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

Research output: Book chapterConference contributionpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationEuro-Par 2021
Subtitle of host publicationParallel Processing Workshops - Euro-Par 2021 International Workshops, 2021, Revised Selected Papers
EditorsRicardo Chaves, Dora B. Heras, Aleksandar Ilic, Didem Unat, Rosa M. Badia, Andrea Bracciali, Patrick Diehl, Anshu Dubey, Oh Sangyoon, Stephen L. Scott, Laura Ricci
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031061554
Publication statusPublished - 2022
Externally publishedYes
Event27th International Conference on Parallel and Distributed Computing, Euro-Par 2021 - Virtual, Online
Duration: 30 Aug 202131 Aug 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13098 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th International Conference on Parallel and Distributed Computing, Euro-Par 2021
CityVirtual, Online


  • COVID-19
  • Epidemiological simulation
  • Heterogeneous data processing
  • Parallel tool


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