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

Effect of superhydrophobic nanoplatelets on the phase behaviour of liquid crystals

We investigate the effect of superhydrophobic Perfluorinated Silica Nanoplatelets (PFSNP) on the Isotropic-Nematic-Smectic-A-Smectic-C phase transitions of the liquid crystalline compound 4-n-pentyloxyphenyl-4′-n-octyloxybenzoate by means of polarised optical microscopy, differential scanning calorimetry, x-ray scattering, and birefringence measurements. We measure the phase transition temperature shift, coexistence temperature span variation, and the orientational order parameter of the nanocomposites, as functions of the mass fraction of nanoplatelets. These variations are non-monotonic function of PFSNP mass fraction and they are definitely related among them with linear laws. We show that the non-monotony may be related with PFSNP dispersion quality, partial-aggregation phenomena of PFSNP, and/or to nanoplatelet organization. The isotropic-nematic transition becomes softer in presence of the nanoplatelets. Nematic and smectic-C phase temperature span is practically independent from the PFSNP volume fraction. In contrary, the smectic-A phase is further stabilized for its temperature window becomes much wider in the presence of the PFSNP. A phenomenological model accounts for the experimental observations. © 2019 Elsevier B.V