Assessing casualty densities based on sensor reports pursuant to a large-scale disaster

One of the newest innovations which are making its way more prevalently into the field of emergency response is information technology. Information technology (IT), in this sense, seeks to turn relevant data into usable information to aid in an emergency response. One of the key elements to useful beneficial IT is to quickly, accurately, and dynamically turn incoming data into usable information. This paper presents a way to statistically analyze incoming casualty reports at specific time intervals to not only estimate casualty densities, but also assess whether or not the casualty densities being observed are within some confidence interval of an expected number of casualties. Simple models of the searching process are developed and used to dynamically analyze an incoming report stream. If the numbers of casualties are sufficiently different than the expected number, then one might conclude either a secondary event has occurred or the initial estimates were simply wrong.