Fuel shortages during hurricanes: Epidemiological modeling and optimal control

Area of Science:

• Disaster Management
• Epidemiology
• Computational Modeling

Background:

• Hurricanes in the southeastern US cause significant socioeconomic impacts, including widespread fuel shortages during evacuations.
• Effective emergency preparedness models are crucial for mitigating hurricane-related disruptions.
• Fuel shortages during mass evacuations pose a persistent challenge.

Purpose of the Study:

• To model and analyze hurricane-induced fuel shortages using an epidemiological approach.
• To develop an optimal control strategy for refueling operations to alleviate fuel scarcity.
• To utilize crowd-sourced data for model parameterization and validation.

Main Methods:

• The Susceptible-Infected-Recovered (SIR) epidemic model was adapted to simulate fuel shortages.
• Unscented Kalman Filter (UKF) was employed to estimate dynamic parameters of the SIR model.
• Crowd-sourced data from Hurricane Irma and Florence were used for model calibration.
• An optimal control strategy, analogous to vaccination, was developed for refueling interventions.

Main Results:

• The basic reproduction number for fuel shortages in Miami during Hurricane Irma was estimated at 3.98.
• The control model demonstrated the effectiveness of targeted refueling interventions.
• Simulations showed that specific refueling rates and durations could significantly reduce peak fuel shortage percentages.
• For Naples-Fort Myers during Hurricane Irma, a refueling rate of 0.1 for 2.2 days reduced peak shortage from 55% to 48%.

Conclusions:

• The SIR epidemic model provides a viable framework for understanding and managing hurricane-related fuel shortages.
• Optimal control strategies, informed by data-driven modeling, can effectively mitigate fuel scarcity during evacuations.
• Proactive and targeted refueling interventions are essential for enhancing community resilience during hurricane events.