Data Management in EpiGraph COVID-19 Epidemic Simulator
27th International Conference on Parallel and Distributed Computing, Euro-Par 2021
; 13098 LNCS:267-278, 2022.
Article
in English
| Scopus | ID: covidwho-1919679
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. © 2022, Springer Nature Switzerland AG.
COVID-19; Epidemiological simulation; Heterogeneous data processing; Parallel tool; Data acquisition; Data handling; Information management; Population statistics; Simulators; Viruses; Agent based; Epidemiological simulations; Heterogeneous data; Heterogeneous data sources; Post-processing; Pre-processing; Social process; Virus propagation
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
27th International Conference on Parallel and Distributed Computing, Euro-Par 2021
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS