Research instruments can be evaluated and improved when tested during a disaster exercise. Lack of data recovery hampers disaster research even in the artificial setting of a national disaster exercise. Providers at every level must be aware that proper data collection is essential to improve the quality of health care during a disaster, and that predisaster cooperation is crucial to validate patient outcomes. These problems must be addressed pre-exercise by stakeholders and decision-makers during planning, education, and training. If not, disaster exercises will not meet their full potential.

In life-threatening situations where every second counts, the timely presence of firefighter services can make the difference between survival and death. Motivated by this, in collaboration with Fire Department Amsterdam-Amstelland in the Netherlands, we developed a mathematical programming model for determining the optimal locations of the vehicle base stations, and for optimally distributing firefighter vehicle types over the base stations. The model is driven by practical considerations. It (1) allows for fixing any subset of existing base locations that cannot be relocated (e.g., for historical reasons); (2) includes multiple vehicle types, each of which may have a type-dependent response-time target; and (3) includes crews that consist of arbitrary mixtures of professional (i.e., career) and volunteer firefighters. Extensive analysis of a large data set obtained from the Fire Department Amsterdam-Amstelland demonstrates: (1) that a reduction of over 50 percent in the fraction of firefighter late arrivals can be realized by relocating only three of the current 19 base locations; and (2) that adding new base locations to improve performance is unnecessary: optimization of the locations of the current base stations is as effective, and saves money. The results show an enormous potential for substantially reducing the fraction of late arrivals of firefighter services, with little investment in relocating a small number of stations.

The effectiveness of a fire department is largely determined by its ability to respond to incidents in a timely manner. To do so, fire departments typically have fire stations spread evenly across the region, and dispatch the closest truck(s) whenever a new incident occurs. However, large gaps in coverage may arise in the case of a major incident that requires many nearby fire trucks over a long period of time, substantially increasing response times for emergencies that occur subsequently. We propose a heuristic for relocating idle trucks during a major incident in order to retain good coverage. This is done by solving a mathematical program that takes into account the location of the available fire trucks and the historic spatial distribution of incidents. This heuristic allows the user to balance the coverage and the number of truck movements. Using extensive simulation experiments we test the heuristic for the operations of the Fire Department of Amsterdam-Amstelland, and compare it against three other benchmark strategies in a simulation fitted using 10 years of historical data. We demonstrate substantial improvement over the current relocation policy, and show that not relocating during major incidents may lead to a significant decrease in performance.