Sureshkumar Rajasekar

Advancements in technology, data and analytics are enabling predictive modeling and forecasting to help improve health outcomes. Similar to how weather forecasting proves to be very useful to prepare for uncongenial weather conditions, disease forecasting can be used to mobilize, allocate and manage resources as well as inform the general population of the impending onset of a seasonal disease, such as the flu, in a timely manner. In the coming years, disease forecasting is positioned to become more accurate and meaningful, with richer and better sources of data from wearable devices, improved ambient computing, more readily available gene data etc. Forecasting methodologies and data analytics models are also advancing, with ongoing innovations around recurrent neural networks, compared to traditional statistical techniques. All of this is powered by the awareness of populations for more accurate information on seasonal and chronic diseases.