Outbreak Simulation and Planning

Johns Hopkins University, Baltimore MD

Investigators

Abstract

Syndromic surveillance systems are becoming quite common in public health departments around[unreadable] the country. These systems allow epidemiologists and other users to monitor disease trends in their[unreadable] communities, generally with the primary objective of identifying and acting on unusual disease patterns,[unreadable] whether natural or man-made, as quickly as possible. Most of the active surveillance systems are used[unreadable] daily by the responsible public health personnel who rapidly become adept at identifying abnormal[unreadable] patterns of disease in their communities and quickly come to recognize the patterns that describe normal[unreadable] seasonal variations in disease. They have little opportunity, however, to see how their normal daily view[unreadable] would change during a disease outbreak caused by a terrorist event because, luckily, few have occurred[unreadable] since most systems were installed. The only way users will become familiar with how their systems react[unreadable] during a man-made outbreak is to participate in training exercises in which simulated data injected into[unreadable] their system mimics outbreak conditions. Unfortunately the creation of such exercises for these systems[unreadable] is a complex task which is beyond the scope of most health departments.[unreadable] The purpose of this project is to produce aframework of standards and software tools, the[unreadable] Exercise/Simulation Framework (ESF), that can be used with multiple syndromic surveillance systems to[unreadable] create 'table top' exercises that mimic disease outbreaks. These exercises can be used for training[unreadable] purposes, to help model the effect of public health response protocols such as mass prophylaxis in a[unreadable] specific community, and to help develop public health response plans for use in emergency situations. In[unreadable] addition the ESF can be used to evaluate the effect of surveillance algorithms under a variety of different[unreadable] disease patterns.