RESUMO
This work analyzes the propagation of Omicron, a high transmissible COVID-19 variant, across Spain by means of 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 related to the 63 most populated cities of Spain for the time interval comprised between May 15th of 2021 and March 6th of 2022. The main variants existing at the start of the simulation were the Alpha and Delta, with a a 4% and 96% prevalence of the existing infections, respectively. Then, during the second mid of November of 2021 the Delta variant appears in Spain. Given 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 of the country. In this work we analyze the propagation of this variant under multiple conditions. First, we define a baseline scenario, that reproduces the existing conditions of the COVID-19 propagation in Spain for this period. Then, we consider alternative scenarios, in which different locations of the initial spread of Omicron variant are considered. Finally, for each one of these scenarios, we evaluate different transportation intensities-i.e. movement of individuals between the cities-. The main conclusion of this work is that, independently of the initial location of the Omicron variant, and the existing transportation conditions, the Omicron variant spreads though all the country in a short time interval. © 2022 IEEE.
RESUMO
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.
RESUMO
Based on our positive, but limited experience with Jigsaw at the university level, half a year ago we initiated a more extensive experiment with a larger sample of students, and incorporating changes that relate back to some negative comments we have received during the previous course. Jigsaw is a collaborative inquiry-based learning technique that works by dividing the learning material into different tasks and the class into different groups. What set out to be a controlled experiment in increasing motivation and participation through collaboration, turned into a much more complex scenario due to the arrival of the Covid-19 pandemic, which gave us some interesting results to report. We have seen more positive results this year than the last: the number of students that felt that Jigsaw requires more effort than traditional methods has fallen, they consistently thought that Jigsaw improved teamwork, and they felt they have learned more from their expert peers as the experiment advanced. Some of the results may be due to the confinement forcing people stay indoors, with no social outings and fewer distractions - so more time to study. Another factor that may be relevant are the implicit expectations that were set by the confinement about distance learning and the need to cooperate. © 2020 17th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2020. All rights reserved.