Matteo Convertino smiling

Sick Time: Convertino Shares New Model for Predicting Seasonal Flu With CDC

									Charlie Plain |
																			September 7, 2016
Matteo Convertino smiling
Assistant Professor Matteo Convertino

Assistant Professor Matteo Convertino recently participated in a Centers for Disease Control and Prevention (CDC) meeting exploring influenza forecasting and new methods for predicting the disease’s incidence and spread in almost real-time.

The CDC organized the event, held Aug. 31-Sept. 1, 2016, to discuss how the work of computer modeling researchers, like Convertino, can help provide early insights into the timing, peak, and intensity of influenza seasons.

During the event, Convertino shared his team’s new approach to modeling seasonal influenza, which is based on reaction-diffusion processes and how factors leading to disease infections interact and spread the flu.

“We collaborated to understand how different models can be used together for making better forecasts and to share the science and the methods involved,” says Convertino. “Such forecasts are useful for deciding how severe a season is, the type and quantity of vaccine to produce, and where and when to start vaccination.”

Convertino was invited to the meeting after his research team scored highly in a CDC seasonal influenza prediction challenge held earlier this year. The top 13 teams from the challenge were selected to participate in the event.

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