Predicting Depression and Anxiety: A Multi-Layer Perceptron for
  Analyzing the Mental Health Impact of COVID-19
David Fong Tianshu Chu Matthew Heflin Xiaosi Gu Oshani Seneviratne
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.