ArticleBMC global and public health2025
The future of pandemic modeling in support of decision making: lessons learned from COVID-19.
Article in BMC global and public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Simulation modeling for the design and evaluation of HIV drug resistance interventions: a systematic review of literature.BMC public health · 2026Pooled it
- Advances in Contact Tracing: A Bayesian Framework to Improve Network and Transmission Models.Journal of molecular evolution · 2026Review
- Epydemix: An open-source Python package for epidemic modeling with integrated approximate Bayesian calibration.PLoS computational biology · 2025Article
- Assessing Public Health Capacity for Infectious Disease Modeling: A Qualitative Study of State and Local Agencies.International journal of environmental research and public health · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The devastating global impacts of the COVID-19 pandemic are a stark reminder of the need for proactive and effective pandemic response. Disease modeling and forecasting are key in this response, as they enable forward-looking assessment and strategic planning. Via 85 interviews spanning 14 countries with disease modelers and those they support, conducted amid the COVID-19 pandemic response, we offer a qualitative overview of challenges faced, lessons learned, and readiness for future pandemics. The interviewees highlighted several key challenges and considerations in forecasting, particularly emphasizing the complications introduced by human behavior and various data-related issues (including data availability, quality, and standardization). They underscored the importance of effective communication among those who create models, those who make decisions based on these models, and the general public. Additionally, they pointed out the necessity for addressing global equity, debated the merits of centralized versus decentralized responses to crises, and stressed the need for establishing measures for sustainable preparedness. Their verdicts on future pandemic readiness were mixed, with only 43% of respondents saying we are better prepared for a future pandemic. We conclude by providing our vision for how modeling can and should look in the context of a successful pandemic response, in light of the insights gleaned via the interview process. These interviews and their synthesis offer crucial perspectives to shape future responses and preparedness for global health crises.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.