The Case of Human Plurality: Hannah Arendt\u27s Critique of Individualism in Enlightenment and Romantic Thinking

The theme of this thesis is Hannah Arendt’s critique and ultimate rejection of the ideas of individualism developed during the Enlightenment and the Romantic periods. She rejects the Enlightenment notion of the “abstract man,” but equally rejects the notion of Romantic introspection that followed. Such a critique is important to Arendt because she makes human plurality the center for her entire system of thought. Using the French Revolution, Jewish history, and totalitarianism as her examples, Arendt explains the effects of such overtly individualistic thinking in both society and politics. The goal of this thesis is not a comprehensive look at the vast number of theories developed during the Enlightenment and Romantic periods. That is far beyond its intended scope. The goal, instead, is to show how Arendt used her critique of a select number of ideas to further define and clarify her own thoughts. In the end it will be shown that while Arendt ranged all over in her thinking (from history to politics to philosophy) she engaged these topics in a systematic way as to explore the affinities and contradictions to human plurality in whatever she studied. She is drawn to the late 18th/early 19th centuries precisely because she envisions it as a watershed moment in Western conceptions of individuality, one that stamped out all thought of human plurality. Arendt wants to rescue the notion of human plurality and elevate it to a primal position in Western thought

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

Establishing core outcome domains in pediatric kidney disease: report of the Standardized Outcomes in Nephrology—Children and Adolescents (SONG-KIDS) consensus workshops

Trials in children with chronic kidney disease do not consistently report outcomes that are critically important to patients and caregivers. This can diminish the relevance and reliability of evidence for decision making, limiting the implementation of results into practice and policy. As part of the Standardized Outcomes in Nephrology—Children and Adolescents (SONG-Kids) initiative, we convened 2 consensus workshops in San Diego, California (7 patients, 24 caregivers, 43 health professionals) and Melbourne, Australia (7 patients, 23 caregivers, 49 health professionals). This report summarizes the discussions on the identification and implementation of the SONG-Kids core outcomes set. Four themes were identified; survival and life participation are common high priority goals, capturing the whole child and family, ensuring broad relevance across the patient journey, and requiring feasible and valid measures. Stakeholders supported the inclusion of mortality, infection, life participation, and kidney function as the core outcomes domains for children with chronic kidney disease

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