Improving targeted surveillance by identifying the source of a contagious disease spreading in a trading network

Finding the index case of a contagious disease spreading within a network is mathematically equivalent of finding the origin of a rumor in a social network. The Sparse Inference algorithm (1) has been developed to deal with the problem of identifying the source of information spreading in different types of network. In this paper, we present an application of this algorithm to the identifcation of the potential sources of Newcastle Disease spreading in a poultry trading network in the Lac Alaotra region, Madagascar. We used the epidemiological and network data collected during a previous study (2), between December 2009 and December 2010. Knowing the structure of the network and sampling a minimum of 3 nodes, the Sparse Inference algorithm computed the likelihood of nodes to be a source. We illustrated how this technique can improve the targeting of particular markets for surveillance. By using the results from a network analysis resulting from the same set of data (3), one large lively-poultry market was identified to play a key role in the spread of Newcastel Disease in the region. (Resume d'auteur)