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

A Hierarchical Decision-Making Method with a Fuzzy Ant Colony Algorithm for Mission Planning of Multiple UAVs

Unmanned aerial vehicles (UAVs) received an unprecedented surge of people’s interest worldwide in recent years. This paper investigates the specific problem of cooperative mission planning for multiple UAVs on the battlefield from a hierarchical decision-making perspective. From the view of the actual mission planning issue, the two key problems to be solved in UAV collaborative mission planning are mission allocation and route planning. In this paper, both of these problems are taken into account via a hierarchical decision-making model. Firstly, we use a target clustering algorithm to divide the original targets into target subgroups, where each target subgroup contains multiple targets. Secondly, a fuzzy ant colony algorithm is used to calculate the global path between target subgroups for a single-target group. Thirdly, a fuzzy ant colony algorithm is also used to calculate the local path between multiple targets for a single-target subgroup. After three levels of decision-making, the complete path for multiple UAVs can be obtained. In order to improve the efficiency of a collaborative task between different types of UAVs, a cooperative communication strategy is developed, which can reduce the number of UAVs performing tasks. Finally, experimental results demonstrate the effectiveness of the proposed cooperative mission planning and cooperative communication strategy for multiple UAVs

Facile Cu(I)-Catalyzed Oxidative Coupling of Anilines to Azo Compounds and Hydrazines with Diaziridinone under Mild Conditions

A mild and highly efficient Cu(I)-catalyzed oxidative coupling of anilines is described. Various primary and secondary anilines can be efficiently coupled under mild conditions to the corresponding azo compounds and hydrazines in high yields. This method provides a direct and practical access to these compounds and is also amenable to gram scale with no special precautions to exclude air or moisture

Chiral Phosphine Oxide–Sc(OTf)₃ Complex Catalyzed Enantioselective Bromoaminocyclization of 2‑Benzofuranylmethyl <i>N</i>‑Tosylcarbamates. Approach to a Novel Class of Optically Active Spiro Compounds

An efficient enantioselective bromoaminocyclization of 2-benzofuranylmethyl <i>N</i>-tosylcarbamates catalyzed by a chiral phosphine oxide–Sc­(OTf)₃ complex is described. A wide variety of optically active spiro benzofuran oxazolidinones can be obtained with high enantioselectivities