{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:21:59Z","timestamp":1787923319182,"version":"build-2784847793"},"update-to":[{"DOI":"10.12688\/wellcomeopenres.21010.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000}},{"DOI":"10.12688\/wellcomeopenres.21010.2","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000}},{"DOI":"10.12688\/wellcomeopenres.21010.3","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000}}],"reference-count":27,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Welcome","award":["227486"],"award-info":[{"award-number":["227486"]}]}],"content-domain":{"domain":["wellcomeopenresearch.org"],"crossmark-restriction":false},"short-container-title":["Wellcome Open Res"],"abstract":"<ns5:p>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.<\/ns5:p>","DOI":"10.12688\/wellcomeopenres.21010.4","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T10:15:17Z","timestamp":1741342517000},"page":"234","update-policy":"https:\/\/doi.org\/10.12688\/wellcomeopenres.crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Empowering Global AMR Research Community: Interactive GIS dashboards for AMR data analysis and informed decision-making"],"prefix":"10.12688","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7933-9004","authenticated-orcid":false,"given":"Stephen 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features overview.","year":"2024"}],"container-title":["Wellcome Open 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status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.21956\/wellcomeopenres.23245.r82552, Md.Abdus Salam, International Islamic University Malaysia,, Kuantan, 16 May 2024, version 1, 1 approved with reservations, 1 not approved","URL":"https:\/\/wellcomeopenresearch.org\/articles\/9-234\/v1#referee-response-82552","order":0,"name":"referee-response-82552","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Stephen Opiyo<\/b>; \n<i>Posted: 25 Aug 2024<\/i>; Dear Dr. Abdus Salam,  Thank you very much for taking the time to review our manuscript. We sincerely appreciate your valuable feedback. We will respond to each of your comments thoroughly. For clarity, your comments will be highlighted in red, while our responses will be indicated in blue. Kind regards,&nbsp;  Stephen \n<b>Response to <\/b>\n<b>Md.Abdus&nbsp;Salam,&nbsp;International Islamic University Malaysia,, Kuantan, Malaysia<\/b> The article entitled \u2018Empowering Global AMR Research Community: Interactive GIS dashboards for AMR data analysis and informed decision-making\u2019 is an original, innovative, and evidence-based work. To my opinion, it is a very good work that bridges the information gap and strategies to address global AMR issue in general and Uganda in particular. The research objectives have been achieved and reflected in the results. &nbsp;Sufficient details of methods and analysis are provided to allow replication by others. &nbsp;Data sources underlying the results are available and methodologies used are reproducible. As a whole there is no issue with language but there are some typos and minor suggestions as appended below. \n<b>Reviewer:<\/b> Under \u2018Plain language summary\u2019 I suggest \u2018visualize and analyze data\u2019, instead of analyze and visualize in the.  \n<b>Response to the Reviewer:<\/b> We have change to \u2018visualize and analyze data\u2019. See line 54 of the attached manuscript. \n<b>Reviewer:<\/b> Under \u2018Abstract\u2019 I suggest to add \u2018AMR\u2019 before the word data in the 2\n<sup>nd<\/sup>&nbsp;line of \u2018Methods\u2019.  \n<b>Response to the Reviewer:<\/b> We have added \u2018AMR\u2019 before the word data.&nbsp; See line 30 of the attached manuscript. \n<b>Reviewer:<\/b> Under \u2018Introduction\u2019 A sustained increase in resistance among commonly used antibiotics in surgical wards of a Ugandan hospital from 2014 to 2018 underscores the need for ongoing monitoring, surveillance, and infection prevention and control practices.  The above sentence should be included under the first paragraph of \u2018Introduction\u2019.  \n<b>Response to the Reviewer:<\/b> We have moved \u201cA sustained increase in resistance among commonly used antibiotics in surgical wards of a Ugandan hospital from 2014 to 2018 underscores the need for ongoing monitoring, surveillance, and infection prevention and control practices.\u201d to the first paragraph.&nbsp; See lines 81 to 83 in the attached manuscript. \n<b>Reviewer:<\/b> However, accessing this data necessitated the submission of a 300-word abstract. The team submitted an abstract  I suggest to replace \u2018this\u2019 with \u2018these\u2019.  \n<b>Response to the Reviewer:<\/b> We have replaced \u2018this\u2019 with \u2018these\u2019. See line 100 of the attached manuscript. \n<b>Reviewer:<\/b> Under \u2018Results\u2019 (2\n<sup>nd<\/sup>&nbsp;paragraph) healthcare professionals to analyze and explore data I suggest to change as to explore and analyze for the above sentence.  \n<b>Response to the Reviewer:<\/b> We have change to \u2018explore and analyze \u2018. See line 180 of the attached manuscript. \n<b>Reviewer<\/b> The limitation(s) has not been mentioned in the \u2018Conclusion\u2019, so I checked \u2018partly\u2019 for question #6.  \n<b>Response to the Reviewer:<\/b> We have added the paragraph below about the limitations of the study to the Conclusion. See lines 236 to 242 of the attached manuscript. \n<b>Limitations:<\/b> \u201cHowever, the research confronts notable challenges, particularly the scarcity of comprehensive AMR data in Africa. Limited sample sizes, such as those in Uganda and Kenya, may not accurately reflect the full spectrum of resistance patterns. Furthermore, inconsistent data collection and reporting across Africa reduce the reliability and generalizability of dashboards, particularly for Uganda and Kenya. The study underscores the necessity for standardized data collection, enhanced laboratory capabilities, and increased investment in healthcare infrastructure to fully leverage dashboards in the fight against AMR in Africa\u201d. &nbsp;","URL":"https:\/\/wellcomeopenresearch.org\/articles\/9-234\/v4#referee-comment-5782","order":1,"name":"referee-comment-5782","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.23245.r88572, Dov Stekel, University of Johannesburg, Auckland Park, Johannesburg, 08 Aug 2024, version 1, 1 approved with reservations, 1 not approved","URL":"https:\/\/wellcomeopenresearch.org\/articles\/9-234\/v1#referee-response-88572","order":2,"name":"referee-response-88572","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Stephen Opiyo<\/b>; \n<i>Posted: 25 Aug 2024<\/i>; \n<b>Dear Dr. Stekel, <\/b>&nbsp;  Thank you very much for taking the time to review our manuscript. We sincerely appreciate your valuable feedback. We will respond to each of your comments thoroughly. For clarity, your comments will be highlighted in red, while our responses will be indicated in blue. &nbsp; Kind regards,&nbsp;  Stephen &nbsp;\n<b>Dov<\/b>\n<b>&nbsp;<\/b>\n<b>Stekel<\/b>,&nbsp;University of Johannesburg, Auckland Park, Johannesburg, South Africa;&nbsp;University of Nottingham, Nottingham, England, UK This is a very interesting report and the idea of an interactive GIS-enabled AMR tool is very good and has my support.  \n<b>Reviewer:<\/b> The most important problem however is the lack of availability of the dashboard creation code and the synthetic data from Objective 3. It is very difficult to see how this idea could be of general use without access either to the code or to the interface: indeed I would expect under FAIR principles there to be much greater availability of the methods. \n<b>Response to the Reviewer:<\/b> We have successfully developed an R Shiny dashboard for Objective 3 and uploaded the corresponding data to GitHub. However, we were unable to develop R Shiny dashboards for Objectives 1 and 2 due to the unavailability of third-party data. Additionally, we cannot provide the code for creating dashboards for Objectives 1 and 2, as they were built in the cloud using Esri\u2019s Graphical User Interface (GUI), which does not generate code for cloud-based dashboards. Nevertheless, the links to these dashboards are available at (https:\/\/patira.maps.arcgis.com\/apps\/dashboards\/d89c5b039552464f8f04103f05ac889f ) for Objective 1 and (https:\/\/patira.maps.arcgis.com\/apps\/dashboards\/a1324c58e49e4561ac9505936db7743a) for Objective 2, as indicated in lines 407 and 408 of the attached manuscript. \n<i><u>Development of R shiny dashboard for Objective 3<\/u><\/i> We developed R shiny dashboard and uploaded the app into R shiny server at http:\/\/167.71.249.72:3838\/AMR-Dashboard\/.&nbsp; This app gives same results as was produced by Esri\u2019s dashboard.&nbsp; Figures 3A and 3B in the attached manuscript explain how the dashboard operates, see lines 249 to 283.&nbsp; The dashboard provides an interactive visualization of antibiotic resistance data using the R Shiny framework. Users are welcomed with a message guiding them to select a species from a dropdown menu (default: \n<i>Staphylococcus aureus<\/i>) and download data as a CSV file. The dashboard includes various visualizations such as a Source Distribution plot, a Geographical Distribution map for Kampala, a Samples by Year bar chart (2019-2022), a Gender Distribution pie chart, and Antibiotic Resistance plots for antibiotics like Amikacin and Ampicillin. When &quot;\n<i>Klebsiella aerogenes<\/i>&quot; is selected, the dashboard updates dynamically to display visualizations specific to this species. Source distribution shows sample locations, with geographical data pinpointing cases in Lugala (Figure 3B). The Samples by Year plot indicates an increase in sample counts over time, and the Antibiotic Resistance charts reveal resistance patterns, such as full resistance to Ampicillin and susceptibility to Amikacin. Data can be exported in CSV format using the &quot;Download Results&quot; button. The \u201capp.R\u201d Shiny application is attached, and you can also find it on GitHub (refer to line 348 of the attached manuscript). The simulated dataset, \u201cKampala_data.csv,\u201d which is used to create the dashboard, is attached as well. It is also available on GitHub within the `data` folder (see lines 267-286 and line 412 of the attached manuscript). Additionally, instructions for installing and using the app are attached and can be found on GitHub (refer to lines 414 and 416 of the manuscript).  \n<b>There are a few minor comments about the way it is written:<\/b>  \n<b>Reviewer:<\/b>  Methods in Abstract: please rewrite the section to avoid the terms &quot;Objective 1&quot; etc: those terms read like a grant application or project plan and don't really fit the report of a published article.  \n<b>Response to the Reviewer<\/b> We have rewritten the Methods in the abstract as suggested. See lines 30 to 35 in the attached manuscript.  &nbsp; \n<b>Reviewer:<\/b>  Introduction first paragraph: authors refer to both UTIs and poultry systems - which is fine (if a bit abrupt). It would be helpful if the authors were to introduce the One Health framing first, because then the link between the human and agricultural aspects would be more logical.(I note that One Health is referred to in the next paragraph - what is needed is a short description of the idea in paragraph 1, with citation, and perhaps justification as to why it is especially important in sub-Saharan Africa (and lower income countries more generally&nbsp; - as implied by the reference to Nepal))  \n<b>Response to the Reviewer:<\/b>  We have introduced one-health and referenced it the manuscript.&nbsp; See lines 80 to 82 in the manuscript, and the reference in line 360.  \n<b>Reviewer:<\/b>  Introduction - penaultimate paragraph reads a bit strange - I would suggest deleting that - it is OK to reference the project abstract - but really what we are looking for is a description of the research, not the details of the Vivli data challenge.  \n<b>Response to the Reviewer:<\/b>  We have deleted the section and added the information on the abstract to lines 113 and 114 of the attached manuscript.  \n<b>Reviewer:<\/b>  In Objective 3, the phrase &quot;Specifically, let\u2019s focus&quot; is too colloquial and needs modifying.  \n<b>Response to the Reviewer:<\/b> We have modified the phrase.&nbsp; See lines 174 to 176 in the attached manuscript.","URL":"https:\/\/wellcomeopenresearch.org\/articles\/9-234\/v4#referee-comment-5783","order":3,"name":"referee-comment-5783","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.25363.r111333, Quentin Leclerc, Institut Pasteur (Ringgold ID: 27058), Paris, \u00cele-de-France, 07 Dec 2024, version 2, 1 approved with reservations, 3 not approved","URL":"https:\/\/wellcomeopenresearch.org\/articles\/9-234\/v2#referee-response-111333","order":4,"name":"referee-response-111333","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Stephen Opiyo<\/b>; \n<i>Posted: 25 Dec 2024<\/i>; Dear Dr. Quentin Leclerc, Thank you very much for taking the time to review our manuscript. We sincerely appreciate your valuable feedback. We will respond to each of your comments thoroughly.&nbsp; Kind regards,&nbsp; Stephen &nbsp; \n<b>Reviewer comments<\/b> In this article, the authors present GIS dashboards they designed using data from industry AMR monitoring systems. Such tools can be interesting to visualise AMR data, both for communication and awareness purposes. The design of the dashboards by the authors is appropriate and I have no major concerns about this. Unfortunately, the conclusions of the study are currently too strong and not supported by the analysis. While I agree that GIS dashboard can be useful, here the authors do not directly demonstrate that they empower stakeholders or allow key decisions to be made. In that sense, the \u201cImpact of the work\u201d section of the Discussion is misleading as it discusses the potential usefulness of dashboard as if this was a certainty that had been proven in this article. Additionally, I think that further information should be provided on the datasets used in this project. Overall, I believe this article can be of general interest only if the conclusions are rephrased to appropriately discuss the potential usefulness of GIS dashboards as well as their limitations. Additionally, I think that the paper would be greatly enhanced by the authors\u2019 perspective on designing such a dashboard: which obstacles did they encounter? How have they adapted their approach? Do they have any recommendations? These questions would be of interest to the scientific community. If such changes are implemented, I would be happy to update my review. \n<b>Response to the Reviewer<\/b> \n<i><u>Datasets and dashboards<\/u><\/i> We have clarified and expanded on the datasets used for developing the dashboards: Supplementary Table 2: Dataset for the Objective 1 dashboard. Supplementary Table 3: Dataset for dashboards developed for Objective 2. Supplementary Table 4: Dataset for creating the Objective 3 dashboard. These datasets have been made publicly available for transparency and reproducibility via GitHub at https:\/\/github.com\/so13839\/Team-8952-Vivli-AMR-Competation\/tree\/main\/data. References 30 to 32 in the revised manuscript now correspond to these datasets. \n<i><u>Impact of the Work<\/u><\/i> We have revised the \u201cImpact of the Work\u201d section to provide greater emphasis on the dashboards' usability and significance: &nbsp; The GIS web-based dashboards developed for the ATLAS dataset, covering 83 countries, and the dashboards for Kenya, Uganda, and simulated data from Kampala provide essential tools for exploring and analyzing AMR data. These dashboards are designed to offer a user-friendly, interactive interface tailored to researchers, technicians, and healthcare professionals. The dashboards allow filtering and segmenting of data by key variables, such as country, species, sample sources, and antibiotic resistance profiles. For instance, the Uganda and Kenya dashboards highlight resistance patterns for \n<i>E. coli<\/i> and visualize data distributions by sample source, year, and resistance levels for antibiotics such as Ampicillin, Levofloxacin, and Imipenem. In Kampala, the simulated dataset provides a focused view, enabling users to identify areas with limited data and prioritize research. Missing data is clearly labeled as \u201cNo Information,\u201d helping to highlight gaps requiring further investigation. This interactive approach offers significant improvements over traditional spreadsheet analysis by uncovering spatial and temporal trends efficiently and enabling informed decision-making. \n<i><u>Conclusions and Limitations of the Study<\/u><\/i> We have rephrased the conclusions to emphasize the dashboards\u2019 potential while addressing their limitations: The interactive GIS dashboards developed using Esri ArcGIS and Shiny represent a significant improvement over spreadsheets, enabling efficient exploration of geographic and temporal trends in AMR data. However, several challenges were encountered. For Esri ArcGIS dashboards, these included the platform's complexity, steep learning curve, and the large dataset size, which required optimization strategies such as data aggregation to improve performance. Limited customization options and the high subscription costs of Esri tools also posed barriers, especially for smaller organizations. Shiny dashboards presented challenges such as the steep learning curve of R programming, deployment complexities, and integration with external APIs. Managing server configurations for platforms like ShinyApps.io required advanced technical skills. Additionally, the study highlights the limitations in AMR data availability, particularly in Africa. Data scarcity, limited sample sizes, and inconsistencies in data collection practices across Uganda and Kenya reduce the dashboards\u2019 reliability. To address these challenges and enhance the dashboards\u2019 effectiveness, we recommend standardized data collection practices, investment in laboratory infrastructure. These steps will ensure the dashboards\u2019 full potential is realized in combating AMR in Africa.  \n<b>Reviewer comments<\/b> \n<b>Introduction<\/b> The Introduction can be refocused on the topic of the study \u2013 for example, although I agree they are important, the concepts of One Health and antibiotic consumption in animals are not directly relevant in this work. Instead, it would be more relevant for you to introduce GIS dashboards in greater detail \u2013 why did the OHE-AMURS work so well? Are there any limitations? How could dashboards be useful in Nepal? By focusing on GIS dashboards before the Objectives section, the scope of the article will be much clearer for readers. \n<b>Response to the Reviewer<\/b> We have revised the manuscript by removing references to One Health and antibiotic consumption in animals. Instead, we have introduced Geographic Information Systems (GIS) and an Interactive GIS Dashboard as central components of the study. Additionally, we have addressed all the reviewer\u2019s comments in the Introduction, specifically within paragraphs 2 to 5 of the revised manuscript.  \n<b>Reviewer comments<\/b> \n<b>Objectives<\/b> It is not clear here what you mean by \u201cdescriptive statistical analysis\u201d. If this refers to the beginning of your Results section, it is a bit hard to follow, as it explains the conclusions before the results. I understand you may be describing your exact work process during the Vivli Data Challenge, but for readers this will not be clear. You could simply state here that you observed data gaps in African countries and that this motivated your three objectives.  Throughout: you used the ATLAS dataset in your analysis, so please correct any references to the data accordingly. For example, \u201cthe Vivli dataset\u201d is not accurate, since Vivli only facilitate access to the data, but do not own it. Objective 3 needs to be clarified. It is not obvious what you mean by \u201csimulated dataset\u201d here, how this may be useful, and what is the link with Kampala, Uganda. &nbsp; \n<b>Response to the Reviewer<\/b> We have removed the descriptive statistical analysis and highlighted the observation of data gaps in African countries in the revised manuscript. Additionally, the Vivli dataset has been replaced with the ATLAS dataset. Objective 3 has been rewritten as: 'To demonstrate the potential usefulness and design of a dashboard that facilitates the analysis of antimicrobial resistance (AMR) patterns in Kampala, Uganda.'&quot;  \n<b>Reviewer comments<\/b> \n<b>Methods<\/b> Further information needs to be provided on the ATLAS dataset which you used to inform the dashboards, otherwise readers cannot understand the context and generalisability of the work. For example: how were isolates collected, what is the purpose of this data, which variables does it contain, which biases could it be exposed to... This has been discussed in previous articles which I have referenced below. \n<b>Response to the Reviewer<\/b> We have incorporated a discussion of the ATLAS dataset in the revised manuscript, detailing the methods used for isolate collection and referencing previous studies that analyzed this data. Please refer to the section titled Pfizer ATLAS Dataset on page 4 of the revised manuscript.  \n<b>Reviewer comments<\/b> \n<b>Results<\/b> Here already, you should adjust the phrasing on the usefulness of GIS dashboards. Here, since you do not directly demonstrate the usefulness of dashboards, you can only discuss their potential value. For example, \u201cis a valuable tool for researchers, policymakers and healthcare professionals\u201d is not supported by the analysis. \n<b>Response to the Reviewer<\/b> We have carefully revised the section as per your feedback. The updated section can now be found on page 6 of the revised manuscript. This revision incorporates the necessary clarifications and aligns with the overarching goals of the study. We believe these changes enhance the manuscript's clarity and relevance. If additional adjustments or further clarifications are required, we are happy to address them. &nbsp;  \n<b>Reviewer comments<\/b> In Objective 2, it is not clear what you mean by \u201ccomparative analysis and focused investigations\u201d. &nbsp; \n<b>Response to the Reviewer<\/b> We have carefully addressed your comment and have rewritten and clarified the content in Objective 2, which can now be found on page 6 of the revised manuscript.  \n<b>Reviewer comments<\/b> For Objective 3 here again it is very unclear what the \u201csimulated dataset\u201d corresponds to and how it was generated. If it is simulated, then interpreting susceptibility results for&nbsp;\n<i>K. aerogenes<\/i>&nbsp;does not make sense. If the point here is to demonstrate the potential usefulness and design of a dashboard using this type of data, then it should be presented as such. &nbsp; \n<b>Response to the Reviewer<\/b> We have revised the objective to clarify that the simulated dataset is intended to demonstrate how the dashboard could be applied to Kampala, Uganda. The Shiny dashboard created using this simulated data is for illustrative purposes only. The primary intention is to showcase the functionality and potential application of the dashboard. We have included this clarification in the revised manuscript, which can be found on page 7.  \n<b>Reviewer comments<\/b> \n<b>Discussion<\/b> A major counter-argument to the use of GIS dashboards is that researchers interested in in-depth analysis would simply obtain the underlying data themselves, rather than use the dashboard. In that sense, you should better discuss the usefulness of dashboards: to whom and how would they be useful exactly? Do you have any examples of dashboards being used in analyses? Are there any previous studies which discussed the potential benefits of GIS dashboards for AMR surveillance? (or for any other surveillance) &nbsp; \n<b>Response to the Reviewer<\/b> In response to your suggestion, we have emphasized the advantages of using dashboards over spreadsheets, particularly in the context of Objectives 1 to 3. Dashboards offer enhanced data visualization, real-time updates, and more intuitive decision-making compared to traditional spreadsheet approaches. For example, Kenya\u2019s One Health AMR Surveillance System (OHAMRS) integrates human and animal health data to visualize AMR patterns across various sectors, making it easier to detect and respond to emerging threats. Similarly, Switzerland\u2019s Swiss Pathogen Surveillance Platform (SPSP) leverages genomics and metadata for effective outbreak monitoring and data sharing. These case studies demonstrate the value of dashboards in surveillance and their ability to improve the efficiency and accessibility of complex data sets. References 15 and 16 in the revised manuscript have been updated to reflect these examples.  \n<b>Reviewer comments<\/b> As mentioned previously, there are several statements in your Discussion which are not supported by your analysis, such as \u201cThe dashboard acts as a centralized hub, aggregating and organizing these datasets for easy access and analysis.\u201d which is not the case here since (if I understood correctly) you only used the ATLAS dataset; \u201ccomplex queries\u201d are not demonstrated since you present only filtering commands; \u201cexamine the impact of different policies or interventions in specific countries, evaluate the effectiveness of existing strategies\u201d policies and interventions are not implemented in the dashboard; \u201chealthcare outcomes\u201d are not included in the dashboard. &nbsp; \n<b>Response to the Reviewer<\/b> We have revised the \n<b>Impact of the Work<\/b> and \n<b>Conclusions sections<\/b> to better reflect the findings of our study. The updated content can be found on pages 9 to 10 of the revised manuscript. We believe these revisions provide a clearer and more accurate representation of the implications of our results.","URL":"https:\/\/wellcomeopenresearch.org\/articles\/9-234\/v4#referee-comment-5985","order":5,"name":"referee-comment-5985","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.26371.r120298, Liam P Shaw, University of Oxford, Oxford, England, 12 Mar 2025, version 4, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/9-234\/v4#referee-response-120298","order":6,"name":"referee-response-120298","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.26371.r120300, Simon Reid, The University of Queensland, Saint Lucia, Queensland, 15 Mar 2025, version 4, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/9-234\/v4#referee-response-120300","order":7,"name":"referee-response-120300","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This work was supported by Wellcome [227486]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":8,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}