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Data Management in EpiGraph COVID-19 Epidemic Simulator

EasyChair Preprint no. 6468

12 pagesDate: August 29, 2021


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.

Keyphrases: COVID-19, Epidemiological simulation, Heterogeneous data processing, Parallel tool

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Miguel Guzmán-Merino and Christian Durán and Maria-Cristina Marinescu and Concepción Delgado-Sanz and Diana Gomez-Barroso and Jesus Carretero and David E. Singh},
  title = {Data Management in EpiGraph COVID-19 Epidemic Simulator},
  howpublished = {EasyChair Preprint no. 6468},

  year = {EasyChair, 2021}}
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