Epidemic disease propagation detection algorithm using MapReduce for realistic social contact networks

The control and prevention of epidemics like influenza is a matter of high concern for the public health and decision support to the policy makers of public health. Epidemic disease propagation in a social contact network for the spread of contagion in a large real social contact having millions of individuals often becomes challenging for high performance computing. In this paper we present a novel MapReduce algorithm to detect the boundary of infectious nodes in social contact network. We used smart phone based personnel and community sensing for collecting the individual´s connection, communication and interaction to others with respect to time. Using this extracted smart phone data; user´s health status is predicted.