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

Kunnen we een betere of een andere participatiewet maken?

Na de treurige berichten over de vernederende en zinloze ‘tegenprestaties’ die van werklozen worden verlangd in Amsterdam, weten Thomas Kampen en Evelien Tonkens de discussie een constructieve wending te geven. Guido Walraven denkt graag mee

Wmo en wijkteams: 'T-shaped professionals': samenwerking in de wijk en de redzame burger

Is er een combinatie nodig van T-shaped professional en Reflective Practioner

Werken aan een Lief & Leedcultuur en duurzaam sociaal werk

Er zit nog veel meer in, columns en interview

Duurzame resultaten van sociale innovatie en het perspectief van de rechtvaardige(r) stad: Nabeschouwing

Volg

Donald Schön: Reflective Practitioner

Hoofdstuk 38 uit boek: Er is veel geschreven over leren, maar goede en toegankelijke overzichtswerken zijn er nauwelijks. Een basiswerk over leren in en om organisaties ontbrak al helemaal. Tot nu toe, want die constatering bracht Manon Ruijters en Robert-Jan Simons ertoe een basisboek te maken met 50 gezichtsbepalende concepten rond leren en ontwikkelen: 'Canon van het leren'. De ambitieuze auteurs kregen hulp van ruim vijftig auteurs, mensen uit wetenschap en praktijk, zodat er 50 toelichtingen op belangrijke concepten en hun grondleggers ontstonden. Deze auteurs brengen de theorieën niet alleen helder en zonder overbodige poespas over, maar delen hun persoonlijke fascinatie, ervaring en kritische reflecties

Mentortraining Diversiteit

Hoofdstuk (65-67) in DIVERS: slotpublicatie van het ZonMw-programma Diversiteit en Jeugdbelei

Coöperatieve gebiedsontwikkeling en sociale innovatie in lerend perspectief: het begin van iets groots?

Samenvatting volg