{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:08:56Z","timestamp":1780913336231,"version":"3.54.1"},"update-to":[{"DOI":"10.12688\/wellcomeopenres.24776.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000}}],"reference-count":13,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["226052"],"award-info":[{"award-number":["226052"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["228186"],"award-info":[{"award-number":["228186"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["225288"],"award-info":[{"award-number":["225288"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Union's Horizon Europe programme projects MOOD and E4Warning","award":["874850;101086640"],"award-info":[{"award-number":["874850;101086640"]}]},{"name":"Medical Research Foundation","award":["MRF-RG-ICCH-2022-100069"],"award-info":[{"award-number":["MRF-RG-ICCH-2022-100069"]}]},{"DOI":"10.13039\/100014013","name":"United Kingdom Research and Innovation","doi-asserted-by":"crossref","award":["APP8583"],"award-info":[{"award-number":["APP8583"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["wellcomeopenresearch.org"],"crossmark-restriction":false},"short-container-title":["Wellcome Open Res"],"abstract":"<ns5:p>Background Early-phase data during an epidemic are often heterogeneous and difficult to integrate across systems, therefore a need for standard tools and reporting guidelines to facilitate timely and reliable data collection. The Global.health team have developed a data schema for the ingestion of epidemic data, allowing interoperability where data curated to this schema are readily ingested into existing systems for analysis. This paper describes the definition of \u2018core data\u2019 within the Global.health schema to focus data collection on the most relevant and available data to inform epidemic response during the first 100\u00a0days of an outbreak. Methods We used expert consultation and a structured literature review to identify key epidemiological questions and parameters that must be addressed during the first 100\u00a0days of an outbreak. Relevant digital toolkits and reporting frameworks were reviewed, and minimum data variables required for parameter estimation were identified. These variables were mapped to the existing Global.health schema and assessed for availability in early outbreak data from four recent epidemics. Variables were categorized by availability and those with sufficient early availability were retained in a proposed core schema. Data formats were harmonized with WHO Epi Core, T0 and T1 toolkits to enhance interoperability. A complementary modular schema was defined to capture pathogen-specific variables. Results The literature review yielded 78 key epidemiological parameters relevant to early outbreak assessment, organized into eleven categories. Analysis of variable availability in early outbreak datasets showed that 42 of 140 variables in the existing Global.health schema were consistently available and suitable for inclusion in a core early-epidemic schema. Variables related to demographics, case status, symptom reporting, confirmation dates, outcomes, and exposure history were frequently available, while vaccination history, detailed treatment data, and certain clinical variables were less consistently reported. The resulting core schema comprises 42 interoperable variables across seven domains and aligns with WHO data standards and controlled terminologies. Conclusions Standardized, interoperable data capture during the early phase of epidemics is essential to enable timely estimation of key epidemiological parameters and to inform response strategies. The Global.health core schema provides a minimum, evidence-informed dataset for early outbreak investigation while maintaining compatibility with WHO reporting standards. By prioritizing variables that are both epidemiologically critical and realistically available in early data streams, this framework supports improved data harmonization, analysis, and decision-making during the first 100\u00a0days of an epidemic.<\/ns5:p>","DOI":"10.12688\/wellcomeopenres.24776.2","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:04:09Z","timestamp":1778216649000},"page":"524","update-policy":"https:\/\/doi.org\/10.12688\/wellcomeopenres.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Unified framework for the ingestion of early epidemic data for downstream data analytics"],"prefix":"10.12688","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4285-2255","authenticated-orcid":false,"given":"Everlyn","family":"Kamau","sequence":"first","affiliation":[{"name":"Francis I. Proctor Foundation San Francisco California University of California San Francisco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sadie","family":"Kelly","sequence":"additional","affiliation":[{"name":"Department of Biology Oxford England University of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhruv","family":"Darji","sequence":"additional","affiliation":[{"name":"Nuffield Department of Medicine Oxford England University of Oxford"},{"name":"Pandemic Sciences Institute Oxford England University of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amrish Y.","family":"Baidjoe","sequence":"additional","affiliation":[{"name":"Medecins Sans Frontieres Brussels Operational Centre Brussels (OCB)"},{"name":"M\u00e9decins Sans Fronti\u00e8res Luxembourg Luxembourg Operational Research Unit (LuxOR)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John S.","family":"Brownstein","sequence":"additional","affiliation":[{"name":"Computational Epidemiology Lab Boston Boston Children\u2019s Hospital"},{"name":"Boston Massachusetts Harvard Medical School"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1849-1886","authenticated-orcid":false,"given":"Finlay","family":"Campbell","sequence":"additional","affiliation":[{"name":"Berlin WHO Hub for Pandemic and Epidemic Intelligence"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4420-0656","authenticated-orcid":false,"given":"Abhishek","family":"Dasgupta","sequence":"additional","affiliation":[{"name":"Department of Biology Oxford England University of Oxford"},{"name":"Research Software Engineering Group Oxford England University of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie-Am\u00e9lie","family":"Degail","sequence":"additional","affiliation":[{"name":"Geneva World Health Organization"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasiia","family":"Demidova","sequence":"additional","affiliation":[{"name":"Limassol Independent Researcher"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Ferretti","sequence":"additional","affiliation":[{"name":"Nuffield Department of Medicine Oxford England University of Oxford"},{"name":"Pandemic Sciences Institute Oxford England University of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8927-3432","authenticated-orcid":false,"given":"Aimee","family":"Han","sequence":"additional","affiliation":[{"name":"Computational Epidemiology Lab Boston Boston Children\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen Leshan","family":"Koyie","sequence":"additional","affiliation":[{"name":"Geneva World Health Organization"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patricia Ndumbi","family":"Ngamala","sequence":"additional","affiliation":[{"name":"Geneva World Health Organization"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier Le","family":"Polain","sequence":"additional","affiliation":[{"name":"Geneva World Health Organization"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3948-0756","authenticated-orcid":false,"given":"Amanda","family":"Rojek","sequence":"additional","affiliation":[{"name":"Nuffield Department of Medicine Oxford England University of Oxford"},{"name":"Pandemic Sciences Institute Oxford England University of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5694-8392","authenticated-orcid":false,"given":"Jacquelin","family":"Sauer","sequence":"additional","affiliation":[{"name":"Department of Community Health Sciences Boston Massachusetts Boston University School of Public Health"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5716-2770","authenticated-orcid":false,"given":"Samuel V.","family":"Scarpino","sequence":"additional","affiliation":[{"name":"Institute for Experiential AI Boston Massachusetts Northeastern University"},{"name":"Department of Public Health and Health Sciences Boston Northeastern University"},{"name":"Santa Fe New Mexico Santa Fe Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kara","family":"Sewalk","sequence":"additional","affiliation":[{"name":"Computational Epidemiology Lab Boston Boston Children\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juliana","family":"Sopko","sequence":"additional","affiliation":[{"name":"Computational Epidemiology Lab Boston Boston Children\u2019s Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2879-8678","authenticated-orcid":false,"given":"Stanislaw","family":"Zakrzewski","sequence":"additional","affiliation":[{"name":"Lodz Technical University of Lodz"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4168-1960","authenticated-orcid":false,"given":"Laura","family":"Merson","sequence":"additional","affiliation":[{"name":"Pandemic Sciences Institute Oxford England University of Oxford"},{"name":"Dakar Institut Pasteur de Dakar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8838-7147","authenticated-orcid":false,"given":"Moritz U. G.","family":"Kraemer","sequence":"additional","affiliation":[{"name":"Department of Biology Oxford England University of Oxford"},{"name":"Pandemic Sciences Institute Oxford England University of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1884-1901","DOI":"10.1016\/S0140-6736(15)60858-3","article-title":"Global health security: the wider lessons from the west African Ebola virus disease epidemic.","volume":"385","author":"D Heymann","year":"2015","journal-title":"Lancet."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"141-146","DOI":"10.1016\/j.puhe.2023.10.008","article-title":"The state of integrated disease surveillance in seven countries: a synthesis report.","volume":"225","author":"A Lee","year":"2023","journal-title":"Public Health."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.3390\/ijerph20021473","article-title":"Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action.","volume":"20","author":"A Quiroga Gutierrez","year":"2023","journal-title":"Int J Environ Res Public Health."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"336","DOI":"10.3389\/fpubh.2018.00336","article-title":"Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards.","volume":"6","author":"G Fairchild","year":"2018","journal-title":"Front Public Health."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"100215","DOI":"10.1016\/j.lanepe.2021.100215","article-title":"Sharing, synthesis and sustainability of data analysis for epidemic preparedness in Europe.","volume":"9","author":"A Kucharski","year":"2021","journal-title":"Lancet Reg Health Eur."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"100382","DOI":"10.1016\/j.infpip.2024.100382","article-title":"Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.","volume":"6","author":"D Fallatah","year":"2024","journal-title":"Infect Prev Pract."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s12992-018-0415-0","article-title":"Sharing public health data and information across borders: lessons from Southeast Asia.","volume":"14","author":"M Liverani","year":"2018","journal-title":"Global Health."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1038\/s41597-020-0448-0","article-title":"Epidemiological data from the COVID-19 outbreak, real-time case information.","volume":"7","author":"B Xu","year":"2020","journal-title":"Sci Data."},{"key":"ref18","doi-asserted-by":"publisher","article-title":"Dryad: Unified framework for the ingestion of early epidemic data for downstream data analytics. Dataset.","author":"E Kamau","DOI":"10.5061\/dryad.9s4mw6mvh"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"2269","DOI":"10.1186\/s12889-021-12206-5","article-title":"Data collection for outbreak investigations: process for defining a minimal data set using a Delphi approach.","volume":"21","author":"A Perrocheau","year":"2021","journal-title":"BMC Public Health."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"20160371","DOI":"10.1098\/rstb.2016.0371","article-title":"Key data for outbreak evaluation: building on the Ebola experience.","volume":"372","author":"A Cori","year":"2017","journal-title":"Philos Trans R Soc Lond B Biol Sci."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"9-10","DOI":"10.1038\/s43588-020-00015-6","article-title":"Data curation during a pandemic and lessons learned from COVID-19.","volume":"1","author":"M Kraemer","year":"2021","journal-title":"Nat Comput Sci."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"r409","DOI":"10.1136\/bmj.r409","article-title":"Medical journals should use the term \u201cpublic health and social measures\u201d.","volume":"388","author":"A Majeed","year":"2025","journal-title":"BMJ."}],"container-title":["Wellcome Open Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v2\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v2\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v2\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T09:29:09Z","timestamp":1780910949000},"score":1,"resource":{"primary":{"URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":13,"URL":"https:\/\/doi.org\/10.12688\/wellcomeopenres.24776.2","relation":{"has-version":[{"id-type":"doi","id":"10.12688\/wellcomeopenres.24776.1","asserted-by":"subject"}],"has-review":[{"id-type":"doi","id":"10.21956\/wellcomeopenres.29012.r155933","asserted-by":"subject"},{"id-type":"doi","id":"10.21956\/wellcomeopenres.29012.r155933","asserted-by":"object"}]},"ISSN":["2398-502X"],"issn-type":[{"value":"2398-502X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]},"assertion":[{"value":"Indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v2#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.21956\/wellcomeopenres.27296.r135848, Juan Torres Mungu\u00eda, Georg-August-Universit\u00e4t G\u00f6ttingen, G\u00f6ttingen, 11 Nov 2025, version 1, 2 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v1#referee-response-135848","order":0,"name":"referee-response-135848","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Everlyn Kamau<\/b>; \n<i>Posted: 21 Mar 2026<\/i>; \n<b><u>Reviewer 1<\/u><\/b> Dear Editorial Team, dear authors,  Thank you very much for the opportunity to review the paper entitled &quot;Unified framework for the ingestion of early epidemic data for downstream data analytics.&quot; I would like to sincerely commend the authors for their effort and dedication. The topic addressed is highly relevant and impactful.  Overall, the paper presents a proposal for the collection of data during the early phase of a disease outbreak by establishing a unified schema that captures the most relevant indicators for decision-making.&nbsp;I believe the manuscript is suitable for indexing after a medium revision. In particular, I have the following comments: &nbsp; \n<b>We thank the reviewer for their positive assessment of our work.<\/b> Abstract 1. The goal of the paper is not mentioned. Please add it in the background subsection. \n<b>Thank you. We have added the goal of the paper in the abstract and background section. <\/b>&nbsp; Methods 2. The description of the methods could be improved. Briefly describe Figure 1, indicating that it represents a methodology based on different steps. \n<b>Thank you. The methods section has been reorganized and rewritten for clarity. Figure 1 has been revised and described for clarity.<\/b> 3. The indication that the early phase refers to the first 100 days should be introduced earlier in the text, ideally at the first instance it is mentioned, to ensure clarity for the reader. &nbsp; \n<b>Thank you. This has been clarified by moving the introduction of this term earlier in the introduction section and defining it at its first instance.<\/b>  Introduction 4. Please add a paragraph about the impact of diseases and the relevance of studying them. For instance, include the number of deaths from COVID-19 using data from the WHO COVID-19 dashboard or the number of disease outbreaks worldwide from this paper: https:\/\/doi.org\/10.1038\/s41597-022-01797-2 \n<b>Thank you. While useful information for any health-related research, the authors do not consider the additional value of such information in the context of the current work.<\/b> &nbsp; 5. Define the concept of data interoperability.\n<b> Thank you. Data interoperability implies the seamless access, exchange, integration, and cooperative of use of data in different systems, applications, and organizations in a coordinated way, ensuring meaning, integrity, and usefulness are maintained. This definition has been added to improve the introductory section<\/b>. 6. Add a paragraph describing the goal of the paper and its structure.\n<b> Thank you. The last paragraph in the introduction now describes the goal and structure of the paper.<\/b> &nbsp; Methods 7.&nbsp;Figure 1 (Conceptual framework of this study) can be improved by presenting it as a logical pipeline, numbering and naming each stage (e.g., Stage 1: Identification of relevant variables, Stage 2: Identification of the minimum set of indicators, etc.). Then, describe these stages in the text using exactly the same names as in Figure 1. \n<b>Thank you. Figure 1 has been updated to improve clarity per your suggestions.<\/b>  Results 8. Elaborate the results according to the stages defined in the Methods section. Follow the same structure and add a framework showing the outputs for each stage and step. \n<b>Thank you. The results section has been restructured to match the sections of the methods. Outputs have been detailed in each Table to show the outputs of the steps.<\/b> 9. In the paragraph &quot;Literature review of key parameters to be addressed during the first 100 days of an epidemic&quot;, the authors use both &quot;35&quot; and &quot;Thirty-five&quot;. Please standardize the format: use numerals for numbers 10 and above, and words for numbers zero through nine. \n<b>Thank you. Use of words and figures for numbers has been standardised.<\/b> 10. In the paragraph &quot;Global.health data schema core variables&quot;, the authors use inconsistent formatting for category names (e.g., Laboratory Information vs. Medical history). Please standardize the formatting. \n<b>This has now been addressed - thank you.<\/b> 11. Include more details about the Mpox data ingestion in the Democratic Republic of Congo. What was learned from that experience? Please add a paragraph discussing the practical feasibility. \n<b>Thank you. That information is beyond the scope of this paper and is under consideration for publication in a separate work. We removed mention of specific outbreaks.<\/b>   Discussion 12. The authors mention that the Global.health core schema may not be comprehensive for all pathogen groups. Please provide an example of a pathogen for which the proposed schema might be insufficient.\n<b> Thank you. We are not aware of an actual example of a pathogen for which the proposed schema might be insufficient. We have provided Disease X as an example and suggested ways in which it may require variables outside of the current schema.<\/b> 13. The manuscript mentions that language could be a barrier for non-native English speakers. How was this challenge addressed in the Democratic Republic of Congo?\n<b> Thank you. In the DRC the primary language for ingestion was French and we used translation services and large language models to translate from French to English. While we managed this situation, languages with more complex translation requirements or different characters may not be so manageable.<\/b>","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v2#referee-comment-6477","order":1,"name":"referee-comment-6477","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.27296.r140042, Geneva Tamunobarafiri Igwama, University of Akron, Akron, Ohio, 13 Jan 2026, version 1, 2 approved with reservations","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v1#referee-response-140042","order":2,"name":"referee-response-140042","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Everlyn Kamau<\/b>; \n<i>Posted: 21 Mar 2026<\/i>; \n<b><u>Reviewer 2<\/u><\/b> This manuscript discusses a framework for gathering and standardizing early epidemic data to assist with epidemiological analysis. The authors introduce the Global. health data schema, which aims to enhance the compatibility and usability of varied outbreak data. They evaluate the framework against World Health Organization (WHO) tools and conduct a literature review to identify key epidemiological parameters relevant in the initial 100 days of an epidemic, alongside expert consultations and reviews of data from recent epidemics like Ebola, COVID-19, Mpox, and Marburg.&nbsp;  The authors compile 78 essential epidemiological parameters into 11 categories and define the minimum data necessary to estimate these parameters. They refine the Global. health schema into a &quot;core&quot; schema and adaptable modular components. The paper emphasizes that better standardization and prioritization of early outbreak data can improve real-time data analysis and public health decisions.&nbsp;  The manuscript presents relevant insights into public health, aligning well with international guidelines and demonstrating solid methodology through a clear workflow. It offers a practical, openly available schema already in use in some field settings. However, there are concerns regarding the lack of detail in the expert consultation process, which may hinder reproducibility. The literature review could benefit from improved rigor, and availability assessments are noted to be somewhat subjective.&nbsp; \n<b>Thank you. The entire methods section has been rewritten to add detail, improve reproducibility and insure clarity of process.<\/b> Major comments that require attention include improving transparency and rigor in expert selection, clarifying the literature review methods, and providing a validation example for the framework's practical benefits. Minor recommendations suggest addressing geographic disparities in data availability, discussing language accessibility, and improving the clarity of data tables.&nbsp; T\n<b>hank you. Disparities in data availability: - This was not a focus of this paper. We were interested in assessing the likelihood of data variables being present in the Global.health data sources. While geographic disparities may play a role in data availability, addressing that is beyond the scope of this work. We have highlighted this in the discussion for future work. Language accessibility: - While the schema is currently in English, accessibility in other languages is possible but not within scope of our current work. Clarity of data tables: - Thank you for identifying this lack of clarity. We have rewritten the results section to match the rewritten methods section. Tables are linked to specific stages of results to improve clarity of process.<\/b>  The manuscript is a significant contribution to epidemic preparedness, but it needs major revisions for methodological transparency and validation before full acceptance. \n<b>Thank you. We have added detail to the specific issues raised and look forward to further feedback.<\/b>","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v2#referee-comment-6478","order":3,"name":"referee-comment-6478","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.21956\/wellcomeopenres.29012.r155933, Lilia Perfeito, LIP\u2013Laboratory for Instrumentation and Experimental Particle Physics, Lisboa, 08 Jun 2026, version 2, indexed","URL":"https:\/\/wellcomeopenresearch.org\/articles\/10-524\/v2#referee-response-155933","order":4,"name":"referee-response-155933","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This work was supported by Wellcome [225288; 226052; 228186 to M.U.G.K.]; M.U.G.K. acknowledges funding from The Rockefeller Foundation, Google.org, the Oxford Martin School Programmes in Pandemic Genomics & Digital Pandemic Preparedness, European Union's Horizon Europe programme projects MOOD [#874850] and E4Warning [#101086640], the John Fell Fund, a Branco Weiss Fellowship, United Kingdom Research and Innovation [#APP8583] and the Medical Research Foundation [MRF-RG-ICCH-2022-100069].\r\nThe contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the funders. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":5,"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"}]}}