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.
A fundamental challenge for the scientific community in the 21st century is learning how to turn data into evidence that can inform decision-making about improving health and preventing illness at the individual and population levels. Real-time infectious disease forecasting is a prime example of a field with great potential for leveraging modern analytical methods to maximize the impact public health. The goal of the proposed research is to develop statistical modeling frameworks for making forecasts of infectious diseases in real-time and integrating these forecasts into public health decision making.
|Ray, Evan L; Reich, Nicholas G (2018) Prediction of infectious disease epidemics via weighted density ensembles. PLoS Comput Biol 14:e1005910|
|Reich, Nicholas G; Lessler, Justin; Varma, Jay K et al. (2018) Quantifying the Risk and Cost of Active Monitoring for Infectious Diseases. Sci Rep 8:1093|
|Lauer, Stephen A; Sakrejda, Krzysztof; Ray, Evan L et al. (2018) Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010-2014. Proc Natl Acad Sci U S A 115:E2175-E2182|
|Tushar, Abhinav; Reich, Nicholas G (2017) flusight: interactive visualizations for infectious disease forecasts. J Open Source Softw 2:|