You and Your Neighbor

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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

Behavioral evidence for a role of α-gustducin in glutamate taste

The taste perception of monosodium glutamate (MSG) is termed \u27umami\u27. Two putative taste receptors for glutamate have been identified, a truncated form of mGluR4 (taste-mGluR4) and the presumed heterodimer T1R1 + T1R3. Both receptors respond to glutamate when expressed in heterologous cells, but the G protein involved is not known. Gα-Gustducin mediates the transduction of several bitter and sweet compounds; however, its role in umami has not been determined. We used standard two-bottle preference tests on α-gustducin knockout (KO) and wildtype (WT) mice to compare preferences for ascending concentrations of MSG and MSG + 5′-inosine monophosphate (IMP). A Latin Square was used to assign the order of tastants presented to each mouse. Statistical comparisons between KO and WT mice revealed that whereas WT mice preferred solutions of MSG and MSG + IMP over water, KO mice showed little preference for these stimuli. Denatonium and sucrose served as control stimuli and, as shown previously, WT mice prefered sucrose and avoided denatonium significantly more than did KO mice. Naïve mice were also tested, and while prior exposure to taste stimuli influenced the magnitude of the preferences, experience did not change the overall pattern of intake. These data suggest that α -gustducin plays a role in glutamate taste. © Oxford University Press 2003; all rights reserved

Fell-Muir Lecture: Fibrillin microfibrils: structural tensometers of elastic tissues?

Fibrillin microfibrils are indispensable structural elements of connective tissues in multicellular organisms from early metazoans to man. They have an extensible periodic beaded organisation, and support dynamic tissues such as ciliary zonules that suspend the lens. In tissues that express elastin, including blood vessels, skin and lungs, microfibrils support elastin deposition and shape the functional architecture of elastic fibres. The vital contribution of microfibrils to tissue form and function is underscored by the heritable fibrillinopathies, especially Marfan syndrome with severe elastic, ocular and skeletal tissue defects. Research since the early 1990s has advanced our knowledge of biology of microfibrils, yet understanding of their mechanical and homeostatic contributions to tissues remain far from complete. This review is a personal reflection on key insights, and puts forward the conceptual hypothesis that microfibrils are structural ‘tensometers’ that direct cells to monitor and respond to altered tissue mechanics