Measurement of Negative Impacts of COVID-19 Pandemic and Lockdown – a Statistical Learning Approach

Abstract

Millions were killed due to COVID-19 pandemic. Lock downs were imposed to control this pandemic. In this paper the negative side effects of this pandemic were measured and quantified. Mathematical models using Structural Equation Modeling and Regression Trees were developed. Based on a primary data of 578 households, out of 56 variables of interest, 47 variables were focused. The other nine variables collected general information. Principal Component Analysis was used for variable reduction. Out of these variables it was found that, Information on people suffering from COVID-19, Information on people recovering from COVID-19, I am afraid of COVID-19 outbreak, News and media increase my tension, Afraid of getting COVID-19, Afraid of losing my life and my relative’s life and Limited my social life, were major contributors to Degradation of Mental Health. These variables had standardized factor loading greater than 0.7. Similarly, it was found that households from lower income groups were worst affected by mental health degradation and food insecurity. These conclusions with p ≤ 0.01, were made. With the developed models, households were profiled with respect to income and degradation of mental health. What gets measured should also get addressed, was the motivation of this work.