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Forecasting subnational COVID-19 mortality using a day-of-the-week adjusted Bayesian hierarchical model
Stat ; 10(1), 2021.
Article in English | Scopus | ID: covidwho-1598217
ABSTRACT
As of October 2020, the death toll from the COVID-19 pandemic has risen over 1.1 million deaths worldwide. Reliable estimates of mortality due to COVID-19 are important to guide intervention strategies such as lockdowns and social distancing measures. In this paper, we develop a data-driven model that accurately and consistently estimates COVID-19 mortality at the regional level early in the epidemic, using only daily mortality counts as the input. We use a Bayesian hierarchical skew-normal model with day-of-the-week parameters to provide accurate projections of COVID-19 mortality. We validate our projections by comparing our model to the projections made by the Institute for Health Metrics and Evaluation and highlight the importance of hierarchicalization and day-of-the-week effect estimation. © 2020 John Wiley & Sons, Ltd.

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: Stat Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Experimental Studies / Prognostic study Language: English Journal: Stat Year: 2021 Document Type: Article