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

doi:10.1007/978-3-031-06156-1_22

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^*

^*Corresponding author for this work

Research output: Book chapter › Conference contribution › peer-review

Abstract

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 language	English
Title of host publication	Euro-Par 2021
Subtitle of host publication	Parallel Processing Workshops - Euro-Par 2021 International Workshops, 2021, Revised Selected Papers
Editors	Ricardo Chaves, Dora B. Heras, Aleksandar Ilic, Didem Unat, Rosa M. Badia, Andrea Bracciali, Patrick Diehl, Anshu Dubey, Oh Sangyoon, Stephen L. Scott, Laura Ricci
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	267-278
Number of pages	12
ISBN (Print)	9783031061554
DOIs	https://doi.org/10.1007/978-3-031-06156-1_22
Publication status	Published - 2022
Externally published	Yes
Event	27th International Conference on Parallel and Distributed Computing, Euro-Par 2021 - Virtual, Online Duration: 30 Aug 2021 → 31 Aug 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13098 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	27th International Conference on Parallel and Distributed Computing, Euro-Par 2021
City	Virtual, Online
Period	30/08/21 → 31/08/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

COVID-19
Epidemiological simulation
Heterogeneous data processing
Parallel tool

Access to Document

10.1007/978-3-031-06156-1_22

Cite this

Guzmán-Merino, M., Durán, C., Marinescu, M. C., Delgado-Sanz, C., Gomez-Barroso, D., Carretero, J., & Singh, D. E. (2022). Data Management in EpiGraph COVID-19 Epidemic Simulator. In R. Chaves, D. B. Heras, A. Ilic, D. Unat, R. M. Badia, A. Bracciali, P. Diehl, A. Dubey, O. Sangyoon, S. L. Scott, & L. Ricci (Eds.), Euro-Par 2021: Parallel Processing Workshops - Euro-Par 2021 International Workshops, 2021, Revised Selected Papers (pp. 267-278). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13098 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-06156-1_22

Guzmán-Merino, Miguel ; Durán, Christian ; Marinescu, Maria Cristina et al. / Data Management in EpiGraph COVID-19 Epidemic Simulator. Euro-Par 2021: Parallel Processing Workshops - Euro-Par 2021 International Workshops, 2021, Revised Selected Papers. editor / Ricardo Chaves ; Dora B. Heras ; Aleksandar Ilic ; Didem Unat ; Rosa M. Badia ; Andrea Bracciali ; Patrick Diehl ; Anshu Dubey ; Oh Sangyoon ; Stephen L. Scott ; Laura Ricci. Springer Science and Business Media Deutschland GmbH, 2022. pp. 267-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{29cd46e35f05498c8db1b3609f987269,

title = "Data Management in EpiGraph COVID-19 Epidemic Simulator",

abstract = "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.",

keywords = "COVID-19, Epidemiological simulation, Heterogeneous data processing, Parallel tool",

author = "Miguel Guzm{\'a}n-Merino and Christian Dur{\'a}n and Marinescu, \{Maria Cristina\} and Concepci{\'o}n Delgado-Sanz and Diana Gomez-Barroso and Jesus Carretero and Singh, \{David E.\}",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 27th International Conference on Parallel and Distributed Computing, Euro-Par 2021 ; Conference date: 30-08-2021 Through 31-08-2021",

year = "2022",

doi = "10.1007/978-3-031-06156-1\_22",

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publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Ricardo Chaves and \{B. Heras\}, Dora and Aleksandar Ilic and Didem Unat and Badia, \{Rosa M.\} and Andrea Bracciali and Patrick Diehl and Anshu Dubey and Oh Sangyoon and \{L. Scott\}, Stephen and Laura Ricci",

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Guzmán-Merino, M, Durán, C, Marinescu, MC, Delgado-Sanz, C, Gomez-Barroso, D, Carretero, J & Singh, DE 2022, Data Management in EpiGraph COVID-19 Epidemic Simulator. in R Chaves, D B. Heras, A Ilic, D Unat, RM Badia, A Bracciali, P Diehl, A Dubey, O Sangyoon, S L. Scott & L Ricci (eds), Euro-Par 2021: Parallel Processing Workshops - Euro-Par 2021 International Workshops, 2021, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13098 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 267-278, 27th International Conference on Parallel and Distributed Computing, Euro-Par 2021, Virtual, Online, 30/08/21. https://doi.org/10.1007/978-3-031-06156-1_22

Data Management in EpiGraph COVID-19 Epidemic Simulator. / Guzmán-Merino, Miguel; Durán, Christian; Marinescu, Maria Cristina et al.
Euro-Par 2021: Parallel Processing Workshops - Euro-Par 2021 International Workshops, 2021, Revised Selected Papers. ed. / Ricardo Chaves; Dora B. Heras; Aleksandar Ilic; Didem Unat; Rosa M. Badia; Andrea Bracciali; Patrick Diehl; Anshu Dubey; Oh Sangyoon; Stephen L. Scott; Laura Ricci. Springer Science and Business Media Deutschland GmbH, 2022. p. 267-278 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13098 LNCS).

Research output: Book chapter › Conference contribution › peer-review

TY - GEN

T1 - Data Management in EpiGraph COVID-19 Epidemic Simulator

AU - Guzmán-Merino, Miguel

AU - Durán, Christian

AU - Marinescu, Maria Cristina

AU - Delgado-Sanz, Concepción

AU - Gomez-Barroso, Diana

AU - Carretero, Jesus

AU - Singh, David E.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - COVID-19

KW - Epidemiological simulation

KW - Heterogeneous data processing

KW - Parallel tool

UR - https://www.scopus.com/pages/publications/85133029620

UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure_univeritat_ramon_llull&SrcAuth=WosAPI&KeyUT=WOS:000851509300022&DestLinkType=FullRecord&DestApp=WOS_CPL

U2 - 10.1007/978-3-031-06156-1_22

DO - 10.1007/978-3-031-06156-1_22

M3 - Conference contribution

AN - SCOPUS:85133029620

SN - 9783031061554

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 267

EP - 278

BT - Euro-Par 2021

A2 - Chaves, Ricardo

A2 - B. Heras, Dora

A2 - Ilic, Aleksandar

A2 - Unat, Didem

A2 - Badia, Rosa M.

A2 - Bracciali, Andrea

A2 - Diehl, Patrick

A2 - Dubey, Anshu

A2 - Sangyoon, Oh

A2 - L. Scott, Stephen

A2 - Ricci, Laura

PB - Springer Science and Business Media Deutschland GmbH

T2 - 27th International Conference on Parallel and Distributed Computing, Euro-Par 2021

Y2 - 30 August 2021 through 31 August 2021

ER -

Guzmán-Merino M, Durán C, Marinescu MC, Delgado-Sanz C, Gomez-Barroso D, Carretero J et al. Data Management in EpiGraph COVID-19 Epidemic Simulator. In Chaves R, B. Heras D, Ilic A, Unat D, Badia RM, Bracciali A, Diehl P, Dubey A, Sangyoon O, L. Scott S, Ricci L, editors, Euro-Par 2021: Parallel Processing Workshops - Euro-Par 2021 International Workshops, 2021, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2022. p. 267-278. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-06156-1_22

Data Management in EpiGraph COVID-19 Epidemic Simulator

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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