Cargando…

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....

Descripción completa

Detalles Bibliográficos
Autores principales: Athayde, Gustavo M., Alencar, Airlane P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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
_version_ 1784765285624971264
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
work_keys_str_mv AT athaydegustavom forecastingcovid19intheunitedkingdomadynamicsirdmodel
AT alencarairlanep forecastingcovid19intheunitedkingdomadynamicsirdmodel