Natural hazards, such as earthquakes, can disrupt the healthcare system heavily, leading to long wait times and many untreated patients for years after the event. Emergency services, in particular, must return to pre-earthquake functionality as soon as possible such that patients, especially critically injured ones, can be treated promptly. However, reconstruction of healthcare facilities, and thus the complete restoration of emergency services, can take years. Due to limited reconstruction resources, decision-makers cannot reconstruct all hospitals simultaneously. They are typically forced to prioritize the reconstruction order, and in reality, this process is often poorly planned. This paper models emergency services as an M/M/s queuing system that accounts for prioritized treatment of critical patients and formulates a greedy algorithm to plan for an effective healthcare system reconstruction. The algorithm finds the reconstruction ordering of hospital buildings such that emergency patients have the shortest time to receiving medical care possible. We show our greedy algorithm’s good performance for small problem instances, with average deviations from the optimal solution below 16%. Further, we apply our methodology to a case study of Lima, Peru, under a hypothetical M8.0 earthquake. The application demonstrates that a policy guided by our formulation performs better than policies typically implemented by decision-makers, reducing the number of untreated patients by more than a factor of three in a worst case scenario with 70% hospital capacity disruption. Finally, we demonstrate that our formulation can be integrated into risk analysis through

The code for this project can be found here.

The journal paper for this project is published in the journal Risk Analysis, and can be found here.

This article also received the honorable mention for the EERI 2022 Graduate Student Paper Award. See the announcement here.