Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, determinist...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autor principal:	Kris V Parag
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/cf1e29876a2d4e0a90b71d5341176ed3
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:cf1e29876a2d4e0a90b71d5341176ed3
record_format	dspace
spelling	oai:doaj.org-article:cf1e29876a2d4e0a90b71d5341176ed32021-12-02T19:57:50ZImproved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.1553-734X1553-735810.1371/journal.pcbi.1009347https://doaj.org/article/cf1e29876a2d4e0a90b71d5341176ed32021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009347https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.Kris V ParagPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009347 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Kris V Parag Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.
description	We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.
format	article
author	Kris V Parag
author_facet	Kris V Parag
author_sort	Kris V Parag
title	Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.
title_short	Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.
title_full	Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.
title_fullStr	Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.
title_full_unstemmed	Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.
title_sort	improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/cf1e29876a2d4e0a90b71d5341176ed3
work_keys_str_mv	AT krisvparag improvedestimationoftimevaryingreproductionnumbersatlowcaseincidenceandbetweenepidemicwaves
_version_	1718375805943283712

Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.

Ejemplares similares