ScriptsUCLopen.zip (38.04 kB)
Download file

Self-Perceived Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication Study

Download (38.04 kB)
dataset
posted on 01.07.2022, 11:33 authored by Alessandro Carollo, Andrea BizzegoAndrea Bizzego, Giulio Gabrieli, Keri Ka-Yee WongKeri Ka-Yee Wong, Adrian Raine, Gianluca Esposito

This is the README file for the scripts of the preprint "Self-Perceived

Loneliness and Depression During the COVID-19 Pandemic: a Two-Wave Replication

Study" by Carollo et al. (2022)


Access the pre-print here:  https://ucl.scienceopen.com/document/read?vid=0769d88b-e572-48eb-9a71-23ea1d32cecf  


Abstract: Background: The global COVID-19 pandemic has forced countries to

impose strict lockdown restrictions and mandatory stay-at-home orders with

varying impacts on individual’s health. Combining a data-driven machine learning

paradigm and a statistical approach, our previous paper documented a U-shaped

pattern in levels of self-perceived loneliness in both the UK and Greek

populations during the first lockdown (17 April to 17 July 2020). The current

paper aimed to test the robustness of these results by focusing on data

from the first and second lockdown waves in the UK. Methods: We tested a) the

impact of the chosen model on the identification of the most time-sensitive

variable in the period spent in lockdown. Two new machine learning

models - namely, support vector regressor (SVR) and multiple linear regressor

(MLR) were adopted to identify the most time-sensitive variable in the UK

dataset from wave 1 (n = 435). In the second part of the study, we tested

b) whether the pattern of self-perceived loneliness found in the first UK

national lockdown was generalizable to the second wave of UK lockdown

(17 October 2020 to 31 January 2021). To do so, data from wave 2 of the UK

lockdown (n = 263) was used to conduct a graphical and statistical inspection

of the week-by-week distribution of self-perceived loneliness scores. Results:

In both SVR and MLR models, depressive symptoms resulted to be the most

time-sensitive variable during the lockdown period. Statistical analysis of

depressive symptoms by week of lockdown resulted in a U-shaped pattern

between week 3 to 7 of wave 1 of the UK national lockdown. Furthermore,

despite the sample size by week in wave 2 was too small for having a meaningful

statistical insight, a qualitative and descriptive approach was adopted and

a graphical U-shaped distribution between week 3 and 9 of lockdown was

observed. Conclusions: Consistent with past studies, study findings suggest

that self-perceived loneliness and depressive symptoms may be two of the

most relevant symptoms to address when imposing lockdown restrictions.


In particular, the folder includes the scripts for the pre-processing,

training, and post-processing phases of the research.


==== PRE-PROCESSING WAVE 1 DATASET ====

- "01_preprocessingWave1.py": this file include the pre-processing of the

variables of interest for wave 1 data;

- "02_participantsexcludedWave1.py": this file include the script adopted to

implement the exclusion criteria of the study for wave 1 data;

- "03_countryselectionWave1.py": this file include the script to select the UK

dataset for wave 1.


==== PRE-PROCESSING WAVE 2 DATASET ====

- "04_preprocessingWave1.py": this file include the pre-processing of the

variables of interest for wave 2 data;

- "05_participantsexcludedWave1.py": this file include the script adopted to

implement the exclusion criteria of the study for wave 2 data;

- "06_countryselectionWave1.py": this file include the script to select the UK

dataset for wave 2.


==== TRAINING ====

- "07_MLR.py": this file includes the script to run the multiple regression

model;

- "08_SVM.py": this file includes the script to run the support vector regression

model.


==== POST-PROCESSING: STATISTICAL ANALYSIS ====

- "09_KruskalWallisTests.py": this file includes the script to run the multipair

and the pairwise Kruskal-Wallis tests.


Funding

UCL Global Engagement Fund

History