01-forecasting.md

title	image	projects	publications
Infectious Disease Forecasting	/images/research/map-all-narrow-zoom.jpg	reichlab/covid19-forecast-hub dsheldon/covid FluSightNetwork/cdc-flusight-ensemble tomcm39/adaptively_stacking_ensembles_for_infleunza_forecasting_with_incomplete_data reichlab/annual-predictions-paper reichlab/KCDE reichlab/2017-2018-cdc-flu-contest reichlab/dengue-thailand-2014-forecasts aaronger/utility-eval-papers reichlab/covid-hosp-forecasts-with-cases	forecasting

Our team is working to develop statistical methods and tools that can improve real-time infectious disease forecasting efforts for a variety of diseases, including COVID-19, dengue fever and influenza. We focus on building robust and interpretable models that can be used by public health officials to drive policy decisions. For example, in a collaboration with the Ministry of Public Health in Thailand, our team built statistical forecast models to predict outbreaks of dengue fever in real-time from early 2014 through 2019 for each of the 77 provinces in Thailand. We also have worked closely with the CDC to produce ensemble forecasts of influenza and COVID-19. From a methodological perspective we work on developing ensemble methodology (fusing together forecasts from multiple models) and on forecasting methods that can account for reporting delays in surveillance data.