Esteban Lanzarotti

@article{lanzarotti_viseps_2026,

title = {VisEPS: a visual explorer of parameter spaces for networked models},

author = {Esteban Lanzarotti and Krešimir Matković and Ezequiel Pecker-Marcosig and Eduard Gröller and Rodrigo Castro},

url = {https://doi.org/10.1007/s12650-025-01093-2},

doi = {10.1007/s12650-025-01093-2},

issn = {1875-8975},

year  = {2026},

date = {2026-01-01},

journal = {Journal of Visualization},

volume = {29},

number = {1},

pages = {153–167},

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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{lanzarotti_multi-scale_2025,

title = {A multi-scale agent-based model of aerosol-mediated indoor infections in heterogeneous scenarios},

author = {Esteban Lanzarotti and Andrea Pineda Rojas and Francisco Roslan and Leandro Groisman and Lucio Santi and Rodrigo Castro},

url = {https://doi.org/10.1080/17477778.2025.2476456},

doi = {10.1080/17477778.2025.2476456},

issn = {1747-7778},

year  = {2025},

date = {2025-01-01},

urldate = {2025-07-01},

journal = {Journal of Simulation},

volume = {0},

number = {0},

pages = {1–19},

publisher = {Taylor & Francis},

abstract = {We present a hybrid agent-based and equation-based simulation model to study airborne infection transmission events in heterogeneous indoor spaces. Agents move between different rooms, generating evolving networked social interactions. Suspended aerosols enable both direct and indirect transmission, shaping multi-scale contagion at local (room) and global (building layout) levels within a SEIR process. This approach provides a very flexible platform for dealing with environmental and population heterogeneities. Our model reproduces well-documented real-world contagion events and analytical models in the literature over a wide range of environments (hospitals, offices, restaurants, and classrooms). The model is applied to study transmission patterns in a typical school day, exploring three heterogeneity-driven scenarios: (i) varying break-time activity intensity, (ii) localized ventilation reductions, and (iii) flexible class/break durations, uncovering emerging nonlinear system-level dynamics. Our results show that neglecting heterogeneities can lead to considerable underestimation of attack rates. Such scenarios cannot be modeled with previous agent-based frameworks.},

note = {_eprint: https://doi.org/10.1080/17477778.2025.2476456},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arrar_mathematical_2021,

title = {Mathematical and Computational Initiatives from the University of Buenos Aires to Contribute to Decision-Making in the Context of COVID-19 in Argentina},

author = {Mehrnoosh Arrar and Laouen Mayal Louan Belloli and Ana Maria Bianco and Leonardo Boechi and Rodrigo Castro and Guillermo Alfredo Duran and Roberto Argentino Etchenique and Natalia Brenda Fernández and Luciana Ferrer and Diego David Garbervetsky and Rodrigo Goldsmit and Carolina Grillo Vidal and Juan Kamienkowski and Pablo Laciana and Esteban Lanzarotti and Mario Lozano and Rodrigo Maidana and Mariano Mendiluce and Sol Minoldo and Ezequiel Pecker-Marcosig and Leonardo Pepino and Armando Ezequiel Puerta and Rodrigo Quiroga and Guillermo Solovey and Marina Valdora and Mariano Zapatero},

doi = {10.52712/sciencereviews.v2i2.38},

year  = {2021},

date = {2021-01-01},

publisher = {Centro de Estudios sobre Ciencia, Desarrollo y Educación Superior},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arrar_iniciativas_2020,

title = {Iniciativas matemático computacionales desde la Universidad de Buenos Aires para contribuir a la toma de decisiones en el contexto del COVID-19},

author = {Mehrnoosh Arrar and Laouen Mayal Louan Belloli and Ana Maria Bianco and Leonardo Boechi and Rodrigo Castro and Guillermo Alfredo Duran and Roberto Argentino Etchenique and Natalia Brenda Fernández and Luciana Ferrer and Diego David Garbervetsky and Rodrigo Goldsmit and Carolina Grillo Vidal and Juan Kamienkowski and Pablo Laciana and Esteban Lanzarotti and Mario Lozano and Rodrigo Maidana and Mariano Mendiluce and Sol Minoldo and Leonardo Pepino and Ezequiel Pecker-Marcosig and Armando Ezequiel Puerta and Rodrigo Quiroga and Guillermo Solovey and Marina Valdora and Mariano Zapatero},

url = {https://ri.conicet.gov.ar/handle/11336/128121},

year  = {2020},

date = {2020-01-01},

urldate = {2025-07-01},

publisher = {Universidad de Chile},

keywords = {},

pubstate = {published},

tppubtype = {article}

}