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Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model
Making use of a state space framework, we present a stochastic generalization of the SIRD model, where the mortality, infection, and underreporting rates change over time. A new format to the errors in the Susceptible-Infected-Recovered-Dead compartments is also presented, that permits reinfection....
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9365164/ https://www.ncbi.nlm.nih.gov/pubmed/35947603 http://dx.doi.org/10.1371/journal.pone.0271577 |
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author | Athayde, Gustavo M. Alencar, Airlane P. |
author_facet | Athayde, Gustavo M. Alencar, Airlane P. |
author_sort | Athayde, Gustavo M. |
collection | PubMed |
description | Making use of a state space framework, we present a stochastic generalization of the SIRD model, where the mortality, infection, and underreporting rates change over time. A new format to the errors in the Susceptible-Infected-Recovered-Dead compartments is also presented, that permits reinfection. The estimated trajectories and (out-of-sample) forecasts of all these variables are presented with their confidence intervals. The model only uses as inputs the number of reported cases and deaths, and was applied for the UK from April, 2020 to Sep, 2021 (daily data). The estimated infection rate has shown a trajectory in waves very compatible with the emergence of new variants and adopted social measures. The estimated mortality rate has shown a significant descendant behaviour in 2021, which we attribute to the vaccination program, and the estimated underreporting rate has been considerably volatile, with a downward tendency, implying that, on average, more people are testing than in the beginning of the pandemic. The evolution of the proportions of the population divided into susceptible, infected, recovered and dead groups are also shown with their confidence intervals and forecast, along with an estimation of the amount of reinfection that, according to our model, has become quite significant in 2021. Finally, the estimated trajectory of the effective reproduction rate has proven to be very compatible with the real number of cases and deaths. Its forecasts with confident intervals are also presented. |
format | Online Article Text |
id | pubmed-9365164 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93651642022-08-11 Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model Athayde, Gustavo M. Alencar, Airlane P. PLoS One Research Article Making use of a state space framework, we present a stochastic generalization of the SIRD model, where the mortality, infection, and underreporting rates change over time. A new format to the errors in the Susceptible-Infected-Recovered-Dead compartments is also presented, that permits reinfection. The estimated trajectories and (out-of-sample) forecasts of all these variables are presented with their confidence intervals. The model only uses as inputs the number of reported cases and deaths, and was applied for the UK from April, 2020 to Sep, 2021 (daily data). The estimated infection rate has shown a trajectory in waves very compatible with the emergence of new variants and adopted social measures. The estimated mortality rate has shown a significant descendant behaviour in 2021, which we attribute to the vaccination program, and the estimated underreporting rate has been considerably volatile, with a downward tendency, implying that, on average, more people are testing than in the beginning of the pandemic. The evolution of the proportions of the population divided into susceptible, infected, recovered and dead groups are also shown with their confidence intervals and forecast, along with an estimation of the amount of reinfection that, according to our model, has become quite significant in 2021. Finally, the estimated trajectory of the effective reproduction rate has proven to be very compatible with the real number of cases and deaths. Its forecasts with confident intervals are also presented. Public Library of Science 2022-08-10 /pmc/articles/PMC9365164/ /pubmed/35947603 http://dx.doi.org/10.1371/journal.pone.0271577 Text en © 2022 Athayde, Alencar https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Athayde, Gustavo M. Alencar, Airlane P. Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model |
title | Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model |
title_full | Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model |
title_fullStr | Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model |
title_full_unstemmed | Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model |
title_short | Forecasting Covid-19 in the United Kingdom: A dynamic SIRD model |
title_sort | forecasting covid-19 in the united kingdom: a dynamic sird model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9365164/ https://www.ncbi.nlm.nih.gov/pubmed/35947603 http://dx.doi.org/10.1371/journal.pone.0271577 |
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