Case Study: Kinsa Predicts Flu Spread with Expero’s AI Forecasting Platform

Kinsa - Customer Case Study

Kinsa needed a better way to predict and plan for flu outbreaks. Traditional CDC reporting is delayed and based on limited clinic data, making it difficult to know where illness is spreading in real time or how to prepare inventory for vaccines and flu treatments. Expero helped address this challenge with machine learning and deep learning services, along with data science support.

Expero built a spatiotemporal forecasting model to predict the spread of influenza-like illness across the United States, using Kinsa’s real-time smart thermometer data and external factors. The team also created a recursive temporal validation suite and a cloud-based ML Ops system that automatically retrains and deploys models when data changes. The result was a long-term forecasting solution with demonstrable 90%+ accuracy in temporal validation, delivered in 3 weeks for the initial model and 3 months for the full production system.

Kinsa