We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression
and Anxiety Predictor (CoDAP) to predict mental health trends, particularly
anxiety and depression, during the COVID-19 pandemic. Our method utilizes a
comprehensive dataset, which tracked mental health symptoms weekly over ten
weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort
of U.S. adults. This period, characterized by a surge in mental health symptoms
and conditions, offers a critical context for our analysis. Our focus was to
extract and analyze patterns of anxiety and depression through a unique lens of
qualitative individual attributes using CoDAP. This model not only predicts
patterns of anxiety and depression during the pandemic but also unveils key
insights into the interplay of demographic factors, behavioral changes, and
social determinants of mental health. These findings contribute to a more
nuanced understanding of the complexity of mental health issues in times of
global health crises, potentially guiding future early interventions.

使用名为 COVID-19 抑郁焦虑预测器（CoDAP）的多层感知器（MLP），跟踪了一组美国成年人在 COVID-19 大流行初期（2020 年 4 月至 6 月）十周内的心理健康症状，从而预测了疫情期间焦虑和抑郁模式，并揭示了人口统计因素、行为变化和社会决定性心理健康之间的关系。这些发现有助于更深入地理解全球卫生危机时期心理健康问题的复杂性，并潜在地指导未来的早期干预。