Tesi etd-09222022-134328 |
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Tipo di tesi
Tesi di laurea magistrale
Autore
MAZZEI, MARCO
URN
etd-09222022-134328
Titolo
Explaining the long-tail effect of COVID-19 lockdown on physical activity, quality of sleep, and quality of life in the Italian population
Dipartimento
INFORMATICA
Corso di studi
INFORMATICA
Relatori
relatore Pappalardo, Luca
relatore Rossi, Alessio
relatore Rossi, Alessio
Parole chiave
- machine learning
- artificial intelligence
- data analytics
- data
- lockdown
Data inizio appello
07/10/2022
Consultabilità
Tesi non consultabile
Riassunto
From March 2020 to May 2021, several lockdown periods caused by the COVID-19 pandemic have limited, with varying degrees of severity, the people’s usual activities and mobility in Italy and around the world. Together with the COVID-related consequences, these unprecedented confinement measures dramatically modified the citizens’ daily lifestyles and behaviors.
However, with the advent of summer 2021, thanks also to the vaccination campaign that significantly reduced the risk of contagion, all the Italian regions finally returned to regular moving behaviors and routines. Despite this, it is plausible that the potential consequences of lockdown are still persistent among the Italian citizens, and whether people’s quality of life, sleep and physical activity-related behaviors have returned to the pre-lockdown level is still unclear. The dataset described in this thesis permits to evaluate the long tail effect of COVID-19 lockdown in Italy comparing the respondents’ habits and well-being status of respondents in the months before (November 2019 and January 2020) and in the months long-after (November 2021 and January 2022) the confinement periods. Through the analysis of this dataset, this thesis permits to evaluate the long-term effects of COVID-19 lockdown in Italy in terms of physical activity, quality of sleep, mental health, and quality of life levels, which are still unexplored in literature. This is done firstly by a statistical analysis, and then by applying Artificial Intelligence techniques. The main finding of this work is that the Italian population worsened in all the explored aspects aforementioned. This worsening was more marked in young adults and women. We provide models to predict which will be the people who are most likely to worsen their condition in case of a new lockdown. This information provides practical suggestions to local, regional, and state institutions and companies to improve infrastructures and services that could be beneficial for the citizens.
However, with the advent of summer 2021, thanks also to the vaccination campaign that significantly reduced the risk of contagion, all the Italian regions finally returned to regular moving behaviors and routines. Despite this, it is plausible that the potential consequences of lockdown are still persistent among the Italian citizens, and whether people’s quality of life, sleep and physical activity-related behaviors have returned to the pre-lockdown level is still unclear. The dataset described in this thesis permits to evaluate the long tail effect of COVID-19 lockdown in Italy comparing the respondents’ habits and well-being status of respondents in the months before (November 2019 and January 2020) and in the months long-after (November 2021 and January 2022) the confinement periods. Through the analysis of this dataset, this thesis permits to evaluate the long-term effects of COVID-19 lockdown in Italy in terms of physical activity, quality of sleep, mental health, and quality of life levels, which are still unexplored in literature. This is done firstly by a statistical analysis, and then by applying Artificial Intelligence techniques. The main finding of this work is that the Italian population worsened in all the explored aspects aforementioned. This worsening was more marked in young adults and women. We provide models to predict which will be the people who are most likely to worsen their condition in case of a new lockdown. This information provides practical suggestions to local, regional, and state institutions and companies to improve infrastructures and services that could be beneficial for the citizens.
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