InfluenzaLab

with Primož Škraba

Influenzanet is a system to monitor the activity of influenza-like-illness (ILI) with the aid of volunteers via the internet. It has been operational for more than 10 years at the EU level since 2008. In contrast with the traditional system of sentinel networks of mainly primary care physicians, Influenzanet obtains its data directly from the population. This creates a fast and flexible monitoring system whose uniformity allows for direct comparison of ILI rates between countries.

Persistent homology is a central tool in topological data analysis, which examines the structure of data through topological structure. In the past years it has taken an important role in the development of medicine. It is an area of mathematics interested in identifying a global structure by inferring high-dimensional structure from low-dimensional representations and studying properties of a often continuous space by the analysis of a discrete sample of it, assembling discrete points into global structure. The basic technique can be extended in many different directions, permuting the encoding of topological features by barcodes and correspondent persistence diagrams.

Using persistence we are able to analyze the Influenzanet data identifying several topological features relevant to the epidemiological study. In particular, we can identify data noise, distinguish higher dimension features and look at join spaces between countries. This is done both in terms of the overall structure of a disease as well as its evolution. Finally, it provides a way to test agreement at a global scale arising from standard local models.

The data used here was provided by Gabriela Gomes and Carl Koppeschaar, from Gripenet Project and its successor Influenzanet. It can be accessed at www.influenzanet.eu/en/data/

Key Words: Topological Data Analysis, Epidemiology, persistence diagrams, algorithms, Influenza.