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The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Instability of the knee. A diagnostic study

The purpose of this thesis is to improve our insight into the diagnosis of chronic posttraumatic instability of the knee. The mechanics of the knee-joint are essential in this respect. Insight into the functioning of the knee-joint improves our understanding of functional disorders of the knee. The diagnosis of chronic instability of the knee ligaments remains difficult. In most cases an accurate diagnosis can only be made on the basis of a combination of several methods of investigation. ... Zie: Summary and conclusions

Case study of wave breaking with high-resolution turbulence measurements with LITOS and WRF simulations

Measurements of turbulent energy dissipation rates obtained from wind fluctuations observed with the balloon-borne instrument LITOS (Leibniz-Institute Turbulence Observations in the Stratosphere) are combined with simulations with the Weather Research and Forecasting (WRF) model to study the breakdown of waves into turbulence. One flight from Kiruna (68° N, 21° E) and two flights from Kühlungsborn (54° N, 12° E) are analysed. Dissipation rates are of the order of 0. 1 mW kg−1 (∼ 0.01 K d−1) in the troposphere and in the stratosphere below 15 km, increasing in distinct layers by about 2 orders of magnitude. For one flight covering the stratosphere up to ∼ 28 km, the measurement shows nearly no turbulence at all above 15 km. Another flight features a patch with highly increased dissipation directly below the tropopause, collocated with strong wind shear and wave filtering conditions. In general, small or even negative Richardson numbers are affirmed to be a sufficient condition for increased dissipation. Conversely, significant turbulence has also been observed in the lower stratosphere under stable conditions. Observed energy dissipation rates are related to wave patterns visible in the modelled vertical winds. In particular, the drop in turbulent fraction at 15 km mentioned above coincides with a drop in amplitude in the wave patterns visible in the WRF. This indicates wave saturation being visible in the LITOS turbulence data