  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 simplify the
regulation landscape that can help build scenarios for resilient demand
planning. We illustrate this resilient demand planning model using a specific
example of gas retailing. We find that demand is sensitive to fear of COVID-19:
as the number of COVID-19 cases increase over the previous week, the demand for
gas decreases -- though this dissipates over time. Further, government
regulations restrict access to different services, thereby reducing mobility,
which in itself reduces demand.
