7–9 Apr 2011
Europe/Stockholm timezone

Identifying diseases of unknown origin using network theory

8 Apr 2011, 14:40
30m
FD5

FD5

Speaker

Dr Sebastian Funk (Institute of Zoology)

Description

Community structure is a ubiquitous feature of complex networks, and methods for its detection has gained much attention in recent years. Beyond the study social networks with well defined links, these methods can be generalised to operate on any dataset in which different entities are similar in one or more traits, and be used to identify meaningful groupings. Here, we describe the application of network theory and methods for finding community structure to identify undiagnosed disease outbreaks reported in online surveillance systems. The efficacy of these programs is often inhibited by the anecdotal nature of informal or rumour-based reporting, and uncertainty of pathogen identity. We create associations between disease outbreaks and and their symptoms, case fatality ratio, and seasonality, and represent them in an abstract network. We train the model with a set of outbreaks reports of 10 known infectious diseases causing encephalitis and combine methods for community detection with an optimisation procedure for symptom weights to generate networks of maximal modularity. We then use these to determine a most probable identification for 97 outbreaks of encephalitis reported in an online surveillance system as undiagnosed or ‘mystery illness’, by determining the best association with communities in the reference networks. This illustrates the general use of methods from network analysis for the study of datasets even where links are not obvious physical entities but mere measures of similarity.

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