002","first-page":"1","article-title":"Drug-resistant infection: causes, consequences, and responses.","author":"E Jamrozik","year":"2020","journal-title":"Ethics and Drug Resistance: Collective Responsibility for Global Public Health. 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