Pennsylvania Folklife Vol. 13, No. 3

• Piece-Patch Artistry • The Horse and Buggy Dutch • Pine Tar and its Uses • Much Ado About Cookies • Folk Festival Program • Outdoor Privies in the Dutch Country • Distillation and Distilleries Among the Dutch • The Folklife Studies Movementhttps://digitalcommons.ursinus.edu/pafolklifemag/1015/thumbnail.jp

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

The unexpected discovery of a novel low-oxygen-activated locus for the anoxic persistence of Burkholderia cenocepacia

Burkholderia cenocepacia is a Gram-negative aerobic bacterium that belongs to a group of opportunistic pathogens displaying diverse environmental and pathogenic lifestyles. B. cenocepacia is known for its ability to cause lung infections in people with cystic fibrosis and it possesses a large 8 Mb multireplicon genome encoding a wide array of pathogenicity and fitness genes. Transcriptomic profiling across nine growth conditions was performed to identify the global gene expression changes made when B. cenocepacia changes niches from an environmental lifestyle to infection. In comparison to exponential growth, the results demonstrated that B. cenocepacia changes expression of over one-quarter of its genome during conditions of growth arrest, stationary phase and surprisingly, under reduced oxygen concentrations (6% instead of 20.9% normal atmospheric conditions). Multiple virulence factors are upregulated during these growth arrest conditions. A unique discovery from the comparative expression analysis was the identification of a distinct, co-regulated 50-gene cluster that was significantly upregulated during growth under low oxygen conditions. This gene cluster was designated the low-oxygen-activated (lxa) locus and encodes six universal stress proteins and proteins predicted to be involved in metabolism, transport, electron transfer and regulation. Deletion of the lxa locus resulted in B. cenocepacia mutants with aerobic growth deficiencies in minimal medium and compromised viability after prolonged incubation in the absence of oxygen. In summary, transcriptomic profiling of B. cenocepacia revealed an unexpected ability of aerobic Burkholderia to persist in the absence of oxygen and identified the novel lxa locus as key determinant of this important ecophysiological trait