Background
In 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.

Results
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.

Conclusions
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.

BMC Medical Informatics and Decision Making – Full text

Experiments using a microsimulation platform show that vaccination against pandemic H1N1 influenza is highly cost-effective. Swedish society may reduce the costs of pandemic by about SEK 2.5 billion (approximately EUR 250 million) if at least 60 per cent of the population is vaccinated, even if costs related to death cases are excluded. The cost reduction primarily results from reduced absenteeism. These results are preliminary and based on comprehensive assumptions about the infectiousness and morbidity of the pandemic, which are uncertain in the current situation.

Eurosurveillance – Full text

A two-layered architecture for reasoning that uses narratives to guide its behavior is presented. The narratives are interpreted by reactive and deliberative reasoning layers to generate responses to external events, in accordance with internal desires created by the deliberative layer. A C++ implementation of the proposed Pickman architecture is described and tested in five scenarios in which three different Pickman versions are tasked with healing a small population suffering from a virtual plague. It is shown that a Pickman implementation using a static desire generation mechanism is efficient, but unreliable. The dynamic version, on the other hand, is not as efficient, but it performs successfully in all scenarios.

Master’s Thesis – Full text