Simulating disaster recovery as discrete event processes using python

Community disaster resilience is commonly conceptualized as the capacity to reduce post-event loss and facilitate effective recovery. Technologies, such as data systems, computer models, and visualization tools, are more common and well developed for understanding immediate (and static) loss than for understanding dynamic processes of recovery. Most available technology for understanding post-disaster dynamics is specific to short-term emergency or crisis processes. As a result, development of simulation models of recovery is necessary to enable technology-supported decision making for realizing community disaster resilience. We present a proof of concept design for a home reconstruction discrete-event simulation (DES) to evaluate its potential for simulating disaster recovery in general. The design is implemented as a prototype using the SimPy discrete-event simulation Python library. Preliminary outputs from the prototype simulation suggest that DES is appropriate and promising for modeling home reconstruction. The ability to alter the quantities of shared resource stocks, event durations, and access qualifications can likely facilitate modeling of other types of recovery processes, as well as a variety of post-disaster scenarios. As such, DES appears to be a novel technological approach that can be developed to support pre-and post-disaster decision making for improved community disaster resilience.