CASE: a framework for computer supported outbreak detection

By B. Cakici, Kenneth Hebing, Maria Grünewald, Paul Saretok, Anette Hulth. 2010

55 citations recorded by the source corpus.

BackgroundIn computer supported outbreak detection, a statistical method is applied to a collection of cases to detect any excess cases for a particular disease. Whether a detected aberration is a true outbreak is decided by a human expert. We present a technical framework designed and implemented at the Swedish Institute for Infectious Disease Control for computer supported outbreak detection, where a database of case reports for a large number of infectious diseases can be processed using one or more statistical methods selected by the user.ResultsBased 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.ConclusionsThe 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.

Subfields: Artificial Intelligence, Biomedical Engineering, Epidemiology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Li Xiong
2. Slawomir Goryczka
3. Rasmus Pagh
4. Badih Ghazi
5. Adrià Gascón
6. Borja Balle
7. Kobbi Nissim
8. James Bell

In Epidemiology

1. Li Xiong
2. Slawomir Goryczka

Citations in the corpus

• A Comprehensive Comparison of Multiparty Secure Additions with Differential Privacy (2015, background): “Syndromic surveillance systems monitor population in the real time for early detection of large-scale disease outbreaks and bioterrorism attacks [10], [54].”