More information at

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.