Researchers from Colorado State University created a modeling tool that could help city planners and emergency managers understand the full functionality and recovery of a healthcare system during a national disaster, like an earthquake or pandemic. The new model could be used for a variety of disasters, including earthquakes and COVID-19.
To measure and assess how quickly a community will recover after a disaster, professionals need to measure how and how quickly hospitals and healthcare will return to being fully functional. This isn’t easy to predict because the resilience measure of a healthcare system is complex.
Healthcare systems are complex networks that can be visualized as nodes with different functionalities. This includes the number of staffed beds, hospital staff availability, housing functionality, patient waiting time for treatment and the probability of patient X going into healthcare facility Y. The availability of water, power, transportation and telecommunication are additional factors added to the model. Healthcare is defined by physical metrics and quality metrics. The level of customer satisfaction is measured by factors such as patient wait time.
The team developed and tested the framework by applying the model to a virtual community, Centerville. Centerville is a mid-sized community with 50,000 residents with commercial and industrial zones, schools, fire stations and hospitals. The team applied an earthquake scenario to Centerville. The virtual environment highlighted the capabilities of the model and impact decisions that were made as the community would recover.
The purpose of this work was to define parameters that needed to be measured by communities to assess how prepared they are for natural disasters. The team started to use the model to predict how hospital networks can better manage pandemics by identifying gaps in resources and potential bottlenecks according to different worst-case scenarios.
The team is currently working with the National Center for Disease and Medicine and Public Health to further refine the model.
A paper on this technology was published in Reliability Engineering and System Safety.
