More information at http://www.ke.tu-darmstadt.de/lehre/ss20/pr-ki
An outbreak refers to the rapid spread of an infectious disease, with an
epidemic usually referring to a more severe and intense form.
The
monitoring and surveillance of (especially contagious) diseases and the
identification of current or future outbreaks is of high importance for
ensuring the public health. Data mining and machine learning with their
different methods can contribute to the support of public health
authorities in performing this sensitive task.
In this practical
course, we will investigate the usage of different data mining
techniques to detect outbreaks and other outbreak related events. We
will be working on a real data set provided by the Robert-Koch-Institute
which provides information for Germany about infected cases over time
and locations. This data set may also be combined with external data
sources like weather information.
Outbreak detection involves
different tasks. For instance, it includes the nowcasting of outbreaks
but also the prediction of distributions of expected disease cases
(forecasting). We will also put a special interest on interpretable
models which allow to find valuable human-understandable relationships
in the data which are relevant for outbreak detection.
Participants
will explore and apply different techniques from data mining and machine
learning such as time series analysis, outlier detection,
auto-regression, weak learning and interpretable machine learning
techniques. But also data preprocessing, including data cleansing and
homogenization, are necessary tasks when working with real data.
- Dozent*in: Eneldo Loza Mencia