{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T07:07:17Z","timestamp":1771830437890,"version":"3.50.1"},"reference-count":16,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p>During the COVID-19 pandemic, policymakers and business leaders had to rapidly make consequential decisions, and researchers rushed to provide useful information, making use of data, infectious disease models, and public health knowledge. Our study surveyed 112 individuals engaged in COVID-19 response in the US, including data collectors, modelers, and users of these tools to determine how useful different data-driven tools were for informing response work, the most impactful challenges, and the most promising opportunities for future investment. Respondents overwhelmingly found data, models, and collaborations with researchers to be useful. The most impactful challenges, and also the most promising areas for future investment, were in data availability and quality. Respondents wanted higher quality data, more granular data, and access to a variety of data types. The second most influential challenge was insufficient human resources, with public health institutions being particularly under-resourced. Academics also played a valuable role, despite incentives that often conflict with real-time response work. Our survey results also highlight the importance and challenges of translational work, including shortcomings in science communication and difficulty navigating political influences. This study provides concrete evidence of the value of data and modeling tools for epidemic response and outlines priorities for future investment.<\/jats:p>","DOI":"10.3389\/fpubh.2026.1718094","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T06:31:15Z","timestamp":1771828275000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Data generation and modeling during COVID-19: utility, barriers, and priorities for future investments in public health response"],"prefix":"10.3389","volume":"14","author":[{"given":"Kristen","family":"Nixon","sequence":"first","affiliation":[{"name":"Department of Civil and Systems Engineering, Johns Hopkins University","place":["Baltimore, MD, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaun","family":"Truelove","sequence":"additional","affiliation":[{"name":"Department of International Health, Johns Hopkins Bloomberg School of Public Health","place":["Baltimore, MD, United States"]},{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health","place":["Baltimore, MD, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lauren","family":"Gardner","sequence":"additional","affiliation":[{"name":"Department of Civil and Systems Engineering, Johns Hopkins University","place":["Baltimore, MD, United States"]},{"name":"Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health","place":["Baltimore, MD, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1038\/s41597-022-01517-w","article-title":"The United States COVID-19 forecast hub dataset","volume":"9","author":"Cramer","year":"2022","journal-title":"Sci Data"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"100705","DOI":"10.1016\/j.epidem.2023.100705","article-title":"Public health impact of the U.S. scenario modeling hub","volume":"44","author":"Borchering","year":"2023","journal-title":"Epidemics"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"100738","DOI":"10.1016\/j.epidem.2023.100738","article-title":"The US COVID-19 and influenza scenario Modeling hubs: delivering long-term projections to guide policy","volume":"46","author":"Loo","year":"2024","journal-title":"Epidemics"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1177\/0272989X21990391","article-title":"The use and misuse of mathematical modeling for infectious disease policymaking: lessons for the COVID-19 pandemic","volume":"41","author":"James","year":"2021","journal-title":"Med Decis Mak"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1177\/1403494821997227","article-title":"The challenges facing evidence-based decision making in the initial response to COVID-19","volume":"49","author":"Rubin","year":"2021","journal-title":"Scand J Public Health"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"20210013","DOI":"10.1098\/rsfs.2021.0013","article-title":"Disease transmission and control modelling at the science-policy interface","volume":"11","author":"McCabe","year":"2021","journal-title":"Interface Focus"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"e008268","DOI":"10.1136\/bmjgh-2021-008268","article-title":"Challenges to evidence-informed decision-making in the context of pandemics: qualitative study of COVID-19 policy advisor perspectives","volume":"7","author":"Vickery","year":"2022","journal-title":"BMJ Glob Health"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1332\/174426420X15808913064302","article-title":"To what extent does evidence support decision making during infectious disease outbreaks? 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