a


Don’t _miss

Wire Festival

 

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam blandit hendrerit faucibus turpis dui.

<We_can_help/>

What are you looking for?

<Good_things_happen/> Welcome to Conference

Copyright Munich Datageeks e.V.

Follow us

>Nicholas Reich
speaker_info

About The Speaker

Nicholas Reich

The Computational Science of Real-time Influenza Forecasting - Seasonal influenza outbreaks cause substantial annual morbidity and mortality worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate real-time data and advanced computational methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized real-time seasonal influenza forecasting challenges since 2013. This talk will summarize the recent collaborative efforts by the FluSight Network consortium to improve real-time forecast accuracy through multi-model ensemble forecasting. We will discuss the process of building and evaluating ensemble approaches, each of which combines over 20 separate component model predictions into a single probabilistic forecast. During the Northern hemisphere winter influenza season, the forecasts from the FluSight Network are publicly reported by the CDC as part of an official weekly communication to inform health practitioners about seasonal influenza trends. Current and past forecasts are available via an interactive data visualization at http://flusightnetwork.io. This talk will highlight the interdisciplinary challenges in designing, implementing, evaluating, and communicating forecasts in a public health context.

 The Computational Science of Real-time Influenza Forecasting

Seasonal influenza outbreaks cause substantial annual morbidity and mortality worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate real-time data and advanced computational methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized real-time seasonal influenza forecasting challenges since 2013. This talk will summarize the recent collaborative efforts by the FluSight Network consortium to improve real-time forecast accuracy through multi-model ensemble forecasting. We will discuss the process of building and evaluating ensemble approaches, each of which combines over 20 separate component model predictions into a single probabilistic forecast. During the Northern hemisphere winter influenza season, the forecasts from the FluSight Network are publicly reported by the CDC as part of an official weekly communication to inform health practitioners about seasonal influenza trends. Current and past forecasts are available via an interactive data visualization at http://flusightnetwork.io. This talk will highlight the interdisciplinary challenges in designing, implementing, evaluating, and communicating forecasts in a public health context.

 

Nicholas G Reich is an Associate Professor of Biostatistics at the University of Massachusetts – Amherst. His research group, the Reich Lab (https://reichlab.io), focuses on developing statistical models for infectious disease dynamics. To forecast outbreaks of diverse infectious diseases (including dengue fever and influenza) and to assist with other outbreak response efforts, his team has developed computational methods and open-source tools for analyzing real-time infectious disease surveillance data. Dr. Reich is the director of an Influenza Forecasting Center of Excellence, funded by the U.S. Centers for Disease Control and Prevention (CDC). For the 2019/2020 academic year, Dr. Reich is a visiting scholar at the LMU-Munich Department of Statistics, supported by an Alexander von Humboldt Fellowship.