Nowcasting the 2022 mpox outbreak in England.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
09 2023
Historique:
received: 17 02 2023
accepted: 25 08 2023
revised: 28 09 2023
medline: 2 10 2023
pubmed: 18 9 2023
entrez: 18 9 2023
Statut: epublish

Résumé

In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact networks between gay, bisexual and other men who have sex with men. Following the COVID-19 pandemic, local health systems were strained, and therefore effective surveillance for mpox was essential for managing public health policy. However, the mpox outbreak in the UK was characterised by substantial delays in the reporting of the symptom onset date and specimen collection date for confirmed positive cases. These delays led to substantial backfilling in the epidemic curve, making it challenging to interpret the epidemic trajectory in real-time. Many nowcasting models exist to tackle this challenge in epidemiological data, but these lacked sufficient flexibility. We have developed a nowcasting model using generalised additive models that makes novel use of individual-level patient data to correct the mpox epidemic curve in England. The aim of this model is to correct for backfilling in the epidemic curve and provide real-time characteristics of the state of the epidemic, including the real-time growth rate. This model benefited from close collaboration with individuals involved in collecting and processing the data, enabling temporal changes in the reporting structure to be built into the model, which improved the robustness of the nowcasts generated. The resulting model accurately captured the true shape of the epidemic curve in real time.

Identifiants

pubmed: 37721951
doi: 10.1371/journal.pcbi.1011463
pii: PCOMPBIOL-D-23-00266
pmc: PMC10538717
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1011463

Subventions

Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 210758/Z/18/Z
Pays : United Kingdom

Informations de copyright

Copyright: © 2023 Overton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

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Auteurs

Christopher E Overton (CE)

Department of Mathematical Sciences, University of Liverpool, Liverpool, United Kingdom.
UK Health Security Agency, Data Science and Analytics, London, United Kingdom.
Department of Mathematics, University of Manchester, Manchester, United Kingdom.

Sam Abbott (S)

London School of Hygiene and Tropical Medicine, London, United Kingdom.

Rachel Christie (R)

UK Health Security Agency, Data Science and Analytics, London, United Kingdom.

Fergus Cumming (F)

UK Health Security Agency, Data Science and Analytics, London, United Kingdom.

Julie Day (J)

UK Health Security Agency, Data Science and Analytics, London, United Kingdom.

Owen Jones (O)

UK Health Security Agency, Data Science and Analytics, London, United Kingdom.

Rob Paton (R)

UK Health Security Agency, Data Science and Analytics, London, United Kingdom.

Charlie Turner (C)

UK Health Security Agency, Mpox Data, Epi and Analytics Cell, London, United Kingdom.

Thomas Ward (T)

UK Health Security Agency, Data Science and Analytics, London, United Kingdom.

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