Using Insights from Behavioral Economics to Mitigate the Spread of COVID-19

Abstract

The outbreak of 2019 coronavirus disease (COVID-19) has become a public health emergency of international concern. The number of COVID-infected individuals and related deaths continues to rise rapidly. Encouraging people to adopt and sustain preventive behaviors is a central focus of public health policies that seek to mitigate the spread of COVID-19. Public health policy needs improved methods to encourage people to adhere to COVID-19-preventive behaviors. In this paper, we introduce a number of insights from behavioral economics that help explain why people may behave irrationally during the COVID-19 pandemic. In particular, present bias, status quo bias, framing effect, optimism bias, affect heuristic, and herding behavior are discussed. We hope this paper will shed light on how insights from behavioral economics can enrich public health policies and interventions in the fight against COVID-19.

Cross-country differences in anxiety and behavioral response to the Covid-19 pandemic

“…The association between patience (i.e. a person's propensity wait self-possessed in the face of suffering and frustration) and anxiety is consistent with the theoretical expectations (Soofi et al 2020) and what has been found previously (Schnitker 2012). More altruistic societies also suffered more from anxiety.…”

“…In the case of the Covid-19 pandemic, preventive policies such as social distancing and staying at home involves a trade-off between other activities such as going to restaurants or malls. Accordingly, those with present bias might be less proactive in response to the Covid-19 pandemic and be more adversely affected by the ongoing situation (Soofi et al 2020).…”

From Cashews to Nudges: The Evolution of Behavioral Economics

“…After all, why should Palm be worth more outside of 3Com than in? 9 Nevertheless, the strategy seemed to work. Upon the announcement of the plan the shares of 3Com soared from about $40 to more than $100.…”

Predicting COVID-19 Spread Level using Socio- Economic Indicators and Machine Learning Techniques

“…Moreover, the previous study was restricted to the case of China. In another study [17], perspectives from behavioral economics were explored, focusing attention on how to encourage people to take part in preventive behaviors with COVID-19. In this previous work, the presented study was qualitative and was not based on any mathematical modeling or calculation.…”