RT Generic
T1 Data management in epiGraph COVID-19 epidemic simulator
A1 Guzmán Merino, Miguel
A1 Duran Gonzalez, Christian
A1 Marinescu, María Cristina
A1 Delgado Sanz, Concepción
A1 Gómez Barroso, Diana
A1 Carretero Pérez, Jesús
A1 Expósito Singh, David
A2 , 
AB The transmission of COVID-19 through a population depends on many factors which model, incorporate, and integrate a large number of 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.
YR 2021
FD 2021-08-29
LK https://hdl.handle.net/10016/33879
UL https://hdl.handle.net/10016/33879
LA eng
NO This work has been supported by the Spanish Instituto de Salud Carlos III under the project grant 2020/00183/001, the project grant BCV-2021-1-0011, of the Spanish Supercomputing Network (RES) and the European Union's Horizon 2020 JTI-EuroHPC research and innovation program under grant agreement No 956748.
DS e-Archivo
RD 27 jul. 2024