Performance of hospital and transportation networks after a simulated seismic event

Hospital overcrowding is a serious issue that compromises daily service quality in many modern communities worldwide. Reduction of ward beds without an increment of low-intensity structures, bad-users, and obsolete equipment are few examples of the common causes of emergency departments overloading. Traffic jam is another ordinary city-scale issue accentuated during the last decades, when the urban population overcame the rural one but roads capacity remained the same. These phenomenon are even more emphasized during emergencies due to natural disasters, because thousands of people need aid. In such situations, roads could be blocked by collapsed buildings, causing an increase of casualties’ travel time towards hospitals and the latter reach quickly their maximum capacity therefore patients might need to be relocated in other facilities. This process has to be carefully managed otherwise it can lead to tremendous human losses. The goal of this research is to analyze the response of transportation and hospital networks of a city after a severe seismic event. The case study focuses on modelling the patient flow into a large-scale virtual environment called Ideal City that is based on the city of Turin in Italy. The number of injured people was estimated in four levels of severity. Two simulated scenario, both of them during the night but the second with some closed roads, were applied to the virtual urban environment including eight modelled hospitals. Each of them has its emergency room, but the number of resources and beds vary according to the collected data. Patient flow is described through an agent-based model that simulates the routing towards the closest hospital. Once casualties arrive to a structure, according to the triage code based on the severity level, they have to wait a certain time, in line with the available resources and spaces, before receiving all medications. Different categories of medical treatment time are defined by Emergo Train System guidelines. The waiting time for each patient is then monitored by a discrete-event model, so that when the hospital reaches its maximum capacity, patients can be transferred to another facility. Critical weaknesses are identify and different approaches are discussed to decrease the waiting time and to improve the overall hospital network performance.