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

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 sus

SUBMITTER: Nevin J 

PROVIDER: S-EPMC8672192 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature