ArticlePLoS computational biology2023
An implementation framework to improve the transparency and reproducibility of computational models of infectious diseases.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of spatial epidemiological modeling approaches applied during the COVID-19 pandemic.BMC public health · 2026Pooled it
- Examining epidemiological models and economic analyses of typhoid conjugate vaccine: A scoping review.PLOS global public health · 2026Article
- Guidelines on reporting and assessing dynamic mathematical models of infectious diseases: a scoping review.BMC infectious diseases · 2025Article
- Calibration of transmission-dynamic infectious disease models: A scoping review and reporting framework.PLoS computational biology · 2025Article
- A Systematic Review of Spatial Epidemiological Modeling Approaches Applied During the COVID-19 Pandemic.medRxiv : the preprint server for health sciences · 2025Article
- Using Surveillance Data to Estimate Infectious Disease Burden: Opportunities and Challenges.American journal of public health · 2025Article
- The EpiFusion Analysis Framework for joint phylodynamic and epidemiological analysis of outbreak characteristics.F1000Research · 2025Article
- Understanding ecological systems using knowledge graphs: an application to highly pathogenic avian influenza.Bioinformatics advances · 2025Article
- Inter-rater reliability of the infectious disease modeling reproducibility checklist (IDMRC) as applied to COVID-19 computational modeling research.BMC infectious diseases · 2023Article
- Inter-rater reliability of the Infectious Disease Modeling Reproducibility Checklist (IDMRC) as applied to COVID-19 computational modeling research.medRxiv : the preprint server for health sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Computational models of infectious diseases have become valuable tools for research and the public health response against epidemic threats. The reproducibility of computational models has been limited, undermining the scientific process and possibly trust in modeling results and related response strategies, such as vaccination. We translated published reproducibility guidelines from a wide range of scientific disciplines into an implementation framework for improving reproducibility of infectious disease computational models. The framework comprises 22 elements that should be described, grouped into 6 categories: computational environment, analytical software, model description, model implementation, data, and experimental protocol. The framework can be used by scientific communities to develop actionable tools for sharing computational models in a reproducible way.
Indexed as
Identifiers
What OpenQuestion holds
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.