Combining probabilistic forecasts of COVID-19 mortality in the United States

• Published in: European journal of operational research Vol. 304; no. 1; pp. 25 - 41
• Main Authors: Taylor, James W., Taylor, Kathryn S.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2023
• Subjects: COVID-19; Distributional forecasts; Forecast combining; Interval forecasts; OR in health services; COVID-19; Forecast combining; Interval forecasts; Distributional forecasts; OR in health services
• ISSN: 0377-2217; 1872-6860; 0377-2217
• DOI: 10.1016/j.ejor.2021.06.044

•Interval and distributional forecast combinations at the state and national level.•Combining considered with frequent entry and exit of forecasting teams.•Weighted combining proposed, based on inverse of interval and distribution scores.•For early periods, the median of the available forecasts was most effective.•For later periods, weighting the forecasts was the most accurate combination. The COVID-19 pandemic has placed forecasting models at the forefront of health policy making. Predictions of mortality, cases and hospitalisations help governments meet planning and resource allocation challenges. In this paper, we consider the weekly forecasting of the cumulative mortality due to COVID-19 at the national and state level in the U.S. Optimal decision-making requires a forecast of a probability distribution, rather than just a single point forecast. Interval forecasts are also important, as they can support decision making and provide situational awareness. We consider the case where probabilistic forecasts have been provided by multiple forecasting teams, and we combine the forecasts to extract the wisdom of the crowd. We use a dataset that has been made publicly available from the COVID-19 Forecast Hub. A notable feature of the dataset is that the availability of forecasts from participating teams varies greatly across the 40 weeks in our study. We evaluate the accuracy of combining methods that have been previously proposed for interval forecasts and predictions of probability distributions. These include the use of the simple average, the median, and trimming methods. In addition, we propose several new weighted combining methods. Our results show that, although the median was very useful for the early weeks of the pandemic, the simple average was preferable thereafter, and that, as a history of forecast accuracy accumulates, the best results can be produced by a weighted combining method that uses weights that are inversely proportional to the historical accuracy of the individual forecasting teams.