Evidence map›Paper›PMID 34950910›Full record

ArticlePatterns (New York, N.Y.)2021

The non-linear impact of data handling on network diffusion models.

James Nevin, Michael Lees, Paul Groth

Abstract read
In one paragraph

Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. The paradox of intervention: Resilience in adaptive multirole coordination networks.Proceedings of the National Academy of Sciences of the United States of America · 2025
    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.

James NevinUniversity of Amsterdam, Amsterdam, the Netherlands.
Michael LeesUniversity of Amsterdam, Amsterdam, the Netherlands.
Paul GrothUniversity of Amsterdam, Amsterdam, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many computational models rely on real-world data, and the steps required in moving from data collection, to data preparation, to model calibration, and input are becoming increasingly complex. Errors in data can lead to errors in model output that might invalidate conclusions in extreme cases. While the challenge of errors in data collection have been analyzed in the literature, here we highlight the importance of data handling in the modeling and simulation process, and how particular data handling errors can lead to errors in model output. We develop a framework for assessing the impact of potential data errors for models of spreading processes on networks, a broad class of models that capture many important real-world phenomena (e.g., epidemics, rumor spread, etc.). We focus on the susceptible-infected-removed (SIR) and Threshold models and examine how systematic errors in data handling impact the predicted spread of a virus (or information). Our results demonstrate that data handling errors can have significant impact on model conclusions especially in critical regions of a system.

Indexed as

computational modelsdata handlingdata reusediffusion modelsnetworks

Identifiers

PMID34950910
PMCPMC8672192

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.