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 Review of Current Methods in Microfluidic Device Fabrication and Future Commercialization Prospects

Microfluidic devices currently play an important role in many biological, chemical, and engineering applications, and there are many ways to fabricate the necessary channel and feature dimensions. In this review, we provide an overview of microfabrication techniques that are relevant to both research and commercial use. A special emphasis on both the most practical and the recently developed methods for microfluidic device fabrication is applied, and it leads us to specifically address laminate, molding, 3D printing, and high resolution nanofabrication techniques. The methods are compared for their relative costs and benefits, with special attention paid to the commercialization prospects of the various technologies

Open Source Software for Evaluation of Applications and Traffic Measurement in an Experimental Testbed for Converged Networks

In this paper we present the development and validation of an experimental platform based in open source software and the development of a package for monitoring purposes. The software package with its components is described and validated through a group of tests and examples. These results are the initial support for future research which includes new ideas in the NGN (Next Generation Networks) topics, such as QoS (Quality of Service) mechanisms, traffic characterization and MPLS (Multi-protocol Label Switching) hybrid routing. The procedures shown in this paper may give other research groups an overview of the primary steps for the implementation of a research lab with similar interests. Keywords- convergence, networks, QoS monitoring, MOS, traffic characterization, traffic measurement

Structural properties of undoped and doped cubic GaN grown on SiC(001)

International audienc