Case Study: Kinsa achieves more accurate flu forecasting with Expero’s predictive analytics platform

Kinsa builds 90%+ accurate flu forecasting in 3 months with Expero

Kinsa, a health technology company, faced the significant challenge of predicting and planning for the spread of influenza-like illness (ILI). Accurately forecasting future outbreaks is extremely difficult due to the complex factors affecting disease spread, and existing methods were often slow and retroactive. This made it hard for health providers to manage perishable and expensive vaccine and treatment inventories, leading to shortages. Expero was engaged to build a predictive data product to address this challenge.

Expero implemented a cutting-edge deep learning model to create a long-term forecast for ILI spread across the United States. The solution included a full ML ops production pipeline that automatically retrains and deploys models when data patterns change. This system achieved a demonstrable accuracy of over 90% in temporal validation, enabling Kinsa to provide health clinics and hospitals with faster, more accurate forecasts. This allows them to optimize inventory, avoid overstocking to save operating capital, and ensure reliable treatment is available.

Kinsa