Nowcasting COVID-19 Deaths in England by Age and Region
| Autor: | Meaghan Kall, Shaun R. Seaman, Pantelis Samartsidis, Daniela De Angelis |
|---|---|
| Přispěvatelé: | Apollo - University of Cambridge Repository, Seaman, Shaun [0000-0003-3726-5937], De Angelis, Daniela [0000-0001-6619-6112] |
| Rok vydání: | 2022 |
| Předmět: |
Statistics and Probability
medicine.medical_specialty Coronavirus disease 2019 (COVID-19) Nowcasting Public health epidemic monitoring right‐truncation Geography generalised Dirichlet Pandemic reporting delay medicine Bayesian hierarchical modeling Statistics Probability and Uncertainty ORIGINAL ARTICLES ORIGINAL ARTICLE Demography |
| Popis: | Understanding the trajectory of the daily numbers of deaths in people with CoVID-19 is essential to decisions on the response to the CoVID-19 pandemic. Estimating this trajectory from data on numbers of deaths is complicated by the delay between deaths occurring and their being reported to the authorities. In England, Public Health England receives death reports from a number of sources and the reporting delay is typically several days, but can be several weeks. Delayed reporting results in considerable uncertainty about the number of deaths that occurred on the most recent days. In this article, we estimate the number of deaths per day in each of five age strata within seven English regions. We use a Bayesian hierarchical model that involves a submodel for the number of deaths per day and a submodel for the reporting delay distribution. This model accounts for reporting-day effects and longer-term changes over time in the delay distribution. We show how the model can be fitted in a computationally efficient way when the delay distribution is same in multiple strata, e.g. over a wide range of ages. |
| Databáze: | OpenAIRE |
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