Baki Cakici

CASE: a framework for computer supported outbreak detection

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.

A workflow for software development within computational epidemiology

A critical investigation into computational models developed for studying the spread of communicable disease is presented. The case in point is a spatially explicit micro-meso-macro model for the e ...

Changing Behaviour to Save Energy: ICT-Based Surveillance for a Low-Carbon Economy in the Seventh Framework Programme

In research and development of information and communication technologies for sustainability, there is a strong belief that human behaviour can be monitored at the individual level to generate different signals, and that these signals can be used to influence individuals to behave differently. We analyse Seventh Framework Programme policy documents published by the European Commission, and descriptions of research projects granted funding from it, to highlight the uncritical development and application of surveillance technologies to change human behaviour. We argue that EU-financed projects dealing with sustainability and information and communication technology use models of social change that have been widely criticised as unlikely to lead to substantial changes in resource consumption. Additionally, we show that these texts discuss only the potential positive effects of technological surveillance, but neither acknowledge nor require the handling of the potential negative effects of surveillance.