Data generation and modeling during Covid-19
Utility, barriers, and priorities for future investments in public health response
Bibliographic Data
| ID | 22084399 |
|---|---|
| Authors | Kristen Nixon (Johns Hopkins University), Shaun Truelove (0000-0003-0538-0607, Johns Hopkins University), Lauren Gardner (0000-0001-5697-8022, Johns Hopkins University, corresponding author) |
| Year | 2026 |
| Volume | 14 |
| Pages | 1718094-1718094 |
| Publication date | 2026-02-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2026.1718094 |
| PMID | 41810285 |
| OpenAlex | W7131102163 |
| Language | EN |
| References cited | 15 |
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
Data quality · Incentive · Public health · Survey data collection · COVID-19 Digital Contact Tracing · COVID-19 epidemiological studies · Data-Driven Disease Surveillance
An interactive web-based dashboard to track Covid-19 in real time
Infectious disease surveillance needs for the United States
Challenges to evidence-informed decision-making in the context of pandemics
To what extent does evidence support decision making during infectious disease outbreaks? A scoping literature review
| Citation velocity | historical |
|---|---|
| Highly cited | No |