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

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.

本文件为Carollo等（2022年）撰写的预印本“COVID-19疫情期间的自我感知孤独与抑郁：一项两次波次复制研究”的脚本说明。访问预印本请见：https://ucl.scienceopen.com/document/read?vid=0769d88b-e572-48eb-9a71-23ea1d32cecf

摘要：背景：全球COVID-19大流行迫使各国实施严格的封锁限制和强制居家令，对个人健康产生了不同程度的影响。结合数据驱动机器学习范式和统计方法，我们先前的研究论文记录了在英国和希腊人群中，在首次封锁期间（2020年4月17日至7月17日）自我感知孤独程度的U型模式。本文旨在通过聚焦于英国首次和第二次封锁波次的数据来检验这些结果的稳健性。方法：我们测试了所选模型对锁定期间识别最敏感变量的影响。采用两种新的机器学习模型——即支持向量回归（SVR）和多元线性回归（MLR）——从第1波次（n=435）的英国数据集中识别最敏感的变量。在本研究的第二部分，我们测试了b）首次英国全国封锁期间发现的自我感知孤独模式是否可推广到第二次英国封锁波次（2020年10月17日至2021年1月31日）。为此，使用了第二次英国封锁波次（n=263）的数据，对自我感知孤独分数的周分布进行了图形和统计分析。结果：在SVR和MLR模型中，抑郁症状在封锁期间均显示出最敏感的变量。对抑郁症状按封锁周次进行的统计分析结果显示，在英国全国首次封锁的第1波次中，第3至7周呈现U型模式。此外，尽管第2波次每周的样本量过小，不足以得出有意义的统计见解，但采用定性和描述性方法，观察到封锁第3至9周存在图形上的U型分布。结论：与以往研究一致，本研究结果表明，自我感知的孤独感和抑郁症状可能是实施封锁限制时需要优先关注的两种最相关症状。特别是，该文件夹包含研究的预处理、训练和后处理阶段的脚本。

==== 预处理第1波次数据集 ====
- “01_preprocessingWave1.py”：此文件包括对第1波次数据中感兴趣变量的预处理；
- “02_participantsexcludedWave1.py”：此文件包括用于实施第1波次研究排除标准的脚本；
- “03_countryselectionWave1.py”：此文件包括用于选择第1波次英国数据集的脚本。

==== 预处理第2波次数据集 ====
- “04_preprocessingWave1.py”：此文件包括对第2波次数据中感兴趣变量的预处理；
- “05_participantsexcludedWave1.py”：此文件包括用于实施第2波次研究排除标准的脚本；
- “06_countryselectionWave1.py”：此文件包括用于选择第2波次英国数据集的脚本。

==== 训练 ====
- “07_MLR.py”：此文件包括运行多元回归模型的脚本；
- “08_SVM.py”：此文件包括运行支持向量回归模型的脚本。

==== 后处理：统计分析 ====
- “09_KruskalWallisTests.py”：此文件包括运行多对和多对Kruskal-Wallis检验的脚本。