Evidence map›Paper›PMID 40128901›Full record

ArticleBMC global and public health2025

The future of pandemic modeling in support of decision making: lessons learned from COVID-19.

Kelly R Moran, Tammie Lopez, Sara Y Del Valle

Abstract readLetter
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Kelly R MoranStatistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM, USA. krmoran@lanl.gov.
Tammie LopezGenomics and Bioanalytics Group, Los Alamos National Laboratory, Los Alamos, NM, USA.
Sara Y Del ValleInformation Systems and Modeling Group, Los Alamos National Laboratory, Los Alamos, NM, USA.

Funding

Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease ActivityR01GM130668 · NIGMS · NORTHEASTERN UNIVERSITY · PI SANTILLANA, MAURICIO · 2018 to 2022
$1.9M
Laboratory Directed Research and Development 20240066DRNational Institutes of General Medical Sciences R01GM130668-01NIGMS NIH HHS R01 GM130668
6 · The paper itself

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.

Indexed as

COVID-19Decision makingLessons learnedModelingPandemic responsePerspectives

Identifiers

PMID40128901
PMCPMC11934449

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.