Data Management in EpiGraph COVID-19 Epidemic Simulator

Miguel Guzmán-Merino, Christian Durán, Maria Cristina Marinescu, Concepción Delgado-Sanz, Diana Gomez-Barroso, Jesus Carretero, David E. Singh

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

1 Cita (Scopus)

Resumen

The transmission of COVID-19 through a population depends on many factors which model, incorporate, and integrate many heterogeneous data sources. The work we describe in this paper focuses on the data management aspect of EpiGraph, a scalable agent-based virus-propagation simulator. We describe the data acquisition and pre-processing tasks that are necessary to map the data to the different models implemented in EpiGraph in a way that is efficient and comprehensible. We also report on post-processing, analysis, and visualization of the outputs, tasks that are fundamental to make the simulation results useful for the final users. Our simulator captures complex interactions between social processes, virus characteristics, travel patterns, climate, vaccination, and non-pharmaceutical interventions. We end by demonstrating the entire pipeline with one evaluation for Spain for the third COVID wave starting on December 27th of 2020.

Idioma originalInglés
Título de la publicación alojadaEuro-Par 2021
Subtítulo de la publicación alojadaParallel Processing Workshops - Euro-Par 2021 International Workshops, 2021, Revised Selected Papers
EditoresRicardo Chaves, Dora B. Heras, Aleksandar Ilic, Didem Unat, Rosa M. Badia, Andrea Bracciali, Patrick Diehl, Anshu Dubey, Oh Sangyoon, Stephen L. Scott, Laura Ricci
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas267-278
Número de páginas12
ISBN (versión impresa)9783031061554
DOI
EstadoPublicada - 2022
Publicado de forma externa
Evento27th International Conference on Parallel and Distributed Computing, Euro-Par 2021 - Virtual, Online
Duración: 30 ago 202131 ago 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13098 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia27th International Conference on Parallel and Distributed Computing, Euro-Par 2021
CiudadVirtual, Online
Período30/08/2131/08/21

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