Use of big data in computational epidemiology for public health surveillance

Abstract

All countries of the world face new epidemics every now and then, and so does India, but the slow rate at which our country is able to respond and contain these diseases, leaves our country's population shattered and victimized on massive scales while the world is harnessing novel technologies to process Big Data and give a tough time to such outbreaks. Though it is not very fair to compare India's novice disease surveillance programme IDSP (Integrated Disease Surveillance Programme) with America's 70-year-old programme CDC (Centers for Disease Control and Prevention), however, it is needless to say, there is a lot of scope to follow the latter. According to various global health indices, India stands as a non-performer in the field of health and needs to catch up fast. There is an urgent need for including unstructured sources of data like social media and carrying out opinion analysis while publishing national health and epidemic outbreak advisories in near future. What traditional case-based reporting may fail to accomplish in terms of efficiency and promptness of disease outbreak reporting may easily be achieved by harnessing hidden potential of health data generated online in large volumes and possessing greater dynamic. This paper experiments the amalgamation of traditional epidemiology and computational epidemiology proposing a new approach that should be tested for its mettle by Indian Epidemic Advisory bodies like IDSP and others.