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.
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
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 - http://www.scopus.com/inward/record.url?scp=85133029620&partnerID=8YFLogxK
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 -
