Exploring the wild birds' migration data for the disease spread study of H5N1: a clustering and association approach
Knowledge about the wetland use of migratory bird species during the annual life circle is very interesting to biologists, as it is critically important in many decision-making processes such as for conservation site construction and avian influenza control. The raw data of the habitat areas and the migration routes are usually in large scale and with high complexity when they are determined by high-tech GPS satellite telemetry. In this paper, we convert these biological problems into computational studies and introduce efficient algorithms for the data analysis. Our key idea is the concept of hierarchical clustering for migration habitat localizations, and the notion of association rules for the discovery of migration routes from the scattered location points in the GIS. One of our clustering results is a tree structure, specially called spatial-tree, which is an illusive map depicting the breeding and wintering home range of bar-headed geese. A related result to this observation is an association pattern that reveals a high possibility that bar-headed geese's potential autumn migration routes are likely between the breeding sites in the Qinghai Lake, China and the wintering sites in Tibet river valley. Given the susceptibility of geese to spread H5N1, and on the basis of the chronology and the rates of the bar-headed geese migration movements, we can conjecture that bar-headed geese play an important role in the spread of the H5N1 virus at a regional scale in Qinghai-Tibetan Plateau.
h5n1 clustering habitat route qinghai lake sequence mining bird migration