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Title: COVID-19: Outbreak prediction combining meteorological, mobility and demographic data
Author: Pérez Ordieres, Jaime
Tutor: Sanchez-Bocanegra, Carlos Luis  
Abstract: Two years after the start of the pandemic caused by the COVID-19 virus, the Spanish health system has been on the verge of collapse on several occasions, forcing an adaptation of the system and professionals and highlighting some of the structural organizational shortcomings. From the scientific and educational fields, the need arises to alleviate these deficiencies through innovation. Unlike to what could happen in the past, a vast amount of data and information is currently available. Given such an amount of data, and in order to alleviate the effects of the pandemic on society, it is vital to identify relevant factors that help to identify situations of high spread of the virus in advance. The present work seeks to understand if the meteorological, mobility and demographic factors are relevant in the spread of the virus. To do this, public data combined with machine learning techniques applied to the prediction of time series will be used. The ultimate goal will be to provide tools that make it possible to predict coronavirus outbreaks, thus being able to optimize the available health resources.
Keywords: COVID-19
meteorological data
demographic data
Document type: info:eu-repo/semantics/masterThesis
Issue Date: 5-Jun-2022
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Appears in Collections:Bachelor thesis, research projects, etc.

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