Evaluating the spread of Omicron COVID-19 variant in Spain Articles uri icon

publication date

  • August 2023

start page

  • 547

end page

  • 561

volume

  • 149

International Standard Serial Number (ISSN)

  • 0167-739X

Electronic International Standard Serial Number (EISSN)

  • 1872-7115

abstract

  • This work analyzes the propagation the highly transmissible COVID-19 variant Omicron across Spain via simulation by using EpiGraph. EpiGraph is an agent-based parallel simulator that reproduces the COVID-19 propagation over wide areas. In this work we consider a population of 19,574,086 individuals of the 63 most populated cities of Spain, for the time interval between May 15th 2021 and March 6th 2022. The main variants existing at the start of the simulation were the Alpha and Delta, with prevalence of 4% and 96%. Then, during the second half of November 2021, the Omicron variant appears in Spain. Due to the higher transmission of this new variant — about 2 times larger than Delta, it quickly spreads through all the cities and becomes the dominant strain in the country. In this work we analyze the propagation of this variant under different mobility restrictions and patient zero scenarios. We first define a baseline scenario which reproduces the existing conditions of the COVID-19 propagation in Spain for our period of study. We then consider alternative scenarios for different starting locations of the propagation. Finally, for each one of these scenarios, we evaluate different transportation intensities — i.e. movement of individuals between the cities. The main conclusion is that, independently of the initial location of the Omicron variant and the existing transportation conditions, the Omicron variant spreads through all the country in a short time interval. The work presented in this paper also implements and evaluates a power monitoring and optimization system aimed at reducing the energy consumption of such massive simulations as the ones performed in EpiGraph.

subjects

  • Computer Science

keywords

  • epidemiological simulation; covid-19; parallel simulator; transportation