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STATISTICAL METHODS FOR REAL-TIME FORECASTS OF INFECTIOUS DISEASE: DYNAMIC TIME-SERIES AND MACHINE LEARNING APPROACHES

Abstract

The past decade of biomedical research has borne witness to rapid growth in data and computational methods. 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 emerging 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 increased healthcare staffing or vector control measures. 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 and validate a suite of robust, real-time statistical prediction models for infectious diseases, (2) we will develop and evaluate an ensemble time-series prediction methodology for integrating multiple prediction models into a single forecast, and (3) to develop a collaborative platform for dissemination and evaluation of predictions by different research teams. Additionally, we will 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.

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Funding Source

Project Period

2016-2021