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.
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. We would like to thank to Diego Fernandez Olombrada for his support in the early collection of part of the data of this work.
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Notes
- 1.
Note the EpiGraph employs static and dynamic contact patterns, and in this section we are referring to the static one.
- 2.
Note that each collective and sub-collective has different group sized based on the activity that they perform.
- 3.
Note that the vaccination prioritization strategy is similar for all European countries.
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Guzmán-Merino, M. et al. (2022). Data Management in EpiGraph COVID-19 Epidemic Simulator. In: Chaves, R., et al. Euro-Par 2021: Parallel Processing Workshops. Euro-Par 2021. Lecture Notes in Computer Science, vol 13098. Springer, Cham. https://doi.org/10.1007/978-3-031-06156-1_22
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