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.