%0 Journal Article
%T CASE: a framework for computer supported outbreak detection
%A Baki Cakici
%A Kenneth Hebing
%A Maria Grščnewald
%A Paul Saretok
%A Anette Hulth
%J BMC Medical Informatics and Decision Making
%D 2010
%I BioMed Central
%R 10.1186/1472-6947-10-14
%X Based on case information, such as diagnosis and date, different statistical algorithms for detecting outbreaks can be applied, both on the disease level and the subtype level. The parameter settings for the algorithms can be configured independently for different diagnoses using the provided graphical interface. Input generators and output parsers are also provided for all supported algorithms. If an outbreak signal is detected, an email notification is sent to the persons listed as receivers for that particular disease.The framework is available as open source software, licensed under GNU General Public License Version 3. By making the code open source, we wish to encourage others to contribute to the future development of computer supported outbreak detection systems, and in particular to the development of the CASE framework.In this paper, we describe the design and implementation of a computer supported outbreak detection system called CASE (named after the protagonist of the William Gibson novel Neuromancer), or Computer Assisted Search for Epidemics. The system is currently in use at the Swedish Institute for Infectious Disease Control (SMI) and performs daily surveillance using data obtained from SmiNet [1], the national notifiable disease database in Sweden.Computer supported outbreak detection is performed in two steps:1 A statistical method is automatically applied to a collection of case reports in order to detect an unusual or unexpected number of cases for a particular disease.2 An investigation by a human expert (an epidemiologist) is performed to determine whether the detected irregularity denotes an actual outbreak.The main function of a computer supported outbreak detection system is to warn for potential outbreaks. In some cases, the system might be able to detect outbreaks earlier than human experts. Additionally, it might detect certain outbreaks that human experts would have overlooked. However, the system does not aim to replace human experts
%U http://www.biomedcentral.com/1472-6947/10/14