Risk prediction system for dengue transmission based on high resolution weather data.

Background: Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks.

A Cloud-Based Infrastructure for Feedback-Driven Training and Image Recognition.

Advanced techniques in machine learning combined with scalable "cloud" computing infrastructure are driving the creation of new and innovative health diagnostic applications. We describe a service and application for performing image training and recognition, tailored to dermatology and melanoma identification. The system implements new machine learning approaches to provide a feedback-driven training loop.

Spatio-temporal information and knowledge representation of disease incidence and respective intervention strategies.

The supplementation of medical data with environmental data offers rich new insights that can improve decision-making within health systems and the healthcare profession. In this study, we simulate disease incidence for various scenarios using a mathematical model. We subsequently visualise the infectious disease spread in human populations over time and geographies.