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Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease

The rapid detection of outbreaks is a key step in the effective control and containment of infectious diseases. In particular, the identification of cases which might be epidemiologically linked is crucial in directing outbreak-containment efforts and shaping the intervention of public health author...

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Autores principales: Cavallaro, Massimo, Coelho, Juliana, Ready, Derren, Decraene, Valerie, Lamagni, Theresa, McCarthy, Noel D., Todkill, Dan, Keeling, Matt J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744322/
https://www.ncbi.nlm.nih.gov/pubmed/36449515
http://dx.doi.org/10.1371/journal.pcbi.1010726
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author Cavallaro, Massimo
Coelho, Juliana
Ready, Derren
Decraene, Valerie
Lamagni, Theresa
McCarthy, Noel D.
Todkill, Dan
Keeling, Matt J.
author_facet Cavallaro, Massimo
Coelho, Juliana
Ready, Derren
Decraene, Valerie
Lamagni, Theresa
McCarthy, Noel D.
Todkill, Dan
Keeling, Matt J.
author_sort Cavallaro, Massimo
collection PubMed
description The rapid detection of outbreaks is a key step in the effective control and containment of infectious diseases. In particular, the identification of cases which might be epidemiologically linked is crucial in directing outbreak-containment efforts and shaping the intervention of public health authorities. Often this requires the detection of clusters of cases whose numbers exceed those expected by a background of sporadic cases. Quantifying exceedances rapidly is particularly challenging when only few cases are typically reported in a precise location and time. To address such important public health concerns, we present a general method which can detect spatio-temporal deviations from a Poisson point process and estimate the odds of an isolate being part of a cluster. This method can be applied to diseases where detailed geographical information is available. In addition, we propose an approach to explicitly take account of delays in microbial typing. As a case study, we considered invasive group A Streptococcus infection events as recorded and typed by Public Health England from 2015 to 2020.
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spelling pubmed-97443222022-12-13 Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease Cavallaro, Massimo Coelho, Juliana Ready, Derren Decraene, Valerie Lamagni, Theresa McCarthy, Noel D. Todkill, Dan Keeling, Matt J. PLoS Comput Biol Research Article The rapid detection of outbreaks is a key step in the effective control and containment of infectious diseases. In particular, the identification of cases which might be epidemiologically linked is crucial in directing outbreak-containment efforts and shaping the intervention of public health authorities. Often this requires the detection of clusters of cases whose numbers exceed those expected by a background of sporadic cases. Quantifying exceedances rapidly is particularly challenging when only few cases are typically reported in a precise location and time. To address such important public health concerns, we present a general method which can detect spatio-temporal deviations from a Poisson point process and estimate the odds of an isolate being part of a cluster. This method can be applied to diseases where detailed geographical information is available. In addition, we propose an approach to explicitly take account of delays in microbial typing. As a case study, we considered invasive group A Streptococcus infection events as recorded and typed by Public Health England from 2015 to 2020. Public Library of Science 2022-11-30 /pmc/articles/PMC9744322/ /pubmed/36449515 http://dx.doi.org/10.1371/journal.pcbi.1010726 Text en © 2022 Cavallaro et al 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
Cavallaro, Massimo
Coelho, Juliana
Ready, Derren
Decraene, Valerie
Lamagni, Theresa
McCarthy, Noel D.
Todkill, Dan
Keeling, Matt J.
Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease
title Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease
title_full Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease
title_fullStr Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease
title_full_unstemmed Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease
title_short Cluster detection with random neighbourhood covering: Application to invasive Group A Streptococcal disease
title_sort cluster detection with random neighbourhood covering: application to invasive group a streptococcal disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9744322/
https://www.ncbi.nlm.nih.gov/pubmed/36449515
http://dx.doi.org/10.1371/journal.pcbi.1010726
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