Socio-economic predictors of public understanding of the COVID-19 pandemic

Abstract

The COVID-19 (Coronavirus 2019) pandemic has proven to be the biggest global shock since World War II. That war resulted in 5.5 million deaths. The number of COVID-19-infected persons exceeded 13 million in the first 6 months of the pandemic and many more asymptomatic cases are undocumented. The global economy has been affected severely. The tension, the fear, the drastic measures to try to control the spread of the disease disrupted everyone's life from child to senior. The condition is worse in the global south, such as in Bangladesh, where the average population density is 7.5 times higher than that of China, where COVID-19 began and spread uncontrollably at the end of 2019. Lockdowns and social distancing were tried to stop the transmission of the disease but were often not observed faithfully or were less effective than thought to be. People need to trade and interact to earn money to survive but these activities could endanger others' lives if they do not maintain safety measures. Individual awareness is not only curtailing the spread of COVID-19 but also saves others' lives. This cross-sectional study used Ordinal and Binary logit models to predict the level of awareness through potential regressors of the citizen toward COVID-19 in Bangladesh. Findings of the study are that the level of awareness is dependent on the level of trauma; also, that household income is a statistically-significant predictor of awareness. Behavioral activities such as use of masks, outdoor activities, and stockpiling tendencies are found to be statistically significant predictors of awareness as well.

Keywords

• COVID-19
• Awareness
• Socio-economic factors
• Behavioral pattern
• Mental condition
• Ordered logit model