Consumer Demand Modeling During COVID-19 Pandemic
Shaz Hoda, Amitoj Singh, Anand Rao, Remzi Ural, Nicholas Hodson

TL;DR
This paper develops a comprehensive model linking COVID-19 disease progression, government regulations, and consumer behavior to improve demand planning, demonstrated through a case study on gas retailing during the pandemic.
Contribution
It introduces a quantitative behavior model of COVID-19 fear, integrates disease and regulation impacts on demand, and employs Bayesian inference for resilient demand scenario analysis.
Findings
Demand decreases as COVID-19 cases increase.
Government regulations reduce consumer mobility and demand.
Demand sensitivity to pandemic dynamics diminishes over time.
Abstract
The current pandemic has introduced substantial uncertainty to traditional methods for demand planning. These uncertainties stem from the disease progression, government interventions, economy and consumer behavior. While most of the emerging literature on the pandemic has focused on disease progression, a few have focused on consequent regulations and their impact on individual behavior. The contributions of this paper include a quantitative behavior model of fear of COVID-19, impact of government interventions on consumer behavior, and impact of consumer behavior on consumer choice and hence demand for goods. It brings together multiple models for disease progression, consumer behavior and demand estimation-thus bridging the gap between disease progression and consumer demand. We use panel regression to understand the drivers of demand during the pandemic and Bayesian inference to…
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Taxonomy
TopicsCOVID-19 Pandemic Impacts · COVID-19 epidemiological studies · Energy, Environment, and Transportation Policies
