{"id":849016,"date":"2022-06-01T00:16:36","date_gmt":"2022-06-01T07:16:36","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-06-01T00:44:28","modified_gmt":"2022-06-01T07:44:28","slug":"evaluation-of-individual-and-ensemble-probabilistic-forecasts-of-covid-19-mortality-in-the-us-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/evaluation-of-individual-and-ensemble-probabilistic-forecasts-of-covid-19-mortality-in-the-us-2\/","title":{"rendered":"Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US"},"content":{"rendered":"<p>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 (<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/covid19forecasthub.org\/\">https:\/\/covid19forecasthub.org\/<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>) 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 na\u00efve 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 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Brennen","user_id":0,"rest_url":false},{"type":"text","value":"[....]","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Chaozhuo Li","user_id":39054,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Chaozhuo Li"},{"type":"user_nicename","value":"Xing Xie","user_id":34906,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Xing 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