VisEPS: a visual explorer of parameter spaces for networked models | TU Wien

Information

Publication Type: Journal Paper (without talk)
Workgroup(s)/Project(s): not specified
Date: December 2025
DOI: 10.1007/s12650-025-01093-2
ISSN: 1875-8975
Journal: Journal of Visualization
Pages: 15
Publisher: SPRINGER
Keywords: Interactive visual exploration, Networked simulation models, Scalable visual parameter tuning, Visual model parameter fitting

Abstract

Simulations of complex social systems, such as those represented by epidemiological models, have been very useful in supporting decision makers during the last pandemic. These models generally comprise a high number of parameters, which makes it hard to identify the values that best reproduce the empirical data. Furthermore, different combinations of parameters may achieve a good fit, which renders an automatic solution ill-suited to the task. A human expert is required to make the final decisions about the optimal parameter values. We present VisEPS (Visual Explorer of Parameter Spaces), a framework for visually analyzing the effects of a very large set of parameters, with the aim of fitting a geographically explicit networked model to data obtained during the COVID-19 pandemic. We use a networked extension of a susceptible-infected-recovered (SIR) model to reproduce the epidemic dynamics in the city of Buenos Aires and its neighboring interconnected districts. We overlay binned scatterplots on a map, which facilitates the visual identification of each district and its connections. To further explore the model’s performance against data, additional views, such as parallel coordinates and histograms, along with drill-down mechanisms, have been incorporated. Finally, a use case is described in which the level of connectivity between districts is included in the analysis. The identification of suitable parameter ranges is facilitated by an iterative and incremental process, whereby new sets of simulations are incrementally requested, guided by interactive visual inspections. This permits the exploration of a parameter space that would otherwise be impossible to fully explore.

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BibTeX

@article{lanzarotti-2025-viseps,
  title =      "VisEPS: a visual explorer of parameter spaces for networked
               models",
  author =     "Esteban Lanzarotti and Kresimir Matkovic and Ezequiel
               Pecker-Marcosig and Eduard Gr\"{o}ller and Rodrigo Castro",
  year =       "2025",
  abstract =   "Simulations of complex social systems, such as those
               represented by epidemiological models, have been very useful
               in supporting decision makers during the last pandemic.
               These models generally comprise a high number of parameters,
               which makes it hard to identify the values that best
               reproduce the empirical data. Furthermore, different
               combinations of parameters may achieve a good fit, which
               renders an automatic solution ill-suited to the task. A
               human expert is required to make the final decisions about
               the optimal parameter values. We present VisEPS (Visual
               Explorer of Parameter Spaces), a framework for visually
               analyzing the effects of a very large set of parameters,
               with the aim of fitting a geographically explicit networked
               model to data obtained during the COVID-19 pandemic. We use
               a networked extension of a susceptible-infected-recovered
               (SIR) model to reproduce the epidemic dynamics in the city
               of Buenos Aires and its neighboring interconnected
               districts. We overlay binned scatterplots on a map, which
               facilitates the visual identification of each district and
               its connections. To further explore the model’s
               performance against data, additional views, such as parallel
               coordinates and histograms, along with drill-down
               mechanisms, have been incorporated. Finally, a use case is
               described in which the level of connectivity between
               districts is included in the analysis. The identification of
               suitable parameter ranges is facilitated by an iterative and
               incremental process, whereby new sets of simulations are
               incrementally requested, guided by interactive visual
               inspections. This permits the exploration of a parameter
               space that would otherwise be impossible to fully explore.",
  month =      dec,
  doi =        "10.1007/s12650-025-01093-2",
  issn =       "1875-8975",
  journal =    "Journal of Visualization",
  pages =      "15",
  publisher =  "SPRINGER",
  keywords =   "Interactive visual exploration, Networked simulation models,
               Scalable visual parameter tuning, Visual model parameter
               fitting",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2025/lanzarotti-2025-viseps/",
}