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

Global neuropathologic severity of Alzheimer’s disease and locus coeruleus vulnerability influences plasma phosphorylated tau levels

Abstract Background Advances in ultrasensitive detection of phosphorylated tau (p-tau) in plasma has enabled the use of blood tests to measure Alzheimer’s disease (AD) biomarker changes. Examination of postmortem brains of participants with antemortem plasma p-tau levels remains critical to understanding comorbid and AD-specific contribution to these biomarker changes. Methods We analyzed 35 population-based Mayo Clinic Study of Aging participants with plasma p-tau at threonine 181 and threonine 217 (p-tau181, p-tau217) available within 3 years of death. Autopsied participants included cognitively unimpaired, mild cognitive impairment, AD dementia, and non-AD neurodegenerative disorders. Global neuropathologic scales of tau, amyloid-β, TDP-43, and cerebrovascular disease were examined. Regional digital pathology measures of tau (phosphorylated threonine 181 and 217 [pT181, pT217]) and amyloid-β (6F/3D) were quantified in hippocampus and parietal cortex. Neurotransmitter hubs reported to influence development of tangles (nucleus basalis of Meynert) and amyloid-β plaques (locus coeruleus) were evaluated. Results The strongest regional associations were with parietal cortex for tau burden (p-tau181 R = 0.55, p = 0.003; p-tau217 R = 0.66, p < 0.001) and amyloid-β burden (p-tau181 R = 0.59, p < 0.001; p-tau217 R = 0.71, p < 0.001). Linear regression analysis of global neuropathologic scales explained 31% of variability in plasma p-tau181 (Adj. R2 = 0.31) and 59% in plasma p-tau217 (Adj. R2 = 0.59). Neither TDP-43 nor cerebrovascular disease global scales independently contributed to variability. Global scales of tau pathology (β-coefficient = 0.060, p = 0.016) and amyloid-β pathology (β-coefficient = 0.080, p < 0.001) independently predicted plasma p-tau217 when modeled together with co-pathologies, but only amyloid-β (β-coefficient = 0.33, p = 0.021) significantly predicted plasma p-tau181. While nucleus basalis of Meynert neuron count/mm2 was not associated with plasma p-tau levels, a lower locus coeruleus neuron count/mm2 was associated with higher plasma p-tau181 (R = -0.50, p = 0.007) and higher plasma p-tau217 (R = -0.55, p = 0.002). Cognitive scores (Adj. R2 = 0.25–0.32) were predicted by the global tau scale, but not by the global amyloid-β scale or plasma p-tau when modeled simultaneously. Conclusions Higher soluble plasma p-tau levels may be the result of an intersection between insoluble deposits of amyloid-β and tau accumulation in brain, and may be associated with locus coeruleus degeneration

A conserved face of the Jagged/Serrate DSL domain is involved in Notch trans-activation and cis-inhibition.

The Notch receptor and its ligands are key components in a core metazoan signaling pathway that regulates the spatial patterning, timing and outcome of many cell-fate decisions. Ligands contain a disulfide-rich Delta/Serrate/LAG-2 (DSL) domain required for Notch trans-activation or cis-inhibition. Here we report the X-ray structure of a receptor binding region of a Notch ligand, the DSL-EGF3 domains of human Jagged-1 (J-1(DSL-EGF3)). The structure reveals a highly conserved face of the DSL domain, and we show, by functional analysis of Drosophila melanogster ligand mutants, that this surface is required for both cis- and trans-regulatory interactions with Notch. We also identify, using NMR, a surface of Notch-1 involved in J-1(DSL-EGF3) binding. Our data imply that cis- and trans-regulation may occur through the formation of structurally distinct complexes that, unexpectedly, involve the same surfaces on both ligand and receptor