Mobile health application for early disease outbreak-period detection

Mankind has experienced several deadly disease outbreaks, such as, cholera, plague, yellow fever, SARS, and dengue. Researchers need to study disease propagation data in order to understand patterns of disease outbreaks, their nature, symptoms, and ways of containment and cure. Though our healthcare establishments record and maintain patient information, they fail to detect a pandemic at an early stage due to the following challenges. Firstly, modern people are too busy to visit a doctor at the early stage of their symptoms which along with their high degree of mobility fuels the risk of contagion. Secondly, even for the recorded cases of a disease, quickly consolidating all local information to detect disease propagation over a large area is nontrivial using today's technology. Finally, all existing methods of outbreak detection identifies a single day of outbreak which is less realistic considering that outbreak happens over a period of time. In this paper, we introduce a wearable sensor based mobile application to capture early symptoms of a disease and to ensure faster consolidation of isolated cases over large areas. We then apply a purely novel technique based on discrepancy scores to detect disease outbreak-period. Experiments and prototypes show the usability and efficiency of our solution.