ArticleEuropean journal of operational research2023
Combining probabilistic forecasts of COVID-19 mortality in the United States.
Article in European journal of operational research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Evaluation of respiratory disease hospitalisation forecasts using synthetic outbreak data.Communications medicine · 2026Article
- Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology.BMC biology · 2025Article
- COVID-19 trends across borders: Identifying correlations among countries.The Journal of international medical research · 2024Article
- Characterising information gains and losses when collecting multiple epidemic model outputs.Epidemics · 2024Article
- Challenges of COVID-19 Case Forecasting in the US, 2020-2021.PLoS computational biology · 2024Article
- An analytical approach to evaluate the impact of age demographics in a pandemic.Stochastic environmental research and risk assessment : research journal · 2023Article
- Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations.eLife · 2023Article
- A Review of Hyperglycemia in COVID-19.Cureus · 2023Review
- Optimal timing of non-pharmaceutical interventions during an epidemic.European journal of operational research · 2023Article
- Dynamic planning of a two-dose vaccination campaign with uncertain supplies.European journal of operational research · 2023Article
- Introduction to the special issue on the role of operational research in future epidemics/ pandemics.European journal of operational research · 2023Article
- A stochastic inventory model of COVID-19 and robust, real-time identification of carriers at large and infection rate via asymptotic laws.European journal of operational research · 2023Article
- AI and data science for smart emergency, crisis and disaster resilience.International journal of data science and analytics · 2023Article
- National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021.Communications medicine · 2022Article
- The United States COVID-19 Forecast Hub dataset.Scientific data · 2022Article
- Enhancing resilience in construction against infectious diseases using stochastic multi-agent approach.Automation in construction · 2022Article
- Modelling the COVID-19 epidemic and the vaccination campaign in Italy by the SUIHTER model.Infectious Disease Modelling · 2022Article
- Interval forecasts of weekly incident and cumulative COVID-19 mortality in the United States: A comparison of combining methods.PloS one · 2022Article
- A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave.Nature communications · 2021Article
- Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.International journal of forecastingArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.