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. 2025 Mar 7:9:234.
doi: 10.12688/wellcomeopenres.21010.4. eCollection 2024.

Empowering Global AMR Research Community: Interactive GIS dashboards for AMR data analysis and informed decision-making

Stephen Obol Opiyo  1   2   3 , Racheal Nalunkuma  4 , Stella Maris Nanyonga  5 , Nathan Mugenyi  6 , Andrew Marvin Kanyike  4
Affiliations

Empowering Global AMR Research Community: Interactive GIS dashboards for AMR data analysis and informed decision-making

Stephen Obol Opiyo et al. Wellcome Open Res. .

Abstract

Background: Antimicrobial Resistance (AMR) poses a global public health challenge, necessitating advanced tools to support data analysis, and visualization. This study introduces interactive Geographic Information System (GIS) dashboards as innovative platforms for AMR data analysis and visualization, offering comprehensive insights into resistance patterns, and geographic distribution across multiple countries, with a specific focus on Africa.

Methods: Three GIS dashboards were developed to address key objectives. The first integrates over 860,000 ATLAS data points from 83 countries, providing an interactive platform. Users can filter data by variables such as country, year, and region, enhancing data accessibility and visualization. The second dashboard focuses on the ATLAS dataset for Kenya and Uganda, incorporating detailed variables such as species, sample sources, and resistance phenotypes. The third involves Kampala, Uganda, to fill data gaps, enabling localized analyses through interactive features like geographic mapping and sample breakdowns by year.

Results: Sub-Saharan Africa faces three major challenges in handling antimicrobial resistance (AMR) data: limited accessibility for non-technical users, inefficiencies in processing large datasets, and insufficient longitudinal data for analysis. The introduction of interactive dashboards significantly improved AMR data visualization and interpretation across different scales. The global AMR dashboard effectively mapped geographical trends, uncovering critical data gaps, particularly the scarcity of AMR records from Africa. The Kenya and Uganda dashboard revealed key resistance patterns, highlighting the ineffectiveness of Ceftriaxone, Erythromycin, Levofloxacin, and Ampicillin against E. coli isolates. Additionally, the Kampala-specific dashboard, developed using simulated data, demonstrated the potential for localized AMR visualization, providing valuable insights where real-world data is limited. Across all platforms, the dashboards' interactive features enhanced data accessibility and streamlined trend identification, making AMR insights more interpretable, especially for researchers in Sub-Saharan Africa.

Conclusions: Interactive GIS dashboards enhance AMR data analysis in Sub-Saharan Africa by improving accessibility, efficiently handling large datasets, and addressing data gaps. Unlike spreadsheets such as Excel, which struggle with large datasets due to computer constraints, dashboards offer dynamic visualization, real-time updates, and intuitive data exploration.

Keywords: Antimicrobial Resistance (AMR); Data analysis; Geographic Information System (GIS) dashboards; Informed decision making.

Plain language summary

Antimicrobial Resistance (AMR) is increasingly serious global health issue, making it harder to treat common infections due to the reduced effectiveness of antibiotics. To address this challenge, researchers, healthcare providers, and professionals need tools to better understand AMR. This study introduces an interactive tool in the form of Geographic Information System (GIS) dashboards, which allow users to explore and analyze AMR datasets. The first objective was to create a global map that tracks antibiotic usage and resistance trends across multiple countries, enabling users to zoom in on specific areas, such as countries or years, for more detailed insights from ATLAS dataset. The second objective was to develop maps for Kenya and Uganda, highlighting the unique AMR challenges faced in these countries. The third goal focused on using this tool to address data gaps in Kampala, Uganda, and help users analyze AMR trends. These interactive maps are highly valuable. The global map reveals trends and identifies areas where data is lacking, especially in Africa. The maps for Kenya and Uganda show concerning resistance levels, where antibiotics are becoming ineffective against bacteria like E. coli. The Kampala map demonstrates how the tool can help provide a more detailed analysis of local AMR issues. Overall, these maps make it easier for experts to explore AMR data and identify key trends. In simple terms, these GIS dashboards are powerful tools for communicating and raising awareness about AMR. They allow us to visualize the spread of resistance and explore detailed AMR data. These interactive dashboards could play a crucial role in the global effort to combat AMR.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Barriers to AMR data analysis and visualization in Sub-Saharan Africa, including fragmented datasets, and limited computational infrastructure.
Figure 2.
Figure 2.
Interactive GIS Dashboards: A) Global Overview for 83 Countries, B) Uganda-Specific View, and C) Africa Regional Trends from 2004 to 2024. The global dashboard (Figure 2 A) features tools such as an interactive map, country selection, regional breakdowns, and intuitive navigation instructions, enabling users to analyze and explore data across multiple countries. In contrast, the Uganda-specific view (Figure 2 B) highlights data collected exclusively for 2021, with a total of 76 isolates, presented through a user-friendly interface designed for simplified data interpretation. Meanwhile, the Africa regional dashboard (Figure 2 C) illustrates the trend of data collection from 2004 to 2024, offering insights into regional patterns and temporal progress.
Figure 3.
Figure 3.
Interactive GIS Dashboards: The figure illustrates three components: A) a combined dashboard for Kenya and Uganda, highlighting key data points such as species, country, sample sources, counts, gender, and antibiotics; B) separate dashboards for Kenya and Uganda, offering country-specific insights; and C) antibiotic resistance in E. coli samples from Uganda, revealing resistance to Ampicillin, Levofloxacin, and Ciprofloxacin (red circles) in 21 samples, while all samples remained susceptible to Imipenem (green circle).
Figure 4.
Figure 4.
Interactive GIS dashboards: A) simulated Kampala data, B) Klebsiella aerogenes from only Lugala. Figure 4 A displays the simulated data examining various regions within Kampala, while Figure 4 B highlights the 2019 and 2021 detection of Klebsiella aerogenes exclusively in Lugala, revealing three resistance to Ampicillin but susceptibility to Amikacin. This simulated dataset, offers crucial insights into AMR trends and can be used to empower stakeholders for more informed decision-making.
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