Methods for real-time forecasting and inference during infectious disease outbreaks

PROJECT SUMMARY A fundamental challenge for the scientific community in the 21st century is learning how to turn this deluge of data into evidence that can inform decision-making about improving health and preventing illness at the individual and population levels. The maturing field of real-time infectious disease forecasting is a prime example of a research area with great potential for leveraging modern analytical methods to maximize the impact on public health. Infectious diseases exact an enormous toll on global health each year. Improved real- time forecasts of infectious disease outbreaks can inform targeted intervention and prevention strategies, such as planning for surge capacity, increasing healthcare staffing, and designing vaccine studies. However we currently have a limited understanding of the best ways to integrate these types of forecasts into real-time public health decision-making. The central research activities of this project are (1) to develop stand-alone and ensemble infectious disease models and methodologies that support forecasting and inference about outbreaks and (2) to expand our collaborative, online platform for collection, dissemination, evaluation, and synthesis of forecasts from different research teams. Additionally, we will continue to develop a suite of open- source educational modules to train researchers and public health officials in developing, validating, and implementing time-series forecasting, with a focus on real-time infectious disease applications.