Viral Predictor

Estimates probability that a child with diarrheal disease has a viral infection.

This software implements four models using machine learning methods to estimate the probability a child with diarrheal disease has a viral infection. The approach utilizes the symptoms of the current patients, an aggregation of clinical presentation of recent patients in the given location, weather events that are predictive of diarrheal disease outbreaks, and seasonality effects. The models are deployed differently depending on the region and country. Development of this software is collaborative between the University of Florida, University of Utah and Brown University (USA). Send questions of inquiry to [email protected].